domain: chim phys chim.theo

text:
Yu-Jie Zhong

Jian-Hong Liao

Tzu-Hao Chiu

Franck Gam

Samia Kahlal

Jean-Yves Saillard

C W Liu

Doping Effect on the Structure and Properties of Eight-Electron Silver Nanoclusters

The bimetallic M20 and M21 compounds, {[Cu3Ag17{S2P(O i Pr)2}12]0.5 [Cu4Ag16{S2P(O i Pr)2}12]0.5} ({[1a]0.5[1b]0.5}) and [Cu4Ag17{S2P(O i Pr)2}12](PF6) (2) have been structurally characterized, in which the Cu(I) ions are randomly distributed on the eight outer positions capping the 8-electron [Ag13] 5+ core. DFT calculations show that his statistical disorder results from the nearly neutral preference of copper to occupy any of the eight outer positions. Surprisingly, the UV-Vis absorption spectra of the M20 and M21 bimetallic nanoclusters display an almost identical absorption profile as that of their homometallic [Ag20{S2P(O i Pr)2}12] and [Ag21{S2P(O i Pr)2}12] + relatives. This is rationalized by TD-DFT calculations which show that the frontier orbitals of such 8-electron alloys are largely independent from the nature of the capping metal ions. A blue-shifted absorption is observed upon replacing by Au the central Ag atom in 2, forming the trimetallic compound [Cu4AuAg16{S2P(O i Pr)2}12](PF6) (3).

I. INTRODUCTION

Ligand-protected noble metal nanoclusters (NCs) are hot targets to seek, due to their captivating structural characteristics and broad applications in a wide spectrum of fields. These include biological labelling, optoelectronics, sensing, and catalysis. [1][2][START_REF] Aikens | Electronic and Geometric Structure, Optical Properties, and Excited State Behavior in Atomically Precise Thiolate-Stabilized Noble Metal Nanoclusters[END_REF][START_REF] Jin | Atomically precise metal nanoclusters: stable sizes and optical properties[END_REF][START_REF] Jin | Toward Active-Site Tailoring in Heterogeneous Catalysis by Atomically Precise Metal Nanoclusters with Crystallographic Structures[END_REF] One of the most topical branches deals with atomically precise superatomic NCs, for which it is possible to investigate accurately the structure-property relationships. [START_REF] Kang | Atomically precise alloy nanoclusters: syntheses, structures, and properties[END_REF][START_REF] Kang | Tailoring the photoluminescence of atomically precise nanoclusters[END_REF] For example, the photoluminescence property of NCs can be tuned by doping the parent homometallic NCs to trigger enhancements of emission intensity. [START_REF] Soldan | Gold Doping of Silver Nanoclusters: A 26-Fold Enhancement in the Luminescence Quantum Yield[END_REF][START_REF] Van Der Linden | Single Au Atom Doping of Silver Nanoclusters[END_REF][START_REF] Silalahi | Synthesis of Two-Electron Bimetallic Cu-Ag and Cu-Au Clusters by using [Cu13(S2CN n Bu2)6(C≡CPh)4] + as a Template[END_REF] From this viewpoint, structural analysis is indispensable and only through X-ray diffraction can we understand the detailed atomic arrangements in the NCs.

Alloying NCs under precise control at atomic level has long been a challenge. Recent studies on atomically precise, superatomic clusters of the type M@Ag19+n (M = Au, Pd, Pt; n = 0, [START_REF] Lin | Synthesis, structural characterization and transformation of an eight-electron superatomic alloy, [Au@Ag19{S2P(OPr)2}12[END_REF] 1, 12-14 5, 15-17 9, [START_REF] Soldan | Gold Doping of Silver Nanoclusters: A 26-Fold Enhancement in the Luminescence Quantum Yield[END_REF] 14, [START_REF] Guan | The stability enhancement factor beyond eight-electron shell closure in thiacalix[4]areneprotected silver clusters[END_REF] 20 [START_REF] Du | Different Types of Ligand Exchange Induced by Au Substitution in a Maintained Nanocluster Template[END_REF] ), where the heteroatom M is lying at the center of an Ag12 icosahedron, to generate a kernel typically surrounded by 7 + n silver atoms, have explicitly demonstrated that electronic properties of 8-electron alloys can be tuned by the heterometal nature. Zhu and co-workers successfully pushed the boundary from mono-metallic to tetra-metallic in the case of M29 NCs. [START_REF] Kang | Rational construction of a library of M29 nanoclusters from monometallic to tetrametallic[END_REF] However, only one Pt/Ag/Cu trimetallic NC, PtAg12Cu16, has been so far structurally characterized and displays a Pt-centered Ag12 cuboctahedron surrounded by 16 Cu atoms. On the other hand, reports of Cu-doped Ag NCs are relatively few, [START_REF] Kang | Rational construction of a library of M29 nanoclusters from monometallic to tetrametallic[END_REF][START_REF] Yan | Asymmetric Synthesis of Chiral Bimetallic [Ag28Cu12(SR)24] 4-Nanoclusters via Ion Pairing[END_REF][START_REF] Kang | Free Valence Electron Centralization Strategy for Preparing Ultrastable Nanoclusters and Their Catalytic Application[END_REF] especially for species in which Cu ions surround a centered icosahedral M@Ag12 core.

Only two examples, Ag17Cu12 and AuAg16Cu12, can be found in the literature. [START_REF] Kang | Free Valence Electron Centralization Strategy for Preparing Ultrastable Nanoclusters and Their Catalytic Application[END_REF] Herein we present the effect of the formal doping with Cu of the related 8-electron homometallic [Ag20{S2P(O i Pr)2}12] [START_REF] Dhayal | Ag20{S2P(OR)2}12]: A Superatom Complex with a Chiral Metallic Core and High Potential for Isomerism[END_REF] and [Ag21{S2P(O i Pr)2}12] + 24 NCs. The later species, both of C3 ideal symmetry, are made of an [Ag@Ag12] 5+ centered icosahedron capped by seven and eight Ag + outer atoms, respectively, and further wrapped up in a passivating shell made of twelve dithiophosphate ([S2P(O i Pr)2] -) ligands (abbreviated as dtp in the followings). In the doped species, the Cu heterometals only replace some of the capping Ag atoms at the surface of the 8-electron superatomic core.

II. EXPERIMENTAL DETAILS

A. MATERIALS

All chemicals used as received were purchased from commercial sources. Solvents were purified following standard protocols. [START_REF] Perrin | Purification of laboratory chemicals. 4 th Edition[END_REF] All reactions were performed in ovendried Schlenk glassware using standard inert atmosphere techniques. All reactions were carried out under N2 atmosphere by using standard Schlenk techniques.

[M(CH3CN)4]PF6 (M = Cu, Ag), [START_REF] Kubas | Tetrakis(Acetonitrile)Copper(I) Hexafluorophosphate[END_REF] [Ag20{S2P(O i Pr)2}12], [START_REF] Dhayal | Ag20{S2P(OR)2}12]: A Superatom Complex with a Chiral Metallic Core and High Potential for Isomerism[END_REF] and NH4[S2P(O i Pr)2] [START_REF] Wystrach | Notes -Basic Zinc Double Salts of O,O-Diakyl Phosphorodithioic Acids[END_REF] were prepared by procedures reported earlier in literature.

B. INSTRUMENTATION

1 H and 31 P{ 1 H} NMR spectra were recorded on a Bruker Avance DPX 300 MHz NMR spectrometer, operating at 300.13 MHz for 1 H and 121.49 MHz for 31 P. The chemical shifts (δ) and coupling constants (J) are reported in ppm and Hz, respectively. X-ray diffraction data were collected on a Bruker APEX II CCD diffractometer. ESI mass spectra were recorded on a Fison Quattro Bio-Q (Fisons Instruments, VG Biotech, U. K.). UV-visible absorption spectra were measured on a Perkin Elmer Lambda 750 spectrophotometer using quartz cells with path length of 1 cm. The elemental analyses (C, H, and S content) of the sample were determined by an Elementar UNICUBE elemental analyzer.

C. COMPUTATIONAL DETAILS

Geometry optimizations were carried out within the formalism of the density functional theory (DFT) with the Gaussian 16 package, 28 using the BP86 functional [START_REF] Becke | Density-functional exchange-energy approximation with correct asymptotic behavior[END_REF][START_REF] Perdew | Density-functional approximation for the correlation energy of the inhomogeneous electron gas[END_REF] and the Def2-TZVP basis set from EMSL Basis Set Exchange Library. [START_REF] Schaefer | Fully optimized contracted Gaussian basis sets for atoms Li to Kr[END_REF][START_REF] Schaefer | Fully optimized contracted Gaussian basis sets of triple zeta valence quality for atoms Li to Kr[END_REF] All the optimized geometries were characterized as true minima by vibrational analysis. The compositions of the molecular orbitals were calculated using the AOMix program. [START_REF] Gorelsky | AOMix program[END_REF] The UV-visible transitions were calculated on the above-mentioned optimized geometries by means of time-dependent DFT (TD-DFT) calculations, with the CAM-B3LYP functional [START_REF] Yanai | A new hybrid exchange-correlation functional using the Coulomb-attenuationg method (CAM-B3LYP)[END_REF] and the Def2-TZVP basis set. The UV-visible spectra were simulated from the computed TD-DFT transitions and their oscillator strengths by using GaussView, 35 each transition being associated with a Gaussian function of halfheight width equal to 1000 cm -1 . [START_REF] Zhong | Hydride-encapsulated bimetallic clusters supported by 1,1-dithiolates[END_REF] The residues retained on the column can be flushed out by using acetone as eluting solvent. The eluent was collected and dried, and then washed by n-hexane (10 mL) and filtered. The filtrate was dried under vacuum to yield [CuxAg20-x{S2P(O i Pr)2}12] (1) as a dark orange-red powder (Yield: 0.105 g, 61.8%, based on Ag). [START_REF] Schaefer | Fully optimized contracted Gaussian basis sets for atoms Li to Kr[END_REF]

D. SYNTHETIC PROCEDURE

[Cu4Ag17{S2P(O i Pr)2}12]PF6 (2)

Method A -A One-Pot Reaction: Compound 2 can be isolated from the synthetic procedure of 1. The reaction product was dissolved in dichloromethane and passed through alumina column by gravity. The residues retained on the column can be flushed out by using acetone as eluting solvent. The eluent was collected and dried, then washed by n-hexane (10 mL)

E. X-ray crystallography

Single crystals suitable for X-ray diffraction analysis of 1 and 2 were obtained by slow evaporation of acetone solution at ambient temperature within a week. Single crystals were mounted on the tip of glass fiber coated in paratone oil and then transferred into the cold N2 gas stream. Data were collected on a Bruker APEX II CCD diffractometer using graphite monochromated Mo Kα radiation (λ = 0.71073 Å) at 150K. Absorption corrections for area detector were performed with SADABS [START_REF] Sadabs | version 2014-11[END_REF] and the integration of raw data frame was performed with SAINT. [START_REF] Saint | V8.30A[END_REF] The structure was solved by direct methods and refined by least-squares against F 2 using the SHELXL-2018/3 package, [START_REF] Sheldrick | A short history of SHELX[END_REF] incorporated in SHELXTL/PC V6. 14. 40 Both Cu and Ag atoms are randomly disordered on capping atom positions and detailed refinements of the occupancy ratios are depicted in FIG. 12.

In the preliminary refinement of 1 ({[1a]0.

III. RESULTS AND DISCUSSIONS

A. Syntheses

In Ag + ] + (x = 0-7). [START_REF] Zhong | Hydride-encapsulated bimetallic clusters supported by 1,1-dithiolates[END_REF][START_REF] Zhong | Intercluster exchanges leading to hydride-centered bimetallic clusters: a multi-NMR, X-ray crystallographic, and DFT study[END_REF] Presumably metal exchanges occur in the gas phase.

D. STRUCTURAL ANALYSIS

Crystals of composition {[1a]0.5[1b]0.5} were obtained from slow evaporation of an acetone solution containing compound 1. The M20 framework consists of a silvercentered icosahedral core (Ag@Ag12) capped by seven outer metal (CuxAg7-x) atoms (FIG. 4a), which is further stabilized by twelve dtp ligands (FIG. 4b). The whole cluster possesses an ideal C3 symmetry with a 3-fold axis passing through the centers of two eclipsed triangular faces of the icosahedron. The only difference between 1a and [Ag20{S2P(O i Pr)2}12] [START_REF] Dhayal | Ag20{S2P(OR)2}12]: A Superatom Complex with a Chiral Metallic Core and High Potential for Isomerism[END_REF] Unfortunately, good-quality single crystals of 3 suitable to collect decent X-ray diffraction data could not be obtained. A preliminary X-ray diffraction study showed that the highest electron density (higher than that for Ag) appeared on the central position (Mcent), which can be reasonably assigned to Au. In our previous researches on isoelectronic AuxAg21-x alloy NCs, we have shown the neat preference for Au to occupy the central position. [START_REF] Lin | Synthesis, structural characterization and transformation of an eight-electron superatomic alloy, [Au@Ag19{S2P(OPr)2}12[END_REF][START_REF] Chang | Eight-Electron Silver and Mixed Gold/Silver Nanoclusters Stabilized by Selenium Donor Ligands[END_REF][START_REF] Bootharaju | Templated Atom-Precise Galvanic Synthesis and Structure Elucidation of a [Ag24Au(SR)18] -Nanocluster[END_REF] Accordingly, the M21 framework in 3 is likely to be an Au-centered Ag12 icosahedron capped by eight metal (CuxAg8-x) atoms (FIG. 4e), the whole framework being further protected by twelve dtp ligands.

E. ELECTRONIC ABSORPTION SPECTROSCOPY

The solution UV-Vis absorption spectra (FIG. 5a) of 1 in both powder and crystal forms and [Ag20{S2P(O i Pr)2}12] are nearly identical. They exhibit two major absorption bands around 355 and 422 nm (TABLE SI). With two major bands at 382 and 422 nm, the absorption spectrum of 2 is also very similar to that of [Ag21{S2P(O i Pr)2}12] + (FIG. 5c andS9). The low-energy feature (a shoulder peak) at 473 and 480 nm in the M20 and M21 nanoclusters, respectively, is of large intraicosahedron MMCT nature due to the fact that the frontier orbitals of the 8-electron alloy are nearly independent from the capping metal ions (see below). [START_REF] Chang | Eight-Electron Silver and Mixed Gold/Silver Nanoclusters Stabilized by Selenium Donor Ligands[END_REF]

F. DFT COMPUTATIONAL ANALYSIS

Compounds 1-3, as well as their homometallic Ag20 and Ag21 analogues were investigated by DFT calculations at the BP86/Def2-TZVP level of calculations.

For the sake of computational limitations, the S2P(O i Pr)2 ligands were replaced by S2PH2 models, a simplification which has been proved to be reasonable in many past investigations. 10-14, 23-24, 36, 41, 43 The computed simplified models are labelled 1'-3' in the followings. In previous calculations [START_REF] Silalahi | Synthesis of Two-Electron Bimetallic Cu-Ag and Cu-Au Clusters by using [Cu13(S2CN n Bu2)6(C≡CPh)4] + as a Template[END_REF][START_REF] Lin | Synthesis, structural characterization and transformation of an eight-electron superatomic alloy, [Au@Ag19{S2P(OPr)2}12[END_REF][START_REF] Chang | Eight-Electron Silver and Mixed Gold/Silver Nanoclusters Stabilized by Selenium Donor Ligands[END_REF][START_REF] Sharma | Structurally Precise Dichalcogenolate-Protected Copper and Silver Superatomic Nanoclusters and Their Alloys[END_REF] and in a comprehensive investigation on site preference to be published, we showed that in this family of M20 and M21 Ag-rich compounds, copper always prefers occupying capping positions, whereas Au occupies preferentially the icosahedron center. Such site preferences are consistent with what was found for other systems. [START_REF] Kang | Rational construction of a library of M29 nanoclusters from monometallic to tetrametallic[END_REF][START_REF] Yan | Asymmetric Synthesis of Chiral Bimetallic [Ag28Cu12(SR)24] 4-Nanoclusters via Ion Pairing[END_REF][START_REF] Kang | Free Valence Electron Centralization Strategy for Preparing Ultrastable Nanoclusters and Their Catalytic Application[END_REF]44 Then, in this work we assume the copper and gold atoms occupying capping and central positions, respectively, in full consistency with the X-ray structures of 1-3 (see above). Considering first 1a', there are 10 ways of distributing the 3 Cu atoms over the 7 capping positions, assuming C3 symmetry for the unsubstituted metal framework.

The metal cores of the corresponding positional isomers are given in FIG. 6, together with some relevant energetics. It makes quite clear that they all are close in energy, lying within a range of ⁓ 4 kcal/mol, both in relative energy (∆E) or free energy (∆G), and half of them lie in a ⁓ 2 kcal/mol range. This result supports the fact that such nearly isoenergetic isomers are difficult to separate, leading to occupational statistical disorder on the capping positions in the X-ray structure.

No structural tendency can be traced to explain these small energy differences, which are likely to result from a compromise between several weak factors. Very similar results are found for the 9 positional isomers of 1b' (FIG. 7), which all lie in a range of ⁓ 3 kcal/mol.

ACCEPTED MANUSCRIPT

SUPPLEMENTARY MATERIAL

See supplementary material for crystallographic data, ESI mass spectra, absorption, and NMR spectra.

1 .

 1 [CuxAg20-x{S2P(O i Pr)2}12] (1), x = 3 (1a), 4 (1b). [Ag(CH3CN)4]PF6 (0.256 g, 0.614 mmol), [Cu(CH3CN)4]PF6 (0.141 g, 0.38 mmol), and NH4[S2P(O i Pr)2] (0.131 g, 0.566 mmol) were added in a 250 mL flask and then THF (50 mL) was added. The solution was cooled to -20℃ and then NaBH4 (0.054 g, 1.415 mmol) was added. It was kept stirring for 4 h and dried under vacuum. The residual was dissolved in dichloromethane and passed through alumina column by gravity. The eluent was collected as [CuxAg7x(H){S2P(O i Pr)2}6].

3 .

 3 [Cu4AuAg16{S2P(O i Pr)2}12]PF6 (3) Compound 2 (0.114 g, 0.024 mmol, based on the molecular formula in crystal 2) was dissolved in THF (20 mL) and cooled to -20℃. Au(PPh3)Cl (0.015 g, 0.030 mmol) was added and kept stirring at same temperature for 1 h. The solution was dried and the residue was washed by n-hexane to yield [Cu4AuAg16{S2P(O i Pr)2}12]PF6 as a dark orange-red powder (Yield: 0.103 g, 88.8%, based on Ag). 31 P{ 1 H} NMR (121.49 MHz, CDCl3, δ, ppm): 93.6 (s, S2P), -143.0 (septet, 1 JPF = 710 Hz, PF6). 1 H NMR (300.13 MHz, CDCl3, δ, ppm): 1.36 (d, 3 JHH = 6 Hz, 144H, CH3), 4.87 (m, 24H, CH). Anal. calcd. for C72H168Ag16AuCu4F6O24P13S24‧2(C6H14): C, 19.96; H, 3.91; S, 15.23. Found: C, 20.12; H, 3.67; S, 15.77. UV-vis [λ in nm (ε in M -1 cm -1 )]: 373 (4100), 413 (4700), 464 (2100).

FIG. 1 .FIG. 2 .

 12 FIG 1 by referring to the optimization result with free variables. The final refinement revealed that a total number of 3.5 Cu atoms were randomly distributed in the seven Mcap positions, which can be attributed to the co-crystallization of [Ag13@Cu3Ag4{S2P(O i Pr)2}12] (1a) and [Ag13@Cu4Ag3{S2P(O i Pr)2}12] (1b) with equal ratio. The final R1 [I>2σ(I)] and wR2 (all data) value was reduced by ~0.08 and ~0.04, respectively, compared to the initial non-disordered model of [Ag20{S2P(O i Pr)2}12]. The same method was applied to the refinement of 2, with [Ag21{S2P(O i Pr)2}12]PF6 as a starting model in the preliminary refinement. Again, the electron holes only appeared around the eight Mcap positions, and the Cu:Ag occupancy ratio at each Mcap position was eventually determined as depicted in FIG 2. Besides, two additional Mcap (cyan color in FIG. 2) positions were identified as a minor contribution (10%) to form pseudo-isomers of M21 skeleton, resulting from different arrangements of the Mcap atoms. It is noted that a similar situation was found in the previously published [Ag21{S2P(O i Pr)2}12]PF6, 24 in which a minor contribution (15%) was also uncovered. In the final refinement, a total of four Cu atoms were found to be randomly disordered at eight Mcap positions. Hence, the molecular formula in 2 is [Ag13@Cu4Ag4{S2P(O i Pr)2}12]PF6. Only the major contribution model (90%) in structure 2 will be used for discussion in the following sections. All non-hydrogen atoms were refined anisotropically. CCDC 2073787 (1) and 2057371 (2) contain the supplementary crystallographic data for compounds 1 and 2 in this article. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc.cam.ac.uk/data_request/cif.

Scheme 1 . 1 (

 11 Scheme 1. (a) A one-pot synthesis of bimetallic NCs (M20 and M21). The synthesis of (b) Cu-doped bimetallic NC (M21), and (c) a trimetallic NC (M21).

FIG. 3 .

 3 FIG. 3. Positive-mode ESI-MS spectrum of 1. The distribution of molecular ion peaks corresponds to [CuxAg20-x{S2P(O i Pr)2}12 + Ag + ] + (x = 1-7).

Fig. 4 .

 4 Fig. 4. (a) The M20 framework of {[1a]0.5[1b]0.5}, and (b) full molecule of {[1a]0.5[1b]0.5} with isopropoxy groups omitted. (c) The M21 framework of 2, and (d) full molecule of 2 with isopropoxy groups omitted. (e) The trimetallic M21 NC core, CuxAuAg20-x.

  Therefore, the substitution by Cu + atoms at Mcap positions does not perturb the characteristic absorption band of 480 nm. Contrarily to that of 2, the absorption bands of 3 at 373, 413, and 464 nm are blue-shifted with respect to that of 2 and [Ag21{S2P(O i Pr)2}12] +(FIG.5b). The larger HOMO-LUMO gap of 3, which arises from the central, more electronegative gold atom to induce larger center-to-surface charge transfer within the icosahedral core, results in the hypsochromic absorption band.[START_REF] Lin | Synthesis, structural characterization and transformation of an eight-electron superatomic alloy, [Au@Ag19{S2P(OPr)2}12[END_REF][START_REF] Chang | Eight-Electron Silver and Mixed Gold/Silver Nanoclusters Stabilized by Selenium Donor Ligands[END_REF][START_REF] Bootharaju | Templated Atom-Precise Galvanic Synthesis and Structure Elucidation of a [Ag24Au(SR)18] -Nanocluster[END_REF]

FIG. 5 .

 5 FIG. 5. (a) The absorption spectra of 1 in powder (red line), crystal (blue line), and [Ag20{S2P(O i Pr)2}12] (black line) in 2-MeTHF. (b) The stacked absorption spectra of [Ag21{S2P(O i Pr)2}12] + (red line), [Cu4Ag17{S2P(O i Pr)2}12] + (green line), and [Cu4AuAg16{S2P(O i Pr)2}12] + (blue line) in 2-MeTHF.

FIG. 6 .

 6 FIG. 6.Metal cores of the 10 computed positional isomers of 1a' with their relative energies (∆E), relative free energies (∆G) and HOMO-LUMO gaps (∆EH-L). Color code: grey = Ag(icosahedral core), green = Ag(capping), red = Cu.

15 FIG. 7 .FIG. 8 .FIG. 9 .FIG. 10 .

 1578910 FIG. 7.Metal cores of the 9 computed positional isomers of 1b' with their relative energies (∆E), relative free energies (∆G) and HOMO-LUMO gaps (∆EH-L). Color code: grey = Ag(icosahedral core), green = Ag(capping), red = Cu.

  Therefore, the cluster nuclearity increases from 20 to 21. Such an addition reaction with cluster nuclearity increment has been previously described by us[START_REF] Chang | Eight-Electron Silver and Mixed Gold/Silver Nanoclusters Stabilized by Selenium Donor Ligands[END_REF] upon the addition of Ag + ions to [Ag20{S2P(O i Pr)2}12],which produces [Ag21{S2P(O i Pr)2}12] + in high yield.[START_REF] Sharma | Structurally Precise Dichalcogenolate-Protected Copper and Silver Superatomic Nanoclusters and Their Alloys[END_REF] A further reaction by doping 2 with single Au + leads to the formation of the trimetallic NC 3 (SCHEME 1c). It is of note that increasing the amount of Cu(I) ions in the reactions leads to the decomposition of the NCs. Thus it seems to be difficult to replace all the capping silver atoms by Cu atoms in the 8-electron Ag20 and Ag21 NCs.

	Ag + + Cu + + (dtp)	-+ BH 4	-	[Cu x Ag 20-x(dtp)12 ] (x = 3, 1a ; 4, 1b ) + [Cu x Ag 21-x(dtp)12 ] + (x = 4, 2 ) +
	[Ag 20(dtp)12 ] + Cu +				[Cu x Ag 21-x(dtp)12 ] + (x = 4, 2 ) + xAg +
	[Cu 4 Ag 17(dtp)12 ] + ( 2 )	+ Au +	[Cu 4 AuAg 16(dtp)12 ]

this work, two bimetallic Cu-Ag NCs, [CuxAg20-x{S2P(O i Pr)2}12] (1) [x = 3 (1a), 4 (1b)], and [Cu4Ag17{S2P(O i Pr)2}12] + (2), as well as a trimetallic NC, [Cu4AuAg16{S2P(O i Pr)2}12] + (3) have been successfully prepared. 1 and 2 were generated from the one-pot reaction of Cu(I), Ag(I), [S2P(O i Pr)2] -, and NaBH4 with molar ratios of Cu + :Ag + :L -:H -= 8:13:12:30 (SCHEME 1a; Method A, see experimental section). 1 and 2 can be easily separated by solvents in which the neutral species 1 is highly soluble in n-hexane but the cationic cluster 2 is not. An alternative way in the preparation of compound 2 is doping [Ag20{S2P(O i Pr)2}12] with Cu(I) (SCHEME 1b;

Method B, see experimental section). Only M21 NCs can be isolated in this reaction, which indicates that both metal exchange and metal addition have occurred on the surface of the [Ag13] 5+ kernel. In the presence of an excess of metal ions, the metal tends to fill up the cavities on the surface. + ( 3 ) + Ag +

  is that four outer capping Ag atoms in the later are replaced by Cu atoms in the former. Indeed, compared with the homometallic [Ag20{S2P(O i Pr)2}12] relative, the electron densities on the seven capping positions are all significantly reduced, contrarily to that of the M13 centered icosahedral core that remain all the same and are consistent with full Ag occupation. This result indicates that the four Cu atoms are randomly disordered on the seven outer positions. A careful Pr)2}12] + .[START_REF] Dhayal | Ag21{S2P(O i Pr)2}12] + : An Eight-Electron Superatom[END_REF] Again, the Cu atoms only occupy the capping positions and are randomly disordered. The final refinement showed a total occupancy of 4 for the Cu atoms on the eight capping positions (FIG.

	refinement led to a total occupancy of 3.5 for Cu, corresponding to a co-crystallization
	of 50% [Cu3Ag17{S2P(O i Pr)2}12] (1a) and 50% [Cu4Ag16{S2P(O 2). Therefore, the molecular formula is [Cu4Ag17{S2P(O i Pr)2}12]PF6. The analysis of
	the metal distances in 2 shows similar trends as in {[1a]0.5[1b]0.5}. The Agcent-Agico
	and Agico-Agico distances in 2 are in the range of 2.7557(9) -2.8020(8) Å (avg.
	2.7719(9) Å) and 2.8455(10) -2.9839(10) Å (avg. 2.9140(9) Å), respectively, which
	are similar to [Ag21{S2P(O i Pr)2}12]PF6 (2.7542(8) -2.8159(8) Å, avg. 2.7801(8) Å and

i Pr)2}12] (1b). Hence, the molecular formula in 1 was determined as {[1a]0.5[1b]0.5}. The Agcent-Agico and Agico-Agico distances in 1 are in the range of 2.7360(11) -2.8197(11) Å (avg. 2.7746(11) Å) and 2.8478(12) -3.0122(12) Å (avg. 2.9177(11) Å), respectively, which are similar to that in [Ag20{S2P(O i Pr)2}12] (2.7299(5) -2.8244(5) Å, avg. 2.7785(5) Å and 2.8606(5) -3.0179(5) Å, avg. 2.9216(5) Å). The average Mcap-Agico distance in 1 (2.9542(13) Å) is significantly shorter than in the Ag20 relative (2.9769(5) Å) by ~0.02 Å. It is attributed to the shorter atomic radii of Cu located on the Mcap position. Crystals of 2 were grown from the slow evaporation of acetone solution.

Comparing with the M20 core revealed in {[1a]0.5[1b]0.5}, the M21 framework is a simple addition of one metal atom (on the C3 axis) to the capping position of 1a. With a total of eight outer metal atoms (CuxAg8-x) capping the Ag13 centered icosahedron, the resulting M21 core has an ideal D3 symmetry (FIG.

4c

). It is further protected by twelve dtp ligands, which lower the symmetry to C3 (FIG.

4d

), leading to a structure similar to that of the homometallic [Ag21{S2P(O i 2.8420(9) -3.0255(9) Å, avg. 2.9385(9) Å). A significantly shorter average Mcap-Agico distance in 2 than in its Ag21 relative (2.950(1) Å vs. 2.9811(10) Å) was also observed.

F. Alkan, P. Pandeya, C. M. Aikens, "Understanding the effect of doping on energetics and electronic structure for Au25, Ag25 and Au38 clusters," J. Phys. Chem. C, 123, 9516-9527 (2019).

ACKNOWLEDGEMENTS

This work was supported by the Ministry of Science and Technology of Taiwan (MOST 109-2113-M-259-008, 108-2923-M-259-001), the France-Taiwan ANR-MOST program (project Nanoalloys) and the GENCI French national computer resource center (grant A0030807367).

DATA AVAILABILITY

The data that support the findings of this study are available within the article (and its supplementary material).


Efstathia Gkioni
email: e.gkioni@liverpool.ac.uk

Susanna Dodd

Roser Rius c Carrol Gamble

Statistical models to predict recruitment in clinical trials were rarely used by statisticians in UK and European networks

Keywords: Recruitment, Prediction, Monitoring, Surveys, Clinical trials

Objective: Identify the current practice for recruitment prediction and monitoring within clinical trials. Study Design and Setting: Chief investigators (CIs) were surveyed to identify data sources and adjustments made to support recruitment prediction. Statisticians were surveyed to determine methods and adjustments used when predicting and monitoring recruitment.

Introduction

Clinical trials are a major financial investment with the time to recruit to the required sample size being a key driver of associated costs. Failure to successfully recruit clinical trial participants as predicted at the design stage has many negative consequences. These range from incurring increased costs and time to answer the clinical question of interest to abandoning the research with the question remaining unanswered. There may also be negative impact on the planning and roll out of future research.

Despite investment aimed at reducing difficulties in recruitment to clinical trials [START_REF] Treweek | Methods to improve recruitment to randomised controlled trials: cochrane systematic review and meta-analysis[END_REF][START_REF] Ross | Barriers to participation in randomised controlled trials: a systematic review[END_REF], there has been no demonstrable improvement and 45% of trials supported by two prestigious UK funding bodies, Health Technology Assessment and Medical Research Council (HTA & MRC), continue to fail to meet their original recruitment targets [START_REF] Sully | A reinvestigation of recruitment to randomised, controlled, multicenter trials: a review of trials funded by two UK funding agencies[END_REF].

Authors' contributions: All authors have read and approved the final version of this manuscript. EG was one of the lead researchers on this project and was responsible for the protocol preparation, creation of surveys, data collection and analysis, and writing of this manuscript. SD is coinvestigator on this project and contributed to its design, creation of surveys, analysis, writing, and proofreading of this manuscript. RR is also coinvestigator and contributed to the creation of surveys and analysis of the results. CG is the principal investigator and is responsible for the project conception, design, creation of surveys, analysis, writing, and proofreading of this manuscript.

What is new?

Key findings

Statistical models to predict recruitment are rarely used. Uptake is limited by the absence of evidence regarding their benefit over more simplistic approaches. Predictions allowed for variation in site recruitment rates and staggered site openings, whereas seasonal variation and holiday periods were less frequently considered.

Respondents in European network had greater awareness of statistical methods to predict recruitment in comparison with the UK respondents; however, numbers are small, and the European sample is subject to potential response bias.

Approximately, one-third of UK and half of the European Clinical Research Infrastructure Network (ECRIN) respondents in the Chief Investigators' survey reported a need to be optimistic about the predicted recruitment rate, to be attractive to the funder.

What this adds to what is known?

Prediction of recruitment in clinical trials continues to represent a formidable challenge. This survey identifies data sources and factors used to adjust recruitment rates. It identifies that simplistic approaches to predict recruitment are favored over more complex statistical models. Barriers to uptake of the statistical methods include complexity of their implementation and absence of evidence that the time taken to implement them will result in improving the accuracy of recruitment prediction.

What is the implication and what should change now?

Evidence demonstrating superiority of statistical methods over simplistic methods needs to be established on a prospective cohort of trials. Subsequently, relevant software and training courses should be made available. Multiple data sources are being used, and further work on the quality of this data is needed. Balance of expectations between funders and applicants needs further exploration.

A prioritization exercise was undertaken to identify uncertainties related to trial recruitment as a focus for future methodological research [START_REF] Healy | Identifying trial recruitment uncertainties using a James lind alliance priority setting partnershipethe PRioRiTy (prioritising recruitment in randomised trials) study[END_REF]. Recruitment prediction was identified as a top 10 priority area. Despite the fact that every clinical trial will require such predictions to be made, little is known about how this is achieved either in terms of data sources or methods used. This is unlikely to change given that reporting requirements for recruitment within a main clinical trial article are minimal [START_REF] Schulz | CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials[END_REF] and grant applications are generally not publicly available.

To determine current practice within the United Kingdom and Europe, we undertook a survey of Chief Investigators (CIs) and a survey of statisticians across a UK and a European network.

Methods

Design

EG led the design of each survey with input from all coauthors. Questions targeted data sources and methods used for recruitment prediction, identifying team members contributing to the process, and awareness and implementation of the statistical models (statisticians survey only). Multiple choice answers were informed by relevant publications about statistical models [START_REF] Barnard | A systematic review of models to predict recruitment to multicentre clinical trials. A systematic review of models to predict recruitment to multicentre clinical trials[END_REF][START_REF] Gkioni | A systematic review describes models for recruitment prediction at the design stage of a clinical trial[END_REF] and other approaches that could be used for recruitment prediction and monitoring, as well as by a number of factors that impact recruitment rate that should be considered [8e11].

Chief investigators' survey

The CIs' survey targeted those collaborating within UK and European research infrastructures. The survey aimed to be brief to maximize return rates collecting information not available from publicly available sources covering data sources used to predict recruitment and how these were applied to trial and site requirements. The survey was reviewed within the study team before circulation across the UK and the European Clinical Research Infrastructure Network (ECRIN, https://www.ecrin.org/).

A prize equivalent to £75 in vouchers was offered as an incentive to participation. The full list of survey questions and the invitation email are provided in the Additional file 1.

2.1.1.1. UK chief investigators. UK CIs of recently funded clinical trials were surveyed as identified from the National Institute for Health Research (NIHR) journals library (https://www.journalslibrary.nihr.ac.uk/programmes/ hta/, searched in May and October 2018). The NIHR is the largest funder of health and care research within the United Kingdom. To be eligible for inclusion projects were required to be randomized with the trial status listed as ''waiting to start''. CI details were obtained from the projects' website and each contacted by EG via email, containing an invitation to participate and the survey attached as a Word document. CIs were also given the option of delegating completion to a trial team member. If no response was obtained within 2 weeks then a reminder email was sent and a further reminder followed 3 weeks later, within which we gave them the option to answer the survey via a phone call. A final attempt was made to contact nonrespondents by phone to ensure the correct contact details. based within each member or observer country, distributed the survey within their respective countries. The survey context and purpose was explained to the EuCos via a Web-based meeting before contacting CIs of ECRINsupported studies via email. The email contained an invitation to participate and the survey as an attached Word document. Two reminders were sent to the EuCos requesting recirculation of the survey.

Statisticians' survey

We surveyed statisticians within UKCRC-registered CTUs and ECRIN. The aim of the survey was to establish current practice, and knowledge and implementation of available statistical models. The survey was reviewed within the study team and piloted with a senior statistician before circulation across the UKCRC-registered CTU Statistics Group and ECRIN. A prize equivalent to £75 in vouchers was offered as an incentive to participation.

The full list of questions for the online survey and the invitation email are provided in the Additional file 1.

UK CTU Statisticians.

The UKCRC-registered CTU secretariat distributed the survey via email to the nominated senior statistician within each registered CTU. The email contained an invitation to participate and a link to an online survey, which was constructed using SelectSurvey.NET (http://selectsurvey.net/). The statistician could discuss responses within the wider statistical team of their CTU, but only a single response per CTU was required. Email reminders were sent after 1, 2, and 4 weeks. Nonrespondents also had the opportunity to respond by completion of a paper copy of the survey distributed during the UKCRC-registered CTU Statistics Operational Group Network statisticians meeting in October 2018.

ECRIN Statisticians.

The EuCos at ECRIN circulated the email invitation with the link to the online survey to the statisticians identified within their national network. The same procedure was followed as for the CIs' survey with the EuCos sending two reminders.

Analysis

Quantitative data from closed-ended questions were analyzed using RStudio, version 3.5.0 [START_REF] Team | RStudio: integrated development environment for R[END_REF]. Due to the restricted sample sizes, statistical testing was not planned and results are reported as frequencies and percentages. EG, SD, and CG reviewed responses to open-ended questions identifying themes within the free text answers and categorized them in groups.

Results

Chief investigators' survey

The CIs' survey was conducted between 24 October 2018 and 30 November 2018 within the United Kingdom and between 18 October 2018 and 8 March 2019 within ECRIN with results summarized in Table 1. A total of 32 studies were identified as eligible for inclusion in the UK cohort, and 17 responses were received (53%) from the CIs contacted. Two CIs completed the survey twice each allowing for the multiple trials which they led as the CI. Nineteen studies were identified via the ECRIN EuCos with six responses (32%) received.

The data source most commonly used to predict trial recruitment was audit data from across multiple sites with the impact of specific eligibility criteria being the most frequently adjusted factor (Table 1, Question 1). Although no respondents reported adjusting for ethnic minorities, one respondent elaborated that not adjusting for this factor negatively impacted their predictions.

Allowing for variation in recruitment rates at individual sites was also common (13/17 UK, 6/6 ECRIN) with comments supporting the need for this practice based on variation in patient numbers and knowledge of site research activity infrastructure and experience. The majority of UK respondents (15/17, 88%) did not assume that all sites would be open for the same length of time in comparison with only one of the six ECRIN respondents (17%). Free text responses reported staggered opening times to reflect variation in time required at each site to obtain approvals.

Eleven (65%) UK respondents searched a trial registry for competing trials compared with 100% of ECRIN respondents (Table 1, Question 6). Thirty-five percent of UK and fifty percent of ECRIN respondents were aware of other trials competing to recruit the same patient population. Co-enrolment was considered for only half of UK and one-third of ECRIN respondents. One-third of UK and half of ECRIN respondents reported a need to be optimistic about the predicted recruitment rate for the trial to be attractive to the funder. Free text comments highlighted the difficulties this practice would lead to during trial conduct. Additional comments stated that the estimates were reflective of recruitments rates if things went well, accepting that this may not be the case with an inability to accurately predict researcher performance and stability of local clinical services.

Statisticians' survey

The survey was conducted between 18 September 2018 and 6 November 2018 among the 51 UKCRC-registered CTUs of whom 50 (98%) responded (46 responses completed online, four responses completed at the network meeting). The ECRIN EuCos circulated the survey between 13 November 2018 and 29 January 2019 to 53 participants of whom 19 (36%) responded.

Table 2 summarizes the survey results. The majority believe that statisticians should be involved in predicting recruitment (86% UK, 68% ECRIN); however, statisticians were reported to have been involved in leading the process in only just over half of the studies. Respondents from ECRIN reported were less confident than UK respondents in their awareness of other trials competing to recruit from the same patient population (12% UK, 63% ECRIN) and were less likely to adjust for staggered site openings (96% UK, 68% ECRIN).

Use of statistical models to predict recruitment was low overall (10%) but higher within ECRIN respondents (6% UK, 21% ECRIN) who also had greater awareness of the individual statistical approaches with 48% of UK respondents not aware of any method compared with 21% within ECRIN.

At the pre-trial planning, only 32% of respondents sometimes simulated data to support recruitment prediction, whereas 67% of them never did (Table 2, Question 8). The time investigators would need to dedicate to perform simulations is an additional challenge, especially if they are not convinced of their value.

The majority of respondents who sometimes simulated data are at least aware of the Poisson model (73%, 16/ 22), whereas six of them are not aware of any statistical model for recruitment prediction (27%, 6/22). On the other hand, almost half of respondents who never simulated data, are not aware of any model (48%, 22/46), whereas 43% of them are at least aware of the Poisson model (20/46). However, because of the small sample size, we cannot conclude a definitive correlation between knowledge of statistical models and use of recruitment simulations.

A sizable proportion of respondents (44% UK, 42% ECRIN) preferred to use a simple approach rather than statistical distributions to predict recruitment. Slightly over half of UK respondents were unconvinced of the value of implementing these methods in comparison to only 11% of ECRIN respondents (Table 2, Question 9).

A minority of respondents were aware of existing software or Web platforms to support planning and monitoring of patient recruitment with over a quarter of respondents being interested in such a resource for predicting recruitment and over half interested for both prediction and monitoring (Table 2, Question 12). Free text responses indicated that time to learn how to use such an application and funder willingness to support any associated costs were a concern. Other participants expressed an interest in comparing any methods alongside those already used in practice to determine whether any time or resource investment was worthwhile. One participant expressed concerns on whether the requirements of more complex clinical trials could be met by such a resource.

Response to the free-text question about further practices, tools or resources that could potentially improve prediction accuracy are provided in Box 1, with suggestions for how funders/trial teams could monitor recruitment progress/milestones summarized in Box 2.

Box 1 Further practices or tools to improve recruitment prediction

Question 13: Please give details of any further practices or tools/resources that you think could influence your future practice, in terms of prediction accuracy in patient recruitment.

Training (e.g., work with CIs to show the value of involving statistics for recruitment purposes before and during the trial/workshops for trial statisticians/challenge the clinicians, etc.) (8/69, 12%)

Better engagement (e.g., with potential sites, using standard questionnaires to elicit proposed recruitment target/easily accessible & timely input from clinical communities & access to relevant patient groups, etc.) (5/69, 7%) Raising awareness of the available approaches (2/69, 3%) Current challenges and conflict with NIHR CRN targets (e.g., sites come back and ask to change their local recruitment target to ensure they are not challenged/providing the funding for realistic timelines/building in flexibility with timelines for project management and funding/allowing for reallocation of research resources to new studies toward the end of the study, etc.) (3/69, 4%) Recommendations to improve prediction (e.g., building up a database of actual recruitment in our studies that could be referred to in future/use anonymized registry of patients with relevant disease along with demographic information/valid international data on disease incidence and prevalence/comparing predictions and targets to what actually happened so future predictions can be improved/comparing recruitment in pilot studies with that in full trials/getting funders to request more rigorous methods to estimate recruitment, etc.) (10/69, 14%)

Tool/model (a tool that automatically integrates recruitment predictions for individual sites in a multicentre trial into an overall prediction for the trial/simple, robust methods yielding accurate results/a smoothed time-autocorrelated prediction might be helpful/any tool that helps to maintain engagement/enthusiasm) (8/69, 12%) Demonstrating evidence that these models actually work in practice (e.g., it is important to show that prediction ability of a model/tool is better than the simpler ways/it can be cumbersome to gather all information to feed into prediction tools and it would require further input from a statistician, etc.) (2/69, 3%)

No response (35/69, 51%)

Response not clear (3/69, 4%)

Discussion

This survey is the first to identify current practice on methods to predict recruitment in clinical trials and raises hypotheses about different practices in the United Kingdom compared with Europe and the perceived value of more complex statistical approaches.

Survey responses clearly indicate that the statistical models available are not being implemented. The absence of a robust demonstration of their benefits in comparison with simple approaches is a key barrier to their uptake. The statistical literature is restricted to the evaluation of these models in simulations or in retrospective trials [START_REF] Gkioni | A systematic review describes models for recruitment prediction at the design stage of a clinical trial[END_REF][START_REF] Baldi | Accrual monitoring in cardiovascular trials[END_REF][START_REF] Lan | Statistical modeling and prediction of clinical trial recruitment[END_REF]. It lacks a real-time prospective evaluation using the same limited information sources to support parameter estimation across models at the design stage, which are then used to monitor actual accrual. Furthermore, the survey suggested that this evidence is required before trial statisticians being able to justify the time required to understand and implement the methodology, suggesting that software availability on its own is insufficient to change practices.

There are many factors to be taken into account when predicting recruitment and in turn defining a trial's duration. Recruitment targets cannot be realized if based on overoptimistic expectations and unrealistic timelines. The overoptimistic expectations of the research team have been reported previously [START_REF] Briel | A systematic review of discontinued trials suggested that most reasons for recruitment failure were preventable[END_REF]; however, this survey highlights the tension felt by a sizable proportion of investigators to be optimistic about their recruitment rates, for the trial to be attractive to funders. Despite this tension, there was a clear appreciation of the difficulties this would cause at later time points, with calls for funders' expectations of trial set up times and recruitment rates to be more realistic, less rigid and to allow for unforeseen delays outside the control of the trial management team.

The importance of adhering to the site initiation schedule is key and our anecdotal experience is that deviations from this often explain a substantial proportion of under recruitment. The time required to complete the administrative arrangements which need to be made to open participating sites can vary and be impacted by site engagement and capacity or by regulatory changes [16e20]. The survey indicated that staggered site openings are more commonly allowed for within the United Kingdom than across the ECRIN network. However, although the rate of site initiations may be informed by past experience, there is an inherent assumption regarding the stability of site resources to deliver the research, remaining stable throughout the trial. Although a potential solution is to improve site feasibility and capability assessments, the variables that need to be included and how they are used within resulting predictions, needs further scrutiny.

Box 2 Suggestions for monitoring recruitment progress

Question 14: Do you have any comments or suggestions on how funders/trial teams monitor recruitment progress/milestones? Educate funders (e.g., Funders' expectations of trials set up and recruitment rate should be more realistic/they should be less rigid in setting targets and monitoring against those targets/funders want more for less/funding panels to define the feasibility of recruitment rates and convince the administrative funders that studies need more time to be successfully delivered, etc.) (3/69, 4%) Educate Chief Investigators (CIs tend to be overoptimistic/Methodologists/statisticians usually try to be conservative but this is challenged by CIs, etc.) (2/69, 3%) Build on skills and experience gained from previous trials (e.g., pass on the skills and experience of trials teams that manage to recruit to time and budget/ a sort of rule of thumb is that 20-40% of potentially eligible people approached to take part will consent to inclusion/in cancer trials we tend to expect about 50% of eligible patients to consent etc.) (2/69, 3%) Take into account factors related to trial/outcomes/intervention/condition being studied (e.g., seasonal factors/clinic frequency/TTE 1 considerations/staggered entry assumptions etc.) (4/69, 6%) Allow for delays outside of the control of the trial management team during the course of the trial (e.g., due to budgeting/staffing/resources/new trials opening/delays in agreeing contracts/whole centers dropping out etc.) (2/69, 3%) Central database with recruitment information from previous trials to accompany the online tool (1/69, 1%) Uncertainty to be considered (e.g., any recruitment estimates at the onset of a trial will be based on assumptions, e.g., average recruitment rate per site or something similar/there are so many variables involved and I am not sure there are any decent ways of getting around that/initial recruitment predictions tend to be very inaccurate/any prediction or monitoring of recruitment using sophisticated modeling may not be any better than using simple projections, etc.) (5/69, 7%) Generic programming (programming something generic is important, because statisticians are already under a lot of time pressure and deadlines, so they want to avoid an overload of duties) (1/69, 1%)

No response (52/69, 75%)

Response not clear (3/69, 4%) 1 TTE: time to event.

The survey demonstrates that investigators and statisticians are using a wide variety of information sources to predict their recruitment rates. However, in practice the extrapolation of these data to a specific multicenter trial often requires adjustments to be used. The size of the adjustments may be considered to be arbitrary or based on guess work, and this may in part reduce the number of factors investigators feel able to include. In addition, as the availability of routinely collected data increases to support clinical trial planning, the assessment of such sources and how they are used will be of increasing importance.

The main focus of our survey was the prediction of recruitment; however, we also aimed to ascertain how this was monitored against observed accrual. The responses demonstrated that this information is considered in multiple ways per trial with comparisons of observed recruitment rates against those predicted in graphical or tabular form, at individual site level and across all sites, and covering the entire recruitment period or restricting to recent months. Although eight respondents indicated ''other'' methods were used, the free text provided, demonstrated that the approach was consistent with the closed response categories. The limitation with these approaches is that they do not allow understanding of whether the observed variation is within reasonable limits of the prediction. This may lead to delays in remedial actions. A potential benefit of using a statistical model is the prespecification of a quantile to act as a trigger when the observed recruitment rate is inconsistent with that prespecified. One respondent commented that the use of statistical models would ''simply give a distribution of recruitment rates from which we would need to pick a final number which would be the mean, so simple multiplication would seem as appropriate given the uncertainty about the assumptions.'' This suggests that even if the uncertainty they elude to is not welcome within prediction, there is potential for their use within monitoring.

In a survey of the UKCRC-registered CTUs, the top inefficiency from recruitment of the first participant to the publication of results, was identified as the failure to meet recruitment targets due to overoptimistic or inaccurate recruitment estimates [START_REF] Duley | What are the main inefficiencies in trial conduct: a survey of UKCRC registered clinical trials units in the UK[END_REF]. Some statisticians reported being under pressure to project optimistic recruitment rates. This is likely due to the perception that realistic rates are associated with increased budgets, beyond what funders are willing to provide. This issue was raised by our survey respondents, with participants' suggestions that training should be provided for both CIs and funders. Increasing funders' flexibility in setting timelines would be helpful and reflects additional calls on requirements with adaptive designs [START_REF] Kairalla | Adaptive trial designs: a review of barriers and opportunities[END_REF].

The majority of our respondents believe that a statistician should be involved in the recruitment prediction process but do not model recruitment as a stochastic process. This may be in part explained by time pressures, as recruitment prediction is undertaken during the unfunded preparation time of a grant application. This will be compounded given investigators and statisticians are unconvinced that the models described in the literature are worthy of the additional time required to support their use. However, the survey demonstrates the majority would be interested if the benefits were found to justify the additional time and statistical expertise required.

Limitations of the studies

This survey aimed to elicit current recruitment practice across the United Kingdom and Europe. The high-response rates from the United Kingdom are a strength of the survey; however, this means that the findings predominantly represent current practice within the United Kingdom.

The network structure within the United Kingdom facilitated survey distribution in a controlled approach using the network secretariat ensuring a targeted delivery and response, whereas the ECRIN approach used a more fluid hub and spoke model where the CIs and the statisticians were contacted by the EuCos in each country. Other surveys targeting statisticians across the UKCRC CTU network have achieved similarly high-response rates [23e25], and we have been unable to identify similar surveys across ECRIN. The lower response rates from ECRIN may be a result of these different network infrastructures; however, they may also impacted by the survey being restricted to the English language.

The comparison of practices between the United Kingdom and Europe therefore needs to be interpreted with caution as this could reflect response bias within ECRIN with those with particular interests in recruitment prediction taking part. This may be an explanation for the greater awareness of the statistical methods in the ECRIN respondents.

Furthermore, the investigators in the UK survey were identified from the website of the largest public funder of clinical trials in the United Kingdom. The sampling framework for investigators across ECRIN was by identification of the EuCos and therefore not restricted to a funding source. However, knowledge of both networks suggests that the portfolio of studies represented is restricted to noncommercial research. It would be of interest to understand differences in methods used in comparison with industry sponsored studies, with research suggesting recruitment for industry-sponsored studies being less problematic [START_REF] Kasenda | Prevalence, characteristics, and publication of discontinued randomized trials[END_REF]. Yet, it is likely that the resources allocated and incentives provided are dissimilar and this complexity of factors warrants further detailed exploration in future research.

Conclusions

Approaches used to predict and monitor recruitment remain frequently unreported, and this survey provides insight from both statisticians and investigators on methods and data sources used. This study indicates that the majority of respondents did not recognize recruitment as a stochastic process in the approaches used and stated a preference for using simple approaches. However, they consider the involvement of statisticians in the recruitment prediction process to be essential. Simple approaches will continue to be used despite the advancement of more complex statistical models until their value in improving prediction can be more robustly demonstrated. Until then, their complexity, time, and training required to aid their implementation will remain a barrier despite the potential for their added benefits in monitoring of recruitment.

CRediT authorship contribution statement

Efstathia Gkioni :

 Gkioni Conceptualization, Methodology, Formal analysis, Data curation, Writing -original draft, Writing -review & editing. Susanna Dodd: Formal analysis, Data curation, Writing -review & editing. Roser Rius: Formal analysis, Data curation, Writing -review & editing. Carrol Gamble: Conceptualization, Methodology, Formal analysis, Data curation, Writing -original draft, Writing -review & editing.

Table 1 .

 1 Survey results of the Chief Investigators' survey (UK & ECRIN) 50% of what the Principal Investigator estimates; eligible Vs consent rate, e.g., we expect the recruitment to be something between 30 and 50% of the eligible population depending on the trial question; impact of recruiters.

	Question

a Local patient survey; data compiled by a specific NIHR biomedical research unit; national data on disease activity; multiple sources. b Difficulties in recruiting; logistics of recruitment; availability of research nurses; data on rates of recruitment from previous studies; a general rule:

Table 2 .

 2 Survey results to closed-ended questions of the Statisticians' Survey (UK and ECRIN)

	Question

7) Are you aware of any of the statistical approaches listed below for use in recruitment prediction? Please select all that apply.

Table 2 .

 2 Continued

	Question

a Free-text responses have been categorized into common themes. b Patient and Public Involvement.

E. Gkioni et al. / Journal of Clinical Epidemiology 124 (2020) 58e68

Acknowledgments

We would like to express our thanks to the following people who contributed to the completion of this work:

All the participants who took the time to complete both surveys. The gatekeeper who circulated the statisticians' survey within the UK CRC network (UKCRC secretariat). The European Correspondents from ECRIN who circulated the statisticians' survey and chief investigators' surveys. The Operations Director of ECRIN Dr Christine Kubiak for facilitating the communication with the European Correspondents within the ECRIN. Dr Ashley Jones for his feedback in piloting the survey questions and answers. Mrs. Elizabeth J. Conroy for her help in reviewing this manuscript before submission.

Participants were identified from the National Institute for Health Research recently funded

Funding: This project is part of the MiRoR (Methods in Research on Research) Joint Doctoral Training Programme. The European Union's Horizon 2020 Marie Sklodowska Curie Innovative Training NetworkseEuropean Joint Doctorate (ITN-EJD) under grant agreement No. 676207, fund MiRoR.

Availability of data and materials: Not applicable. Authors' information: Not applicable.

Ethics approval and consent to participate: Ethical approval was granted from Health and Life Sciences Committee on Research Ethics (Human participants, tissues and databases) at The University of Liverpool on 5 September 2018 (reference 2282). All survey participants were provided with full written information before survey commencement with the act of completing the survey, a demonstration of consent.

Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests.

Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclinepi.2020.03.012.


Melissa K Sharp
email: msharp@unist.hr

Ketevan Glonti

Darko Hren

Online survey about the STROBE statement highlighted diverging views about its content, purpose, and value

Keywords: Observational studies, Epidemiologic research design, Guidelines as topic, Information dissemination/methods, STROBE Reporting guidelines

Background and objective: The endorsement rates of The STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) Statement are low and little is known about authors' opinions about this reporting guideline. We conducted an online survey with observational study authors on attitude toward and experiences with the STROBE Statement with the aim of understanding how to effectively implement STROBE.

Methods: A thematic analysis on the responses to an open-ended question was conducted using inductive coding. Two coders classified responses independently into themes using a codebook. The inter-rater agreement ranged from 87.7 to 99.9%.

Results: 15% (n 5 150) of survey participants (n 5 1,015) shared perceptions and insights on STROBE. We established four themes: 1) perceptions of the checklist, 2) academic confidence, 3) use in education and training, and 4) journal endorsement and use in peer review. Views were diverse and revealed multiple misunderstandings about the checklist's purpose and content, and lack of incentives for its use.

Conclusions: Better communication efforts are needed when disseminating STROBE and other reporting guidelines. These should focus on content, education for early career researchers, and encouragement of critical self-reflection on one's own work. In addition, results emphasized the need for better incentive and enforcement mechanisms.

Background

Reporting guidelines (RGs) were created to help reduce research waste and promote reproducibility by providing a minimum set of items to be reported when describing the results of a study. Incomplete reporting contributes to a ''reproducibility crisis'' where scientific progress is impeded because of an inability to replicate results and to accurately interpret findings [START_REF] Begley | Reproducibility in science[END_REF][START_REF] Glasziou | Reducing waste from incomplete or unusable reports of biomedical research[END_REF]. Furthermore, reporting clear and complete information is an ethical responsibility as it informs clinical practice [START_REF] Altman | Chapter 4. Using reporting guidelines effectively to ensure good reporting of health research[END_REF]. In addition, incomplete reporting causes studies to be excluded from systematic reviews and meta-analyses, resulting in research waste. With the rise in systematic reviews [START_REF] Fontelo | A review of recent publication trends from top publishing countries[END_REF], more attention has been given to the necessity of complete reporting and therefore reporting guidelines [5e7].

The RG movement began in the mid-1990s and first focused on randomized control trials and systematic reviews, resulting in the Consolidated Standards of Reporting Trials (CONSORT) [START_REF]Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials[END_REF] and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [START_REF] Moher | Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement[END_REF] Statements. The focus then turned to observational studies which represent most of health research and are often ''the most necessary and difficult'' studies to conduct in epidemiology [10e14]. Observational studies can provide a large number of participants at an affordable cost, allowing for subgroup comparisons and longer follow-up periods to Ethics approval and consent to participate: Ethical approval was granted by the University of Split (2181-198-03-04-18-0010).

Availability of data and material: The deidentified standalone qualitative responses will be made available on the Open Science Framework [START_REF] Sharp | Use of reporting guidelines as an educational intervention for teaching research methods and writing[END_REF].

Conflict of interest: M.K.S. works with the STROBE Statement as a part of her doctoral studies. D.H. provides support and mentoring as a part of the Methods in Research on Research (MiRoR) project. K.G. is a doctoral student on the MiRoR project.

Funding: This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 676207.

Many participants noted that they use STROBE as a teaching tool for early career researchers and find its structure and content useful. However, for midto late-career researchers, there was an overwhelming response of self-assuredness that STROBE was not as useful given their level of expertise.

What this adds to what was known?

This is the first survey done evaluating authors attitudes towards the STROBE Statement.

Respondents reported mixed feelings about STROBE and expressed concerns about the perceived benefits of using it given additional time requirements of use.

Authors also thought that there is a need for better incentive and enforcement mechanisms from journals. When journals request completed checklists, it should be ensured that it is used during editorial or peer review.

What is the implication and what should change now?

We need to better communicate flexibility to authors who are investing extra time, often perceived to be at the sacrifice of one's ego, to complete reporting checklists.

determine long-term risks and benefits. However, they are prone to biases and confounding, making careful design and analysis invaluable [START_REF] Dreyer | Why observational studies should be among the tools used in comparative effectiveness research[END_REF]. In 2007, the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) Statement was developed to help address these problems. It has since been endorsed by the International Committee of Medical Journal Editors and a number of journals [16e18]. However, endorsement rates remain low [19e23] and, while some studies have been conducted on editors' perceptions [START_REF] Grindlay | A survey of the awareness, knowledge, policies and views of veterinary journal Editors-in-Chief on reporting guidelines for publication of research[END_REF][START_REF] Fuller | What affects authors' and editors' Use of reporting guidelines? Findings from an online survey and qualitative interviews[END_REF], little is known about what authors think of reporting guidelines and how they perceive journal requests for completed checklists. To our knowledge, only one smallscale study, investigated author's perspectives on an RG, the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) Statement [START_REF] Fuller | What affects authors' and editors' Use of reporting guidelines? Findings from an online survey and qualitative interviews[END_REF].

To address this research gap, we conducted an online survey asking observational study authors about their experiences with and attitudes toward the STROBE Statement.

Methods

Data collection

Details of study methods were previously published [START_REF] Sharp | Using the STROBE statement: survey findings emphasized the role of journals in enforcing reporting guidelines[END_REF]. Briefly, data were drawn from a cross-sectional online survey on STROBE that was completed by authors of observational studies. The survey was distributed from March 5 to August 31, 2018 via social media, and emails to 257 biomedical journal editors and over 14,000 authors. Participants (n 5 1,015) comprised three groups including those who 1) had used the STROBE checklist before (group 3, n 5 635), 2) had heard of STROBE, but had not used it (group 2, n 5 195), and 3) were new to the concept of STROBE, and were asked to give their initial thoughts on it after a brief introduction (group 1, n 5 185).

The survey included questions about demographics, timing and frequency of use, awareness referral mechanisms, motivators, facilitators, and barriers to use. It concluded with an open-ended question: ''Do you have any other comments? Please feel free to expand on anything related to STROBE or this survey. For example, your experiences with STROBE, thoughts about its usefulness, content, format, the extensions, etc.'' Nearly 20% (n 5 203) of those who completed the survey responded to this open-ended question. After eliminating nonsubstantive responses (e.g., ''N/A''), 150 participants gave detailed feedback. Owing to the number and richness of responses, we are discussing these separately in this article as it has implications for understanding how to effectively implement STROBE and other reporting guidelines.

Analysis

Open-ended responses were imported from SurveyMonkey into R and then into NVivo 12 [START_REF]Buy NVivo Now | NVivo[END_REF]. Using inductive coding, one coder (M.K.S.) proposed the initial schema which the other coder (D.H.) used to code the first 100 responses (of the original 203); agreement was over 90% for all codes. Results were then discussed to identify any potential missing categories or disagreements. No issues were found and no changes were made.

Results

Demographic data for the full sample was reported previously [START_REF] Sharp | Facilitators and barriers to using the STROBE statement: an online survey of authors' perspectives[END_REF]. Our full sample had roughly equal distributions for age, gender, and time spent in research across groups and the qualitative respondents generally did as well (Table 1). Of the 150 qualitative respondents, 65% (n 5 98) had used STROBE before (group 3), 17% (n 5 26) had heard of STROBE, but had not used it (group 2), and 17% (n 5 26) had never heard of STROBE before nor used it (group 1). Representation was roughly equal between groups with 15% of each subgroup responding to the open-ended question.

Thematic coding established four main content areas: 1) mixed perceptions of the checklist, 2) academic confidence and self-assuredness, 3) use in education and training, and 4) journal endorsement and use in peer review. Owing to group imbalances and in the interest of transparency, the participant's subgroup accompanies each quote.

Mixed perceptions of the checklist

General perceptions of STROBE were mixed, ranging from positive reviews that hailed STROBE for how it ''helps in standardizing how research is reported and guides the author/researcher to ensure all the necessary information (that the reader would be looking for) is included'' (group 3) to harsh reviews that called it a ''procedural straightjacket'' (group 3).

Participants also had varied opinions on the additional time investment required to complete STROBE vs expected gain. Respondents referred to the uncertain impact on article publication despite the substantial amount of time required to complete the checklist which implicitly revealed their motivation for using the checklist: ''it also adds to the time required to put together a manuscript, and I am not sure how much it improves the chances of a manuscript being published'' (group 3). Conversely, the expected quality improvement was considered a key motivational aspect of using STROBE despite the additional working time required, ''it does increase the quality of the articles, it is clearly worth the time'' (group 3). STROBE's length and content is a key factor influencing the time needed to complete it. Several authors expressed concerns

(0) 0 (0) 0 (0) 0 (0) 3 (!1) 0 (0) 0 (0) 3 (!1)
Prefer not to say 4

that the checklist is too exhaustive and ''rigid,'' (group 3) reporting fears of an ''incomplete'' checklist giving the impression that their study is ''less than 'perfect'.'' (group 3).

These uncertainties stress the need for flexibility when using STROBE. Authors may ''fear the 'Checklist Manifesto' becoming a rigid bureaucracy, and also becoming contrived'' (group 1, ID1). Although Atul Gawande's ''Checklist Manifesto'' argues for implementing checklists [START_REF] Gawande | The checklist manifesto: How to get things right[END_REF], our authors cautioned that ''that balance between freedom and structure is important to consider'' (group 1, ID1) and that it is ''important to recognise that each study/analysis is unique and doesn't always fit with the recommendations'' (group 3, ID1).

In recognition of the variety of different types of observational studies, many field-and method-specific extensions to STROBE have been created to provide more nuanced guidance. However, some participants pointed out that these extensions have created needless complexity ''. additional confusion in reporting of observational studies'' (group 3) and that the ''number of extensions has become excessive, especially given that multiple extensions may apply to a single study,'' (group 3).

Academic confidence and self-assuredness

Although authors expressed the need for a general flexibility in use and assessments, they also conveyed strong beliefs in their abilities to adhere to the checklist and the standards that it contains. One of the most prevalent themes was the expression of self-assuredness.

''[I] follow the STROBE guidelines in my reporting reasonably well without actually referring to them or using a checklist'' (group 3, ID1) and ''[I] already apply the STROBE recommendations despite not having heard of it until today'' (group 1).

Many authors claimed to be using or following the checklist when, in fact, as demonstrated by the quotes aforementioned, it became evident that they were not completing it or sometimes had never even seen it before.

Furthermore, several authors conveyed their beliefs that STROBE ''is a waste of my time'' (group 3) in light of their own training and experience. However, they were ''glad that investigators with limited training are expected to use STROBE when they approach publication'' (group 3).

Despite the prevailing attitudes of self-confidence, there was also recognition that STROBE can be helpful to experienced researchers for quality assurance: ''even for those of us who have been researchers for many years, it is sometimes helpful to check a tool such as STROBE, to ensure that we have included everything'' (group 3).

Use in education and training

Despite experienced researchers generally not seeing a benefit to personally using STROBE, there were strong feelings that it is valuable to early-career researchers (ECRs). Many participants shared that they use STROBE's structure and content as an educational tool for ECRs to instill good practice in writing manuscripts. ''STROBE is useful for any observational researcher, but exceptionally useful for new researchers. it can help them structure their drafts and develop a strong foundation and habits as they write their first papers. We use it in our epidemiologic analysis course and hope that students continue to use it'' (group 3).

Aligned with an early intervention stance to intervene in the initial stages of one's research career, some also suggested that STROBE should be used earlier in the research process itself, like when writing study protocols. Some respondents also thought that intervening earlier would have the most impact on the final quality of reporting: ''To fully apply the criteria, I would need to systematically apply the STROBE criteria on the front end design of a project, grant, etc.rather than at the time of writing a project. Encouraging policy that focuses on a front end approach would be helpful'' (group 2).

Intervening at the early stages of research and in one's career could theoretically instill greater contemplation and caution in research planning. While in-depth analytical and epidemiological thinking is not embedded within STROBE, responses revealed that authors see an educational purpose in STROBE and expressed the need for optimization: ''woefully deficient in encouraging.use of appropriate data analytic approaches. Strobe should, for example, encourage analysts seeking causal effect estimates to highlight their assumptions with a causal diagram'' (group 3).

Journal endorsement and use in peer review

Aside from the personal and educational use of STROBE, many authors expressed beliefs that journals are largely responsible for properly implementing STROBE through mandatory enforcement ''I think the main way to increase its use is to make it mandatory before submission,'' (group 3) and ''guidelines should need to be obligatory for every study. Better implementation is needed'' (group 3).

However, it seems that a number of authors primarily looked at the administrative burden of using STROBE over its primary purpose, that is, ensuring that the study is completely reported. One participant noted that ''there are so many guidelines like STROBE, it can be difficult to put the energy into using STROBE (or any other) one a priori since ultimately, it depends on the journal submitted to and accepted to'' (group 3). While others expressed frustration that it is ''annoying to upload the STROBE checklist with journal submissions'' (group 3) that ''the elucidation of exact pages where the criteria were met, which I found arduous and a bit pedantic'' (group 3).

Although STROBE may be seen as an administrative burden to some, other reporting guidelines may not share similar harsh reviews as they are more broadly endorsed by journals. This difference in RG acceptance was pointed out by one participant: ''I am sorry to say that PRISMA and CONSORT have become mandatory but STROBE isn't?'' (group 3).

One possible reason for this difference in acceptance might be the relationship to other implementation efforts. For example, one author noted that the conventional nature of trial or protocol registration might affect the acceptability of RGs: ''since observational studies do not require prospective registration unlike RCTs or systematic reviews, I don't think STROBE is used as much as CONSORT or PRISMA even though these reporting guidelines substantially improve study design and reporting'' (group 3).

Key to the crux of the issue is again the perceived benefit and establishing a norm for requiring RGs. If an author spends time using a requested checklist, it should be used in the evaluation by peer reviewers and/or editors. However, one author noted a current problem with implementation: ''I have never had (nor have I heard of) an editor or reviewer pushing back on a claim that all STROBE criteria were met. Therefore, when a STROBE checklist is required for manuscript submission, it seems to turn into a[n] exercise in additional administrative busywork without really improving the research.'' (group 3, ID2).

Other survey respondents echoed concerns regarding the peer review process. When authors go through the trouble of completing a checklist, oftentimes there seems to be little benefit from using it as ''the information provided does not matter as the reviewers do not know what to do with it'' (group 3).

Despite these reported challenges, using STROBE in peer review can also be beneficial as it provides a reference of support when requesting additional information from authors: ''As a junior scientist it gives me confidence to request the reporting of a certain piece of information knowing I have the backing of STROBE'' (group 3).

Discussion

Responses revealed multiple misunderstandings about STROBE's purpose and content, and a lack of incentives for use. Our findings emphasize the need to better communicate the reasons for using STROBE and reporting guidelines in generaldexplaining their potential impact on reproducibility, clinical decision-making, and future research. It is important to convey the idea that complete and transparent reporting goes beyond perceived article publishability. Awareness and education campaigns are key to addressing skepticism and maladaptive beliefs regarding time requirements, benefits of use, and (over) self-confidence.

Part of these efforts must be focused on communicating the flexible nature of STROBE and the continued need for a strong epidemiological education which STROBE cannot replace. A reporting guideline cannot fix study aspects that were not thought about previously or were performed incorrectly. Related to this, some reported the need to use STROBE at earlier stages of research. This suggestion is aligned with a recent scoping review (2019) on interventions to improve adherence to reporting guidelines [START_REF] Blanco | Scoping review on interventions to improve adherence to reporting guidelines in health research[END_REF]. The authors found a general lack of attention given to interventions at the early stages of research and suggested that early-intervention policies (e.g., at the funder or ethical review board level) may be more effective in promoting more carefully designed studies.

Although reporting guidelines were not intended to be used for educational purposes, many authors reported that STROBE is useful for teaching early career researchers. STROBE can be a valuable tool to demonstrate how epidemiological concepts work together in practice and we encourage expansion and elaboration on its content. However, although suggestions for more detailed guidance are valid, there is only so much education that an RG can contain. STROBE is not meant to be a guideline on how to properly conduct research [START_REF] Da Costa | Uses and misuses of the STROBE statement: bibliographic study[END_REF], but it appears as though many participants did not see education and reporting as distinct concepts. In-depth continuous education should be provided to researchers as epidemiological understanding and critical thinking cannot be taught through an RG alone.

There are many leverage points in the system to target for increased implementation of STROBE; but ultimately the task is placed on authors as they are the creators and owners of the research. Authors highlighted several areas of concern that must be addressed to better implement reporting guidelines. Above all, the perceived benefit and impact of using STROBE must be communicated and established. Authors need to be reassured that extra time spent will be personally rewarding and impact scientific literature as a whole, as it will help knowledge synthesis efforts. Unlike previous work focused on the TREND guideline [START_REF] Fuller | What affects authors' and editors' Use of reporting guidelines? Findings from an online survey and qualitative interviews[END_REF], which found that authors did not think that their RGs took too long to complete, time and the perceived benefit of use was a recurring issue from our participants. Thus, there is a need for research investigating the impact of endorsement on completeness of reporting and on the submission process (e.g., likelihood of being published, speed of reviews, etc.).

To date, research on STROBE's impact has shown mixed resultsdeither showing no effect on the reporting of confounding [START_REF] Pouwels | Quality of reporting of confounding remained suboptimal after the STROBE guideline[END_REF] or insufficient evidence to determine an impact on overall completeness of reporting [START_REF] Stevens | Relation of completeness of reporting of health research to journals' endorsement of reporting guidelines: systematic review[END_REF]. However, recent work by Vilar o et al. demonstrated that having a methodological reviewer dedicated to looking for missing reporting guideline items (not only STROBE) increased the number of article citations by 43% [START_REF] Vilar O | Adherence to reporting guidelines increases the number of citations: the argument for including a methodologist in the editorial process and peer-review[END_REF]. This could be seen as an incentive for authors but also a proxy for perceived higher quality/impact. We need more research in this area to provide convincing evidence that additional time spent using STROBE can have a positive impact. Furthermore, when journals request a completed STROBE checklist, it should actually be used by editors and peer reviewers. Otherwise, authors may feel like they did extra work for no benefit. It is theorized that requiring a completed checklist is the most effective form of implementation by journals. However, most endorsement literature does not differentiate between requiring and recommending RGs, so it is unclear whether there are discernible differences on completeness of reporting [START_REF] Stevens | Relation of completeness of reporting of health research to journals' endorsement of reporting guidelines: systematic review[END_REF]. Journal editors have also been reluctant to enforcing RGs, expressing concerns that authors will switch to journals with easier submission processes, that their instructions to authors are sufficient, and that implementation would place undue burdens on reviewers [START_REF] Grindlay | A survey of the awareness, knowledge, policies and views of veterinary journal Editors-in-Chief on reporting guidelines for publication of research[END_REF].

Survey responses solidified often-discussed benefits and issues with reporting guidelines. There is a great potential to increase transparency and reproducibility through complete reporting, provide structure to manuscript writing, and educate early career researchers on the proper conduct of observational research. However, we need to better communicate flexibility to authors who are investing extra time, often perceived to be at the sacrifice of one's ego, to complete reporting checklists. Perhaps the most challenging aspect is the culture change needed to shift away from ingrained personal (over)confidence. Recognizing that these beliefs are quite common is the first step to better acknowledging the importance of humility. Although the ivory tower of academia is still standing, we might be inadvertently missing a few bricks.

CRediT authorship contribution statement Melissa K. Sharp: Conceptualization, Methodology, Formal analysis, Data curation. Ketevan Glonti: Writing -review & editing. Darko Hren: Validation, Investigation, Resources, Writing -review & editing, Supervision, Funding acquisition.

Table 1 .

 1 Sample demographics

			Qualitative Respondents (n [ 150)	Entire Sample of Survey Respondents (n [ 1,015)
			Never heard of	Heard of	Heard of		Never heard of	Heard of	Heard of
		Total	STROBE, never	STROBE, Never	STROBE, have	Total	STROBE, never	STROBE, Never	STROBE, have
		sample	used [group 1]	used [group 2]	used [group 3]	sample	used [group 1]	used [group 2]	used [group 3]
		N (%)	N (%)	N (%)	N (%)	N (%)	N (%)	N (%)	N (%)
	Variables	150 (100)	26 (17)	26 (17)	98 (65)	1,015 (100)	195 (19)	185 (18)	635 (62)
	Time spent in
	research
	1e10 y	42 (28)	6 (23)	7 (27)	29 (30)	332 (33)	57 (29)	65 (35)	210 (33)
	11e30	48 (32)	4 (15)	7 (27)	37 (38)	362 (36)	107 (55)	95 (51)	372 (59)
	31 þ	39 (26)	10 (38)	6 (23)	23 (23)	86 (10)	30 (15)	25 (14)	48 (8)
	I do not work	15 (10)	3 (12)	5 (19)	7 (7)	3 (!1)	1 (0)	0 (0)	2 (!1)
	in research
	Prefer not to say	6 (4)	3 (12)	1 (4)	2 (2)	3 (!1)	0 (0)	0 (0)	3 (!1)
	Age
	18e34	29 (19)	4 (15)	4 (16)	21 (21)	185 (18)	36 (19)	38 (21)	111 (1)
	35e54	74 (49)	9 (35)	11 (42)	54 (55)	589 (58)	101 (52)	83 (45)	405 (64)
	55 þ	46 (31)	13 (50)	11 (42)	22 (23)	235 (23)	58 (30)	64 (35)	113 (18)
	Prefer not to say	1 (!1)	0 (0)	0 (0)	1 (1)	6 (!1)	0 (0)	0 (0)	6 (!1)
	Gender
	Woman	69 (46)	12 (46)	12 (46)	45 (46)	469 (46)	97 (50)	82 (44)	289 (46)
	Man	77 (51)	13 (50)	14 (54)	50 (51)	525 (52)	94 (48)	101 (55)	329 (52)
	Trans	0

M.K. Sharp et al. / Journal of Clinical Epidemiology 123 (2020) 100e106

Acknowledgments

Authors' contributions: M.K.S. conceptualized the study and led the writing of the manuscript. K.G. contributed to the manuscript preparation. D.H. led the supervision of the manuscript preparation. M.K.S. and D.H. performed all analyses. All authors read and approved the final manuscript.