Sarah F Feldman

Nathanaël Lapidus

Céline Dorival

Alpha Diallo

Imane Amri

Hélène Fontaine

Stanislas Pol

Fabrice Carrat
email: fabrice.carrat@iplesp.upmc.fr

Sarah ; Feldman

Disproportionality Analysis of the ANRS CO22 HEPATHER Cohort to Identify Associations between Direct Acting Antivirals and Adverse Events in Patients with Hepatitis C Virus Chronic Infection

Keywords: Adverse Drug Event, Cohort Studies, Hepatitis C, Pharmacovigilance

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Purpose. Our aim was to explore a signal detection method for early identification of potential adverse drug reactions (ADRs) in a patient cohort. Methods. ANRS CO22 HEPATHER is a French multicentre prospective observational cohort started in 2012. The cohort includes patients with chronic hepatitis C virus (HCV) infection with reports of all adverse events (AEs) occurring in patients exposed to HCV drugs. We applied a disproportionality method, which calculated a measure of association, the Bayesian Information Component (IC), for each drug-AE pair. ICs were continuously updated and a positive drug-AE association was detected when the lower limit of an IC 95% Credible Interval (95%CI) exceeded 0. We illustrate how the method could result in timely detection of photosensitivity reaction with simeprevir use. Signal detection on a patient cohort: a disproportionality analysis of the ANRS CO22 HEPATHER cohort to identify associations between direct acting antivirals and adverse events in patients with Hepatitis C virus chronic infection Detection of adverse event in a patient cohort Sarah F Feldman 1, 2 , Nathanaël Lapidus 1, 2 , Céline Dorival 2 , Alpha Diallo 3 , Imane Amri 3 , Hélène Fontaine [START_REF] Van Holle | Using time-to-onset for detecting safety signals in spontaneous reports of adverse events following immunization: a proof of concept study[END_REF] , Stanislas Pol [START_REF] Van Holle | Using time-to-onset for detecting safety signals in spontaneous reports of adverse events following immunization: a proof of concept study[END_REF] , Fabrice Carrat 1, 2 for the ANRS/AFEF HEPATHER study group.

KEY POINTS

• Safety information from cohort studies can be used to detect adverse drug reactions.

• We applied a quantitative signal detection method for identifying potential adverse drug reactions in a cohort of patients with chronic hepatitis C exposed to new antiviral drugs. • The disproportionality method relied on the calculation of a Bayesian Information Component, and resulted in timely detection of both known and unknown associations between drugs and adverse events. • As with any signal detection method, a clinical review of the signal is required to confirm or eliminate the relevance of the detected association.

INTRODUCTION

Detection of potential adverse drug reactions (ADRs) 1 is a major concern. Although the gold standard for identifying and quantifying frequent ADRs is clinical trials, 2 the sample sizes of trials are usually too small to detect ADRs when the frequencies of adverse events (AEs) following treatment are rare.

To address this issue, spontaneous pharmacovigilance reporting systems have been set up to record incidental serious or unexpected AEs, and numerous analytical methods have been developed to link these events with current or past medication intake. [START_REF] Van Puijenbroek | A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions[END_REF][START_REF] Van Holle | Using time-to-onset for detecting safety signals in spontaneous reports of adverse events following immunization: a proof of concept study[END_REF][START_REF] Huang | Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection[END_REF][START_REF] Zhao | Extended likelihood ratio test-based methods for signal detection in a drug class with application to FDA's adverse event reporting system database[END_REF] Analyses of healthcare databases with thousands of patients exposed to a drug, eventually compared to thousands of unexposed patients, also allow the identification of associations between a drug and an AE, i.e., "a signal". Among various signal detection methods, disproportionality (DP) methods [START_REF] Van Puijenbroek | A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions[END_REF] are commonly applied to spontaneous reporting systems [START_REF] Wisniewski | Good Signal Detection Practices: Evidence from IMI PROTECT[END_REF][START_REF] Harpaz | Performance of Pharmacovigilance Signal-Detection Algorithms for the FDA Adverse Event Reporting System[END_REF][START_REF] Lindquist | the WHO Global ICSR Database System: Basic Facts[END_REF] or healthcare databases. [START_REF] Zorych | Disproportionality methods for pharmacovigilance in longitudinal observational databases[END_REF][START_REF] Suling | Signal Detection and Monitoring Based on Longitudinal Healthcare Data[END_REF] However, in some of these systems, and particularly in healthcare databases, the timeliness of data collection is a strong limitation when one wants rapid identification of a signal for a drug recently released on the market. In observational patient cohort studies, a large amount of pharmacovigilance data is also collected. Our main objective was to explore a signal detection method for early identification of potential ADRs in a patient cohort. We illustrate the potentials of the method for detecting associations between antiviral drugs and AEs in patients with chronic hepatitis C virus (HCV) infection included in the prospective ANRS CO22 HEPATHER cohort study.

METHODS Patients

The ANRS CO22 HEPATHER cohort ("Therapeutic options for hepatitis B and C: a French cohort") is a national multicentre prospective observational cohort study of patients with viral hepatitis B or C (this study is registered with ClinicalTrials.gov, number NCT01953458). [START_REF] Pol | study group. Safety and efficacy of daclatasvir-sofosbuvir in HCV genotype 1-mono-infected patients[END_REF] The cohort was set up in August 2012 with the main objectives to quantify the clinical efficacy and safety of new hepatitis treatments in real life. Written informed consent was obtained from each patient before enrolment.

The protocol was conducted in accordance with the Declaration of Helsinki and French law for biomedical research and was approved by the "CPP Ile de France 3" Ethics Committee (Paris, France)

and the French National Agency for Medicines and Health Products Safety (ANSM). HCV-positive patients were defined as patients with positive HCV-RNA or positive anti-HCV antibodies. Main exclusion criteria were HIV coinfection and being on HCV-treatment at inclusion. Enrolment of patients started on August 6, 2012 in two centres and was progressively extended to 32 centres by September 2014 to reach 14,599 patients with past or active HCV infection by August 28, 2016.

During the inclusion visit, detailed demographics, clinical (including history of past treatments) and biological data were collected using a dedicated electronic case-report form. Follow-up visits occurred once a year for every patient and the planned duration of follow-up is at least eight years.

In some but not all patients, HCV treatment with antivirals was initiated during regular patient visits.

The choice of the antiviral combination was left to the physician's discretion but followed the national guidelines. [START_REF]Prise en charge thérapeutique et suivi de l'ensemble des personnes infectées par le virus de l'hépatite C[END_REF] When an antiviral treatment was initiated, a specific schedule was implemented with additional visits 6 months after the last antiviral intake. Date of beginning and end of treatment, any dose modification, as well as all AEs regardless of their intensity occurring from first treatment intake until one month after the end of treatment were to be reported. AEs were described in terms of date of onset and resolution, intensity (using the ANRS grading scale), [START_REF]Echelle ANRS de cotation de la gravité des événements indésirables chez l'adulte[END_REF] and causality assessment; all events were coded centrally by a trained research assistant using the Medical Dictionary for Regulatory Activities (MedDRA, v17.0).

Signal detection

Signal detection in the Hepather database was performed using a disproportionality analysis method already applied to observational healthcare databases. [START_REF] Zorych | Disproportionality methods for pharmacovigilance in longitudinal observational databases[END_REF][START_REF] Dumouchel | Evaluation of Disproportionality Safety Signaling Applied to Healthcare Databases[END_REF][START_REF] Bate | A Bayesian neural network method for adverse drug reaction signal generation[END_REF][START_REF] Bate | The use of Bayesian confidence propagation neural network in pharmacovigilance[END_REF] This method calculates a measure of association, the Bayesian Information Component (IC), for each drug-AE pair reported in the database. The IC is the logarithm of the ratio of the observed count of drug i reported with AE j to its expected value under the hypothesis of no association (i.e. independence) between the drug and the AE. Weighting is added to both observed and expected counts to increase stability at very low numbers of AEs. [START_REF] Norén | Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events[END_REF] Using this method and under the hypothesis that the IC is normally distributed, the IC and its variance V(IC) are calculated by [START_REF] Bate | A Bayesian neural network method for adverse drug reaction signal generation[END_REF] :

ܥܫ = ݈݃ ଶ ( ೕ ାఊ భభ )(ା)(ାఉ) ( ା భ )( ೕ ାఉ భ )(ାఊ) )ܥܫ(ܸ = ቀ ଵ (ଶ) ቁ ଶ ି ೕ ାఊିఊ భభ ൫ ೕ ାఊ భభ ൯(ଵାାఊ) + ି ାି భ ( ା భ )(ଵାା) + ି ೕ ାఉିఉ భ ൫ ೕ ାఉ భ ൯(ଵାାఉ) ൨ with weights α ଵ = 1, α = 2, ߚ ଵ = 1 ߚ = 2, ߛ ଵଵ = 1 and ߛ = ߛ ଵଵ (ା)(ାఉ) ( ା భ )( ೕ ାఉ భ )
ܥ is the total number of events, ܿ is the number of times a specific drug-AE pair is reported, ܿ the number of reports of a specific drug in the database and ܿ the number of reports of a specific AE in the database. The weights α 1 , α, β 1 , β and γ 11 are coefficients in the prior beta distributions for the probability of a drug, an AE, or a drug-AE pair to be listed in the database. Their values were chosen under the a priori hypothesis of no association between the drug and the AE.

The IC and V(IC) are updated each time a new unit of a drug-AE pair is reported in the database.

Based on the posterior normal probability distribution for IC, 95% credible intervals (95%CI) are calculated as plus or minus two standard deviations from the estimated IC value. A signal is identified when the lower bound of the 95%CI exceeds 0 (positive association) or when the upper bound of the 95%CI is less than 0 (negative association). [START_REF] Bate | A Bayesian neural network method for adverse drug reaction signal generation[END_REF] Positive drug-AE associations are considered to be potential ADRs and candidates for further clinical review or investigation. In a sensitivity analysis, we

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explored the impact of other choices for prior coefficients α 1 , α, β 1 , β, γ 11 on the total number of positive drug-AE associations.

Application to the HEPATHER cohort

The selection of drug-AE pairs for our analysis required defining the at-risk period of a drug, [START_REF] Zorych | Disproportionality methods for pharmacovigilance in longitudinal observational databases[END_REF] which was chosen as the time between first to last treatment intake + 30 days. We considered a patient as experiencing a potential ADR when an AE was reported during this at-risk period. In addition, two issues had to be addressed to select drug-AE pairs to include in the analysis: (1) Several drugs might be combined while the signal detection is applied to each drug individually (2) A patient might experience recurrent AEs but the drug-AE pair should not be counted more than once in a patient.

Therefore we proceeded as follows (for illustration we assume a combination of two drugs A and B):

(1) we generated a drug-AE pair for each drug in the combination (e.g. for a combination of two drugs A and B: A-AE, B-AE); (2) for each drug-AE pair we checked if it was already reported in this patient, and if not, we kept the pair for analysis. Finally, for each drug-AE pair, we built a two dimensional contingency table (table 1). [START_REF] Zorych | Disproportionality methods for pharmacovigilance in longitudinal observational databases[END_REF] AEs were analyzed at two different hierarchical levels of coding in the MedDRA terminology, the Preferred Term (PT), a level describing a single medical concept, and the System Organ Classes (SOC), a level grouping AEs by etiology, manifestation site, or purpose. Only pairs with a minimum of 1 AE reported were selected (minimum required value of ܿ for the IC calculation with PT and with SOC). [START_REF] Dumouchel | Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System[END_REF] To calculate the positive predictive value (PPV) of our method at both the PT and SOC levels, we built a reference set with all described ADRs reported in the summary of product characteristics (SPC) for each drug considered in our study. We used the SPC available at the date of the To illustrate the method, we chose known ADRs with hepatitis C drugs that are often given together in combination: simeprevir and sofosbuvir with photosensitivity reaction (PT), an association first reported in 2010, [START_REF] Reesink | Rapid HCV-RNA Decline With Once Daily TMC435: A Phase I Study in Healthy Volunteers and Hepatitis C Patients[END_REF][START_REF] Edition ; Ira | NetherlandsSpecial Reporting on:• Simeprevir Plus Peginterferon/Ribavirin Is Associated with a High SVR12 Rate in Treatment-Naive Patients with Genotype 1 Hepatitis C Virus Infection• Addition of Simeprevir to Peginterferon/Ribavirin Is Associated with Faster Resolution of Fatigue in Treatment-Naive Patients• Sofosbuvir Plus Ribavirin Demonstrates Significant Efficacy in Multiple HCV Genotype 2/3 Populations• Daclatasvir Plus Sofosbuvir with or without Ribavirin Yields 100% SVR24 Rate in Genotype 1 Patients Who Fail Telaprevir or Boceprevir• Addition of TG4040 Vaccine to Peginterferon/Ribavirin Increases Sustained Virologic Response Rate at 24 Weeks in Genotype 1 Hepatitis C InfectionPLUS Meeting Abstract Summaries With Expert Commentary by[END_REF] and dasabuvir, ombitasvir, paritaprevir and ritonavir with hyperbilirubineamia (PT), an association first reported in 2012. [START_REF] Edition | Advances in the Treatment of Hepatitis C Virus Infection From AASLD 2012: The 63rd Annual Meeting of the American Association for the Study of Liver DiseasesNovember 9-13, 2012 • Boston, MassachusettsSpecial Reporting on:• Timing and Magnitude of Ribavirin Dose Reduction Do Not Impact SVR Rates with Boceprevir Plus Peginterferon α and Ribavirin• A 12-Week Interferon-Free Treatment Regimen with ABT-450/r, ABT-267, ABT-333, and Ribavirin Achieves High SVR12 Rates• High Rate of SVR with the All-Oral Combination of Daclatasvir Plus Sofosbuvir with or without Ribavirin• An Interferon-Free, Ribavirin-Free 12-Week Regimen of Daclatasvir, Asunaprevir, and BMS-791325 Achieved High SVR4 RatesPLUS Meeting Abstract Summaries With Expert Commentary by: Fred Poordad, MDUniversity of San Antonio Health Science CenterSan Antonio, Texas[END_REF][START_REF] Kowdley | Phase 2b Trial of Interferon-free Therapy for Hepatitis C Virus Genotype 1[END_REF] We also investigated in detail two positive drug-AE associations which were not reported as known ADRs: ledipasvir with chest pain (PT) and asunaprevir with cardiac disorders (SOC).

All

Positive HCV drug-AE associations not known as ADRs

Among the eight positive drug-PT associations which were not identified in the safety notices (table A2-1 in appendix 2), the association between ledipasvir (always combined with sofosbuvir as fixeddose pill) and chest pain (PT) was detectable on July 3, 2015 with an estimated IC of 1. when 2100 patients had received ledipasvir (figure 3). We performed additional analysis as this positive association between ledipasvir and chest pain could be potentially confounded by the systematic combination of ledipasvir with sofosbuvir, and in some patients with advanced liver disease, to the added use of ribavirin. Chest pain is one reported symptom of anaemia, a frequent AE caused by ribavirin. However, we could not detect associations either between sofosbuvir and chest pain, (estimated IC -0.01, 95%CI [-0.88; 0.86]) or between ribavirin and chest pain (estimated IC -0.04, 95%CI [-1.07; 0.98]). Investigation of the cohort database for these 9 ledipasvir-chest pain reports revealed that one chest pain episode was attributed by the clinician investigator to pulmonary mycobacterial infection and one occurred in a patient with a recent history of colorectal cancer, but we did not identify any other potential specific causes in the other patients. The delay between the first intake of ledipasvir and onset of chest pain was on average 91 days (min: 48 days;

F o r R e v i e w O n l y max: 210 days).
The other detected positive drug-PT associations which were not known ADR (ranked by order of IC) were simeprevir-sunburn, simeprevir-dyspepsia, ledipasvir-depressed mood, telaprevir-neutropenia, daclatasvir-varices oesophageal, ledipasvir-vertigo and daclatasvir-oedema peripheral.

Five positive drug-SOC associations which were not reported in the safety notices were found (table A2-2 in appendix 2), including an association between asunaprevir and cardiac disorders. The specific AEs reported in patients exposed to asunaprevir were 3 atrial fibrillations and 1 cardiac failure.

Although an association of atrial fibrillation with asunaprevir has been suspected but not confirmed [START_REF] Manns | Alloral daclatasvir plus asunaprevir for hepatitis C virus genotype 1b: a multinational, phase 3, multicohort study[END_REF] , all patients who received asunaprevir also received daclatasvir, a drug which is known to be associated with some cardiac events. [START_REF] Pol | study group. Safety and efficacy of daclatasvir-sofosbuvir in HCV genotype 1-mono-infected patients[END_REF][START_REF] Coilly | CUPILT study group. Multicentre experience using daclatasvir and sofosbuvir to treat hepatitis C recurrence -The ANRS CUPILT study[END_REF][START_REF] Hézode | Simeprevir and daclatasvir for 12 or 24 weeks in treatment-naïve patients with hepatitis C virus genotype 1b and advanced liver disease[END_REF] In addition, detailed clinical review showed that all patients who experienced asunaprevir-cardiac disorders had a past history of cardiac disorders and were receiving co-medications at the time of the event (including one patient treated with amiodarone). The delay between the first intake of asunaprevir and onset of cardiac event was on average 29 days (min: 0 days; max: 65 days). The other positive drug-SOC associations were peginterferon alpha-2b-surgical and medical procedures, simeprevir-injury poisoning and procedural complications, simeprevir-eye disorders and ledipasvir-vascular disorders. With a database of 6600 patients receiving HCV treatment, and 12,720 drug-AE pairs reported in different patients, we could detect 52 potential ADRs when the analysis was performed at the PT level (1.3% of the total drug-PT pairs) and 29 potential ADRs when the analysis was performed at the SOC level (7.8% of the total drug-SOC pairs). Eighty to eighty-five percent of detected potential ADRs were confirmed by a review of safety notices. In addition, the detection could occur early with a limited number of specific drug-AE reports, as illustrated by the positive association between simeprevir and photosensitivity reaction that was based on a total of only 4 reports and was detected just 1 month after market authorization for this drug in France. We found also that the method did not detect a lot of spurious associations, and as an illustration, no association was reported between sofosbuvir and photosensitivity reaction, a drug that is systematically combined with simeprevir but also combined with other antiviral drugs. The coincidental associations of hyperbilirubinemia with dasabuvir and ombitasvir, paritaprevir, ritonavir were expected as these drugs are to be given together; hyperbilirubinemia was almost surely caused by the boosted protease inhibitor Paritaprevir/ritonavir inhibition of the bilirubin transporter OATP1B1 as reported with other NS3 protease inhibitors. [START_REF] Monteagudo | The metabolism and disposition of a potent inhibitor of hepatitis C virus NS3/4A protease[END_REF] Compared to the analysis at the SOC level, the analysis at the PT level decreased the rate of detected positive association among all reported drug-AE pairs. On the contrary, associations detected at the PT level might be more relevant to investigate as they are related to a more specific characterization of the ADRs. [START_REF] Hill | An attempt to expedite signal detection by grouping related adverse reaction terms[END_REF][START_REF] Pearson | Influence of the MedDRA® hierarchy on pharmacovigilance data mining results[END_REF] However, both analyses are recommended to increase sensitivity and specificity of signal detection. [START_REF] Bate | Terminological challenges in safety surveillance[END_REF] There are several limitations in this study. First, we didn't distinguish between AEs reported with a prior knowledge of a potential ADR from those reported without such knowledge. For example, photosensitivity reaction reports with simeprevir might have been driven by prior specific awareness

F o r R e v i e w O n l y
of this event when using this drug, and the detected signal could be a consequence of over-reporting in simeprevir-treated patients (or of underreporting in patients receiving other treatments). By contrast, prior knowledge of a potential ADR could bias the association towards the null if there was an indication to use other drugs in at-risk patients. As a consequence, the association with the AE could be detected with other unrelated drugs, a bias similar to reverse causality. Confounding by indication and reverse causality also likely explain some striking positive (or negative) drug-AE associations, such as the positive associations between daclatasvir with varices oesophageal or oedema peripheral, as these events are complications of cirrhosis and daclatasvir was preferentially prescribed to patients with cirrhosis at the initial stage of the cohort. These issues of confounding and reverse causality could be addressed with adjustments on patient characteristics or using statistical models for causal inference. [START_REF] Robins | Marginal structural models and causal inference in epidemiology[END_REF] Second, we performed the analysis at the drug level and not at the drug combination level, which prevents detecting associations between AEs and specific drug-drug interactions. However, adapting the method to explore the association of AEs with combinations of drugs would be straightforward.

Third, we did not quantify the sensitivity and specificity of the disproportionality analysis on this cohort databases with a limited number of patients. However, other works performed on simulated data with millions of patients reported adequate performances in terms of sensitivity / specificity for this method. [START_REF] Zorych | Disproportionality methods for pharmacovigilance in longitudinal observational databases[END_REF][START_REF] Ahmed | Bayesian pharmacovigilance signal detection methods revisited in a multiple comparison setting[END_REF] We found that detected positive drug-AE associations were only slightly affected by the choice of prior coefficients α 1 , α, β 1 and β. However the number of positive associations were sensitive to the prior coefficient γ 11 , related to the prior probability of a specific drug-AE association.

In addition, we showed that the positive predictive values of positive signal detection were over 80% assuming that the positive drug-AE associations that were not reported in our reference set did not correspond to unknown but nevertheless true ADRs.

Fourth, the disproportionality method did not take into account the duration of exposure to the different drugs, as this kind of information is usually not reported in pharmacovigilance database.

Other methods dealing with duration of exposure to a drug could be tested for that purpose, such as

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the Information Component Temporal Pattern Discovery (ICTPD), [START_REF] Norén | Temporal pattern discovery in longitudinal electronic patient records[END_REF] the Longitudinal Gamma Poisson Shrinker (LGPS) [START_REF] Schuemie | Methods for drug safety signal detection in longitudinal observational databases: LGPS and LEOPARD[END_REF] or the Longitudinal Evaluation of Observational Profiles of Adverse Events Related to Drugs (LEOPARD). [START_REF] Schuemie | Methods for drug safety signal detection in longitudinal observational databases: LGPS and LEOPARD[END_REF] Their performances have been tested on real or simulated healthcare or observational databases, [START_REF] Ryan | A Comparison of the Empirical Performance of Methods for a Risk Identification System[END_REF][START_REF] Ryan | Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership[END_REF] including millions of patients and reports. How these methods perform for early signal detection on cohorts of thousands of individuals with regularly updated databases remains to be evaluated.

Fifth, variance and 95% credible intervals calculations were performed under the hypothesis of a normal distribution for the IC, and this assumption may not hold true when the number of drug-AE pairs are limited. In this case, other methods based on Monte-Carlo simulations or tabular methods can be used. [START_REF] Norén | Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events[END_REF] A final limitation, common to all signal detection methods, is the impossibility to draw conclusions from a detected association on the causal relationship between exposure to the drug and the AE: any detected signal has to be explored for clinical validation. [START_REF] Suling | Signal Detection and Monitoring Based on Longitudinal Healthcare Data[END_REF][39][40][41] To conclude, the disproportionality method can help with early detection of drug-AE associations using information collected from cohorts of thousands of patients. Other methods that take account of the duration of exposure could also be promising in this context and warrant further explorations. c i is the number of reports of a specific drug in the database, c j the number of reports of a specific AE in the database. c ii is the number of times a specific drug-AE association was reported, and C is the total number of events. ADR: adverse drug reaction. † at the date of detection. ‡ at the date of August 28, 2016

Methodology and Coordinating

F o r R e v i e w
O n l y the ANRS CO22 HEPATHER cohort study is sponsored by ANRS -the French Agency on AIDS and Viral Hepatitis -a public agency. ANRS has received fundings from BMS, Gilead, Abbvie, Janssen, MSD to support this cohort study (but not specifically in relation with this paper). Signal detection on a patient cohort: a disproportionality analysis of the ANRS CO22 HEPATHER cohort to identify associations between direct acting antivirals and adverse events in patients with HCV chronic infection 9. Author's full name (a separate form must be submitted for each author)

Sarah F FELDMAN The ANRS CO22 HEPATHER cohort study is sponsored by ANRS -the French Agency on AIDS and Viral Hepatitis -a public agency. ANRS has received fundings from BMS, Gilead, Abbvie, Janssen, MSD to support this cohort study (but not specifically in relation with this paper). Signal detection on a patient cohort: a disproportionality analysis of the ANRS CO22 HEPATHER cohort to identify associations between direct acting antivirals and adverse events in patients with HCV chronic infection Signal detection on a patient cohort: a disproportionality analysis of the ANRS CO22 HEPATHER cohort to identify associations between direct acting antivirals and adverse events in patients with HCV chronic infection Oral presentations, boards, invitatinons for national and international meetings, sub investigator for therapeutic trials: BMS, Gilead, MSD, Roche, Janssen and Abbvie.

Manuscript title (first six words are sufficient)

Signal detection on a patient cohort: a disproportionality analysis of the ANRS CO22 HEPATHER cohort to identify associations between direct acting antivirals and adverse events in patients with HCV chronic infection 9. Author's full name (a separate form must be submitted for each author)

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• been paid as a consultant (or in a similar capacity) by a company with a vested interest in the product being studied, on issues related to the product being studied; Yes

• been paid as a consultant (or in a similar capacity by a company with a vested interest in the product being studies, on issues unrelated to the product being studied; Yes Signal detection on a patient cohort: a disproportionality analysis of the ANRS CO22 HEPATHER cohort to identify associations between direct acting antivirals and adverse events in patients with HCV chronic infection 9. Author's full name (a separate form must be submitted for each author) • been paid as a consultant (or in a similar capacity) by a company with a vested interest in the product being studied, on issues related to the product being studied; No

• been paid as a consultant (or in a similar capacity by a company with a vested interest in the product being studies, on issues unrelated to the product being studied; No the ANRS CO22 HEPATHER cohort study is sponsored by ANRS -the French Agency on AIDS and Viral Hepatitis -a public agency. ANRS has received fundings from BMS, Gilead, Abbvie, Janssen, MSD to support this cohort study (but not specifically in relation with this paper).

  Adverse Drug Event; Cohort Studies; Hepatitis C; Pharmacovigilance

DISCUSSION

  Disproportionality analysis can be applied to patient cohorts for early detection of potential ADRs.

  Hospitalier Intercommunal, Créteil, France), Véronique Loustaud-Ratti, Paul Carrier, Maryline Debette-Gratien, Jérémie Jacques (CHU Limoges, Limoges, France), Philippe Mathurin, Florent Artu, Valérie Canva, Sébastien Dharancy, Alexandre Louvet (CHRU Claude Huriez, Lille, France), Anne Minello, Marc Bardou, Donya Da Costa Souhiel, Patrick Hillon, Marianne Latournerie (Dijon University Hospital, Dijon, France), Louis D'Alteroche, Yannick Bacq, Didier Barbereau, Charlotte Nicolas (CHU Trousseau, 37044 Tours, France), Nisserine Ben Amara, Danièle Botta-Fridlund, Isabelle Portal (CHU Timone, Marseille, France), Moana Gelu-Simeon, Marie-Josée Lafrance, Eric Saillard (CHU de Pointeà-Pitre, Pointe-à-Pitre, Guadeloupe) Scientific Committee: -Voting members : Marc Bourlière (Hôpital St Joseph, Marseille, Paris), Patrice Cacoub (Hôpital Pitié salpêtrière, Paris, France), Fabrice Carrat (Scientific Coordinator, Hôpital Saint-Antoine, Paris, France), Patrizia Carrieri (INSERM U912, Marseille, France), Victor De Ledinghen (Hôpital Haut-Lévêque, Pessac, Bordeaux, France), Elisabeth Delarocque-Astagneau (INSERM 1181, Paris, France), Céline Dorival (UPMC & INSERM U1136, Paris, France), Jean Dubuisson (Inserm U1019, Lille, France), Hélène Fontaine (Hôpital Cochin, Paris, France), Chantal Housset (Hôpital St Antoine, Paris, France) Dominique Larrey (Hôpital Saint Eloi, Montpellier, France), Patrick Marcellin (Hôpital Beaujon, Clichy, France), Philippe Mathurin (CHRU Claude Huriez, Lille, France), Pierre Nahon (Hôpital Jean Verdier, Bondy, France), Georges-Philippe Pageaux (Hôpital Saint Eloi, Montpellier, France), Jean-Michel Pawlotsky (Hôpital Henri Mondor, Créteil, France), Ventzislava Petrov-Sanchez (ANRS, Paris, France), Stanislas Pol (Principal Investigator, Hôpital Cochin, Paris, France), Sophie Vaux (Institut de veille Sanitaire, Saint-Maurice, France), Linda Wittkop (ISPED-INSERM U897, Bordeaux, France), Yazdan Yazdanpanah (Hôpital Bichat Claude Bernard, Paris, France), Jean-Pierre Zarski (CHU de Grenoble, Grenoble, France), Fabien Zoulim (Hospices Civils de Lyon, Lyon, France).

-

  Non voting members: Marianne L'hennaff (ARCAT-TRT-5-CHV, France), Michèle Sizorin (SOS hépatites, France); one representative of INSERM-ANRS Pharmacovigilance team, Paris, France (Imane Amri, Alpha Diallo), Mélanie Simony (INSERM-ANRS, Paris, France), one member of Inserm Transfert, Paris, France (Françoise Crevel, Mireille Caralp), and one representative of each pharmaceutical company (MSD, Janssen, Gilead, Abbvie, BMS, Roche). Sponsor: Imane Amri, Carole Cagnot, Alpha Diallo, Ventzi Petrov-Sanchez (coordinator), Mélanie Simony (INSERM-ANRS, Paris, France).

8 .

 8 Manuscript title (first six words are sufficient)

8 .

 8 Manuscript title (first six words are sufficient) Signal detection on a patient cohort: a disproportionality analysis of the ANRS CO22 HEPATHER cohort to identify associations between direct acting antivirals and adverse events in patients with HCV chronic infection 9. Author's full name (a separate form must be submitted for each author) DORIVAL 10. In checking this box, I confirm I have completed this form to the best of my knowledge.

FONTAINE 10 .

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/a 2 .

 2 Question 4): POTENTIAL STUDY INTERPRETATION CONFLICTS 1. Some or all of the data that were used in this study were provided by a company with a vested interest in the product being studied. nThe sponsor of this project had the right of commenting but the authors retained the right to accept or reject comments or suggestions. n/a 3. The sponsor of this project had the right of final editing and/or approval of the manuscript submitted. n/a Corresponding author and Co-authors: POTENTIAL FINANCIAL CONFLICTS 4. I, my spouse, or one of my dependent children is an employee of a company whose product is being studied. No 5. I, my spouse, or one of my dependent children has significant equity interest (>USD 10,000) in the company that owns the product being studied. No

Pol 10 . 6 .

 106 In checking this box, I confirm I have completed this form to the best of my knowledge.This form is available online by clicking here In the past three years I have:

8 .

 8 Manuscript title (first six words are sufficient) Signal detection on a patient cohort: a disproportionality analysis of the ANRS CO22 HEPATHER cohort to identify associations between direct acting antivirals and adverse events in patients with HCV chronic infection 9. Author's full name (a separate form must be submitted for each author) Carrat 10. In checking this box, I confirm I have completed this form to the best of my knowledge. This form is available online by clicking here Page 56 of 55 http://mc.manuscriptcentral.com/pds Pharmacoepidemiology and Drug Safety

  WORD COUNT -3202 wordsAn abstract of this study was presented in March 2017 at the 21 st International Workshop on HIV and

	F o F o
	Hepatitis Observational Databases. r r
	R R
	e e
	v i v i
	e e
	w w
	O n O n
	l l
	y y

  Concerning the drug-PT association dasabuvir-hyperbilirubinaemia, a positive signal was detectable on April 14, 2015. At this date 77 patients had been receiving dasabuvir and the estimated IC was 1.26, 95%CI [0.02; 2.51] (figure2A). Similarly, a positive signal was detectable on March 12, 2015 for the associations of hyperbilirubinaemia with ombitasvir and paritaprevir (always combined) with an

	RESULTS
	General findings
	By August 28, 2016, 6600 patients with chronic HCV infection at entry in the cohort had been treated
	or were undergoing current HCV treatment, and 3464 patients had experienced at least one AE. In
	total 13,306 drug-AE pairs were reported, of which 12,720 were reported at least once in different estimated IC of 1.52, 95%CI [0.13; 2.92] and with ritonavir (combined with ombitasvir and
	patients (table 2). paritaprevir but sometimes used as a booster with other drugs) with an estimated IC of 1.42, 95%CI
	When the analysis was performed at the PT level, 3550 different drug-AE pairs were reported 1 to 9 [0.03; 2.80].
	times, 423 were reported 10 to 99 times, and 60 were reported more than 100 times (table 3). At the
	statistical computing and analysis were performed with the R statistical computing open source F o SOC level, 149 different drug-AE pairs were reported 1 to 9 times, 121 pairs 10 to 99 times and 65 more than 100 times. We detected 95 potential drug-PT signals, among which 52 were positive F o
	r r R associations, including 44 ADRs reported in the reference set (PPV = 85%, 95% confidence interval software (version 3.3.1). 25 R
	e [72%; 93%]) (table 4 -see also appendix 2). We detected 49 potential drug-SOC signals among which e
	v i 26 were positive associations, including 21 ADRs reported in the reference set (PPV = 81%, 95% v i
	e confidence interval [61%; 93%]). These results were only slightly affected by different choices for the e
	w prior coefficients α 1 , α, β 1 and β. However the number of positive associations were sensitive to the w
	O n prior coefficient γ 11 , related to the prior probability of a specific drug-AE association (see appendix 3). O n
	l l
	y y
	Illustration with known ADRs caused by HCV drugs
	A positive association between simeprevir and photosensitivity reaction (PT), could have been
	detected on June 4, 2014. At this date 68 patients had received simeprevir, 157 AEs had been
	reported during the simeprevir at risk period, and a total of 6 photosensitivity reactions (4 during the
	simeprevir at risk period) were reported, for an estimated IC of 1.9, 95%CI [0.20; 3.61] (figure 1A -
	see also appendix 2). In contrast, no signal was detected between sofosbuvir and photosensitivity

reaction: by August 28, 2016, 6,139 patients had been receiving sofosbuvir and the estimated IC was 0.16, 95%CI [-0.43; 0.74] (figure

1B

).

Table 1 .

 1 Two by two contingency table for a drug-AE pair i-j

	F o
	r
	R
	e
	v i
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	w
	O n
	l
	y

Centre: Lynsée Anastase, Loubna Ayour, Manon Bergier, Vincent Bonnemains, Fabrice Carrat (coordinator), Frederic Chau, Céline Dorival, Sarita Ghanem, Audrey Gilibert, Isabelle Goderel, Georges Haour, Sandy Lucier, Ophélie Lutton, Godwin Mawuvi, Lea Mba-Mintsa, Caroline Montaudouin, Dorian Multedo, Elodie Munier, Marion Pirot, Noëlle Pouget, Emmanuelle Quinty, Evelyne Rasamoelina, Claire Vezier (UPMC & INSERM U1136, Paris, France).

Table 2 .

 2 Patients characteristics

	HCV past or present

experiencing AE during the at risk period § 3464 Patients experiencing serious AE during the at risk period § 760 AE during the at risk period § 12,720 Serious AE during the at risk period § 1321

	F o
	r
	R
	e
	v i
	e
	w
	† at inclusion	O n l
	‡ most frequent combinations of drugs were sofosbuvir/daclatasvir+/-ribavirin (n = 2184), sofosbuvir/ledipasvir+/-ribavirin (n = 2092), sofosbuvir/simeprevir+/-ribavirin (n = 914), y
	sofosbuvir/ribavirin (n = 560), sofosbuvir/pegylated interferon/ribavirin (n = 273),
	telaprevir/pegylated interferon/ribavirin (n = 46), pegylated interferon/ribavirin (n = 107),
	boceprevir/pegylated interferon/ribavirin (n = 9), ombitasvir/paritaprevir/ritonavir/dasabuvir+/-
	ribavirin (n = 382), ombitasvir/paritaprevir/ritonavir +/-ribavirin (n = 115), elbasvir/grazoprevir+/-
	sofosbuvir (n = 16), others (n = 74)
	§ from first treatment intake to D+30 after last treatment intake

Table 3 .

 3 Distribution of the number of drug-AE pairs reported

		1	2	3 to 9	10 to 99	100 and more Total
	PT	2205	581	764	423	60	4033
	SOC	48	17	84	121	65	335

Table 4 .

 4 IC results byAugust 28, 2016

		PT	SOC
		n (%)	n (%)
	IC > 0	52 (1.3%)	26 (7.8%)
	IC includes 0	3933 (97.6%)	286 (85.4%)
	IC < 0	43 (1.1%)	23 (6.9%)

Table A1 -

 A1 Source of summary of products characteristics

		Pharmacoepidemiology and Drug Safety
	APPENDIX 2
	International non-	Trade name	Source	Language First	Last
	proprietary name				published	updated
	Daclatasvir	Daklinza	EMA	English	15/09/2014 15/03/2017
	Boceprevir	Victrelis	EMA	English	03/08/2011 21/11/2017
	Peginterferon alpha-2a Peginterferon alpha 2b Simeprevir Asunaprevir Dasabuvir Telaprevir Ombitasvir/paritaprevir/ ritonavir Ritonavir Sofosbuvir Grazoprevir/elbasvir Ledipasvir/sofosbuvir Ribavirin	pegasys PegIntron Olysio Exviera Incivo Viekirax Norvir Sovaldi Zepatier Harvoni Rebetol F o r R e v i e w EMA English 16/11/2009 18/10/2017 EMA English 17/09/2009 23/08/2017 EMA English 04/06/2014 22/08/2017 Not available EMA English 12/02/2015 17/10/2017 EMA English 03/10/2011 06/10/2016 EMA English 09/03/2015 16/10/2017 EMA English 15/11/2009 27/10/2017 EMA English 05/02/2014 18/10/2017 EMA English 28/07/2016 17/05/2017 EMA English 04/12/2014 05/09/2017 EMA English 22/10/2009 29/03/2017 O n l y

EMA: European Medicines Agency http://mc.manuscriptcentral.com/pds

Table A2 -

 A2 1 AE-drug positive associations with HCV therapy at the PT level, in decreasing order of IC.

	Page 33 of 55		Pharmacoepidemiology and Drug Safety Pharmacoepidemiology and Drug Safety Pharmacoepidemiology and Drug Safety
	Peginterferon alpha-2b Neutropenia Ombitasvir Hyperbilirubinaemia Ombitasvir Asthenia	April 28, 2014 March 12, 2015 May 27, 2015	29 41 140	80 32 160	119 10 217 4 960 23	2146 9732 10,715 0.62 [0.31-0.93] 1.36 [0.47-2.24] 0.83 [0.21-1.45]	Yes Yes Yes
	Boceprevir Paritaprevir Paritaprevir	Leukopenia Hyperbilirubinaemia Asthenia	October 15, 2014 March 12, 2015 May 27, 2015	25 41 140	64 32 160	305 7 217 4 960 23	7275 9732 10,715 0.62 [0.31-0.93] 1.33 [0.24-2.42] 0.83 [0.21-1.45]	Yes Yes Yes
	Drug Ledipasvir Peginterferon alpha-2a Peginterferon alpha-2a Anaemia Event (PT) Chest pain Gamma-glutamyltransferase Simeprevir Photosensitivity reaction Telaprevir Anal pruritus Peginterferon alpha-2a Neutropenia Peginterferon alpha-2a Influenza like illness Simeprevir Sunburn Peginterferon alpha-2b Influenza like illness Peginterferon (no more precision) Neutropenia Boceprevir Anaemia F o r R e v i e w Date of detection Patients treated with drug i † July 3, 2015 1243 October 13, 2014 378 August 22, 2014 341 June 4, 2014 68 June 2, 2014 62 March 19, 2014 214 May 15, 2014 280 July 11, 2014 225 June 15, 2014 32 June 15, 2014 18 April 15, 2014 24 O n l y c i † 1058 17 c j † 1608 127 42 c ij † 6 1516 117 44 157 6 4 248 4 4 700 82 62 1148 46 32 314 4 4 103 49 5 43 174 6 62 61 6 Peginterferon alpha-2a Thrombocytopenia June 10, 2014 310 1275 160 79 Telaprevir Anaemia June 17, 2014 64 257 89 12 Dasabuvir Hyperbilirubinaemia April 14, 2015 77 67 224 5 Simeprevir Dyspepsia November 19, 2014 746 1256 25 10 Ledipasvir Depressed mood November 30, 2015 1743 1570 32 10 Telaprevir Neutropenia May 15, 2015 65 265 276 13 Peginterferon alpha-2b Leukopenia December 16, 2014 36 123 331 10 Dasabuvir Pruritus March 16, 2015 35 34 247 4 F o r R e v i e w O n l y increased Ritonavir Pruritus June 15, 2015 162 218 271 12 Peginterferon alpha-2a Decreased appetite July 15, 2014 335 1443 67 32 Daclatasvir Varices oesophageal May 13, 2014 276 466 10 7 Simeprevir Hyperbilirubinaemia June 24, 2014 143 218 94 11 Ritonavir Hyperbilirubinaemia March 12, 2015 44 38 217 4 Ledipasvir Headache January 13, 2015 82 33 448 6 Simeprevir Pruritus March 3, 2014 14 31 31 5 Peginterferon alpha-2a Cough June 27, 2014 328 1373 32 20 Ribavirine Anaemia August 18, 2014 1064 3617 115 96 Ribavirine Neutropenia July 8, 2014 978 3120 195 171 4418 C † 11,085 1.19 [0.10-2.29] IC ‡ 7063 0.82 [0.33-1.31] 5858 0.58 [0.13-1.03] 3271 2.52 [1.92-3.12] 3159 2.11 [0.42-3.81] 1315 1.85 [1.55-2.15] 2701 1.84 [1.32-2.36] 4539 1.82 [0.28-3.37] 3751 1.78 [0.61-2.95] 3751 1.71 [0.67-2.74] 1909 1.67 [0.51-2.83] 3438 1.16 [0.84-1.48] 3813 1.13 [0.29-1.97] 10,195 1.08 [0.34-1.81] 8055 1.07 [0.14-2.00] 12,088 0.97 [0.00-1.93] 10,627 0.94 [0.13-1.75] 8548 0.93 [0.01-1.85] 9891 0.92 [0.28-1.56] 10,938 0.82 [0.26-1.37] 4816 0.80 [0.32-1.28] 2505 0.76 [0.13-1.40] 3980 0.76 [0.35-1.17] 9732 0.74 [0.13-1.35] 8870 0.72 [0.48-0.97] 987 0.69 [0.30-1.08] 4122 0.68 [0.05-1.31] 5763 0.58 [0.29-0.86] 0.57 [0.31-0.83] Daclatasvir Oedema peripheral November 15, 2014 1454 3487 57 39 7968 0.56 [0.06-1.06] Ribavirine Dyspnoea November 22, 2014 1306 4369 103 76 8105 0.51 [0.18-0.84] Ribavirine Leukopenia July 10, 2014 987 3153 238 202 4512 0.51 [0.29-0.73] F o r R e v i e w Ledipasvir Asthenia January 21, 2015 168 61 783 11 9065 0.45 [0.25-0.66] Ribavirine Cough February 15, 2016 2536 6211 107 73 12,499 0.44 [0.01-0.88] O n l y Daclatasvir Arthralgia July 15, 2014 848 1722 49 29 4816 0.43 [0.04-0.81]	ADR reported in the No Yes Yes reference set Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes
	Ombitasvir Peginterferon (no more Paritaprevir precision) Ritonavir	Pruritus Pruritus Leukopenia Asthenia	June 15, 2015 June 15, 2015 August 4, 2014 May 29, 2015	159 159 20 145	212 212 51 168	271 12 271 12 261 7 962 24	10,938 0.91 [0.35-1.47] 10,938 0.91 [0.35-1.47] 5282 1.55 [0.57-2.54] 10,728 0.66 [0.35-0.97]	Yes Yes Yes Yes
	Peginterferon alpha-2a Leukopenia Ledipasvir Vertigo	March 25, 2014 April 12, 2015	221 814	747 580	103 74 93 11	1429 10,164 0.66 [0.08-1.24] 1.46 [1.18-1.73]	Yes No
	Simeprevir	Rash	March 31, 2015	910	1612 67	19	10,046 0.88 [0.19-1.58]	Yes
	Boceprevir Ribavirine	Neutropenia Dyspnoea exertional	October 15, 2014 October 22, 2014	25 1239	64 4169 65 236 6 54	7275 7433	1.45 [0.29-2.61] 0.65 [0.25-1.04]	Yes Yes
	Peginterferon alpha-2a Weight decreased	October 15, 2015	415	1790 52	15	11,834 0.87 [0.05-1.69]
	Ribavirine	Influenza like illness	October 3, 2014	1189	4009 64	55	6878
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Table A2 -

 A2 2 AE-drug positive associations with HCV therapy at the SOC level, in decreasing order of IC.If you have answered YES to any of the above questions, or if you have additional personal, commercial or academic conflicts of interest, please draft a statement to publish with the article. e.g., AB has been reimbursed by Safe Drug Ltd. for international conference attendance.

		6. In the past three years I have:
			been paid as a consultant (or in a similar capacity) by a company with a vested interest in the
			product being studied, on issues related to the product being studied;	Patients No	reported ADR
	Drug		Event (SOC) been paid as a consultant (or in a similar capacity by a company with a vested interest in the Date of detection treated c i †	c j †	c ij †	C †	IC ‡	in the
			with drug i † product being studies, on issues unrelated to the product being studied; No	reference set
	Asunaprevir  received research or educational support from a company with a vested interest in the product(s) Cardiac disorders August 8, 2014 13 64	36	4	5404	1.78 [0.37-3.19] No
			being studied.							No
	Boceprevir	Blood and lymphatic system disorders		March 15, 2014	23	62	279	24	1230	1.68 [1.01-2.36] Yes
	Peginterferon (no more precision) 7. A company whose product is being studied has provided funding to support the work on this Blood and lymphatic system disorders May 27, 2014 17 27 project. Yes	578	13	3022	1.46 [0.78-2.15] Yes
	Peginterferon alpha-2a	Endocrine disorders			October 15, 2014	378	1635 8	7	7275	1.36 [0.10-2.63] Yes
	Peginterferon alpha-2a	Blood and lymphatic system disorders F o F o F o F o		March 5, 2014	185	575	244	166 1029	1.34 [1.18-1.50] Yes
	Peginterferon alpha-2b	Surgical and medical procedures September 10, 2014 34 r r r r	116	73	5	6224	1.31 [0.07-2.55] No
	Simeprevir Peginterferon alpha 2b	Injury poisoning and procedural complications R R R R e e e e Blood and lymphatic system disorders v i v i v i v i		July 11, 2014 February 7, 2014	225 20	314 61	26 156	6 25	4539 644	1.16 [0.56-1.76] No 1.04 [0.48-1.60] Yes
	Dasabuvir	Hepatobiliary disorders	e e e e	April 14, 2015	77	67	294	6	10,195 0.97 [0.34-1.60] Yes
	Telaprevir	Skin and subcutaneous tissue disorders w November 29, 2013 43 w w w	130	31	18	443	0.80 [0.26-1.33] Yes
	Simeprevir Ombitasvir	Skin and subcutaneous tissue disorders Hepatobiliary disorders	May 15, 2014 April 7, 2015 O n O n O n O n	43 79	115 69	171 291	15 6	2701 10,107 0.79 [0.24-1.33] Yes 0.79 [0.56-1.02] Yes
									l l l l
									y y y y
					2	1	2		2	22	2.26
			22	0	1	1	2		2	23	8.96
			23	1	1	1	2		2	22	2.35
			24	1	2	0	1		2	22	1.39
			25	1	2	1	1		2	22	2.35
			26	0	1	0	1		2	23	8.13
			27	0	1	1	1		2	23	8.96
			28	1	1	0	1		2	22	1.39
			29	1	1	1	1		2	22	2.35
			30	2	4	2	4		2	22	3.61
		†Baseline
										September 2016

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		product being studied, on issues related to the product being studied; product being studied, on issues related to the product being studied;	No No
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		project. project.	Yes Yes
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  • received research or educational support from a company with a vested interest in the product(s) being studied. Yes 7. A company whose product is being studied has provided funding to support the work on this project. Yes If you have answered YES to any of the above questions, or if you have additional personal, commercial or academic conflicts of interest, please draft a statement to publish with the article. e.g., AB has been reimbursed by Safe Drug Ltd. for international conference attendance. SP received honoraria for his participation in advisory boards from Abbvie, Janssen, MSD, BMS, Gilead and Novartis 8. Manuscript title (first six words are sufficient)

  • received research or educational support from a company with a vested interest in the product(s) being studied. No 7. A company whose product is being studied has provided funding to support the work on this project. Yes If you have answered YES to any of the above questions, or if you have additional personal, commercial or academic conflicts of interest, please draft a statement to publish with the article. e.g., AB has been reimbursed by Safe Drug Ltd. for international conference attendance.

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ACKNOWLEDGEMENTS Role of the funding source

The ANRS CO22 HEPATHER cohort is sponsored and funded by Inserm-ANRS and conducted in collaboration with Association Française pour l'étude du Foie (AFEF). The cohort received supports from ANR (Agence Nationale de la Recherche), DGS (Direction Générale de la Santé) and MSD, Janssen, Gilead, Abbvie, BMS, Roche. The public/private partnership is built in total transparency through a specific contract. The pharmaceutical companies are not involved in scientific decisions.

The biobank of the cohort is stored by Cell&Co Biorepository, Pont du Château, France and has been managed temporarily by Centre de Ressources Biologiques-HUEP, Hôpital St Antoine, Paris, France.

The Institut national de la santé et de la recherche médicale (INSERM)-ANRS had a role in study design, data collection and approval of the final report. The funders of the study other than INSERM-ANRS had no role in study design, data collection, data analysis, data interpretation, or writing of the report. FC had full access to all the data in the study and had final responsibility for the decision to submit for publication.

We thank Amber Kunkel, Center for Global Health, Institut Pasteur in Paris, for her help in editing the manuscript.

ANRS-AFEF Hepather Study group

Laurent Alric, Delphine Bonnet, Virginie Sicart (CHU Purpan, Toulouse, France), Fabien Zoulim, François Bailly, Marjolaine Beaudoin, Dominique Giboz, Kerstin Hartig-Lavie, Marianne Maynard (Hospices Civils de Lyon, Lyon, France), Eric Billaud, Morane Cavellec, Marjorie Cheraud-Carpentier, Jérome Gournay, François Raffi, Florian Vivrel (Hôpital Hôtel-Dieu, Nantes, France), Isabelle Hubert, Jaouad Benhida, Jérôme Boursier, Paul Calès, Françoise Lunel, Frédéric Oberti (CHU Angers, Angers, France), Patrick Marcellin, Nathalie Boyer, Audrey Gilibert, Nathalie Giuily (Hôpital Beaujon, Clichy, France), Giovanna Scoazec (Hôpital Beaujon, Clichy, France and Hôpital Henri Mondor, Créteil, France), Stanislas Pol, Hélène Fontaine, Sandrine Fernandes, Sylvie Keser, Philippe Sultanik, Anaïs

CONFLICT OF INTEREST

SP received honoraria for his participation in advisory boards from Abbvie, Janssen, MSD, BMS, Gilead and Novartis. SFF, NL, CD, AD, IA and FC declare that they have no conflicts of interest. APPENDIX 3:

We explored the impact of modifying the prior coefficients on the total number of detected positive signals. For drug-AE pairs with a positive signal at both baseline and for other choices of coefficients, we calculated the mean difference of detection times between the scenarios (in days). A negative mean difference indicated an earlier positive signal in the sensitivity analysis than with our baseline non-informative priors.

At the PT level of analysis, 45 of 52 positive associations detected with our baseline choice for prior coefficients were also detected using other coefficient combinations. The highest absolute mean difference between detection times was 35.54 days. At the SOC level, 22 of 26 positive associations detected with our baseline choice for prior coefficients were also detected using all 30 combinations, and the highest absolute mean difference was 22.12 days (table A3-1 & 2). Overall this sensitivity analysis shows that our results were slightly affected by different choices for the prior coefficients α 1 , α, β 1 and β. However the number of positive associations were sensitive to the prior coefficient γ 11 , related to the prior probability of a specific drug-AE association.

CONFLICT OF INTEREST DISCLOSURE

The Editors of Pharmacoepidemiology and Drug Safety recognize that most studies in pharmacoepidemiology cost money and thus pose a potential conflict of interest. As a conflict of interest may affect the assessment or judgment of an author, we ask that all authors (not just the Corresponding Author) complete the following form.

For Co-authors: Please complete questions 4-10. Completed forms should be saved, and emailed as an attachment to the Corresponding Author.

CONFLICT OF INTEREST DISCLOSURE

The Editors of Pharmacoepidemiology and Drug Safety recognize that most studies in pharmacoepidemiology cost money and thus pose a potential conflict of interest. As a conflict of interest may affect the assessment or judgment of an author, we ask that all authors (not just the Corresponding Author) complete the following form.

For Co-authors: Please complete questions 4-10. Completed forms should be saved, and emailed as an attachment to the Corresponding Author.

CONFLICT OF INTEREST DISCLOSURE

The Editors of Pharmacoepidemiology and Drug Safety recognize that most studies in pharmacoepidemiology cost money and thus pose a potential conflict of interest. As a conflict of interest may affect the assessment or judgment of an author, we ask that all authors (not just the Corresponding Author) complete the following form.

For Co-authors: Please complete questions 4-10. Completed forms should be saved, and emailed as an attachment to the Corresponding Author.

CONFLICT OF INTEREST DISCLOSURE

The Editors of Pharmacoepidemiology and Drug Safety recognize that most studies in pharmacoepidemiology cost money and thus pose a potential conflict of interest. As a conflict of interest may affect the assessment or judgment of an author, we ask that all authors (not just the Corresponding Author) complete the following form.

For Co-authors: Please complete questions 4-10. Completed forms should be saved, and emailed as an attachment to the Corresponding Author.

CONFLICT OF INTEREST DISCLOSURE

The Editors of Pharmacoepidemiology and Drug Safety recognize that most studies in pharmacoepidemiology cost money and thus pose a potential conflict of interest. As a conflict of interest may affect the assessment or judgment of an author, we ask that all authors (not just the Corresponding Author) complete the following form.

For Co-authors: Please complete questions 4-10. Completed forms should be saved, and emailed as an attachment to the Corresponding Author.

CONFLICT OF INTEREST DISCLOSURE

The Editors of Pharmacoepidemiology and Drug Safety recognize that most studies in pharmacoepidemiology cost money and thus pose a potential conflict of interest. As a conflict of interest may affect the assessment or judgment of an author, we ask that all authors (not just the Corresponding Author) complete the following form.

For Co-authors: Please complete questions 4-10. Completed forms should be saved, and emailed as an attachment to the Corresponding Author.

CONFLICT OF INTEREST DISCLOSURE

The Editors of Pharmacoepidemiology and Drug Safety recognize that most studies in pharmacoepidemiology cost money and thus pose a potential conflict of interest. As a conflict of interest may affect the assessment or judgment of an author, we ask that all authors (not just the Corresponding Author) complete the following form.

For Co-authors: Please complete questions 4-10. Completed forms should be saved, and emailed as an attachment to the Corresponding Author.