# Incomplete reporting of adverse events in duloxetine trials: a meta-research survey of randomized controlled trials versus placebo

P Rolland, A Jutel, K Douget, Florian Naudet, Jean-Charles Roy

► To cite this version:

P Rolland, A Jutel, K Douget, Florian Naudet, Jean-Charles Roy. Incomplete reporting of adverse events in duloxetine trials: a meta-research survey of randomized controlled trials versus placebo. Journal of Clinical Epidemiology, 2025, 180, pp.111677-111677. 10.1016/j.jclinepi.2025.111677. hal-04923740

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# ORIGINAL RESEARCH

## Incomplete reporting of adverse events in duloxetine trials: a meta-research survey of randomized controlled trials vs placebo

P. Rolland<sup>a,b</sup>, A. Jutel<sup>b</sup>, Le Douget<sup>b</sup>, F. Naudet<sup>a,b,c,d,l</sup>, J.C. Roy<sup>a,b,e,l,*</sup>

Accepted 9 January 2025; Published online 16 January 2025

## Abstract

**Background and Objectives:** Relying on published data alone might be insufficient for meta-analyses to be reliable and trustworthy since selective outcome reporting is common, especially for adverse events (AEs). We investigated the existence of selective reporting and its potential for bias in a case study exploring AEs of duloxetine in adults.

**Study Design and Setting:** We systematically searched all previous meta-analyses/pooled analyses on duloxetine published on PubMed for seven indications approved by the American and European health authorities. We included all randomized controlled trials (RCTs) vs placebo. For each RCT, we extracted the number of serious adverse events (SAEs), AEs, drop-outs (DOs) and drop-outs for safety reasons (DOSRs) using four information sources: published articles, clinical study registries, clinical study reports and data available in meta-analyses/pooled analyses. To assess the range of differences resulting from these four extraction strategies, we performed 4 meta-analyses using random effect models as well as a complete meta-analysis combining all sources.

**Results:** A total of 70 RCTs (including 24,330 patients) were included. Of those, SAEs were identified for 42 studies (61%) in published articles, 58 (84%) in study reports (8 study reports were not retrieved), 24 (34.7%) in registries, and 21 (30.4%) in meta-analyses/pooled analyses. For 2 (2.9%), 2 (2.9%), 2 (2.9%) and 1 (1.4%) studies, we found respectively no data on SAEs, AEs, DOs, and DOSRs in any sources. Discrepant results across sources were found in 24 (34.5%), 20 (28.5%), 13 (18.6%), and 9 (12.8%) studies, respectively for SAEs, AEs, DOs, and DOSRs. Despite variations in point estimates and their 95% confidence intervals, we did not find different results in the conclusions of meta-analyses depending on the different information sources used, except for DOs, for which no effect was found using results published in registries, in contrast to other information sources.

**Conclusion:** None of the four information sources provided complete retrieval of safety results for duloxetine in adults across various indications. However, we did not find strong evidence that this underreporting leads to different conclusions in meta-analyses. Nonetheless, this finding remains uncertain, as we were unable to obtain complete information for all studies despite extensive searches. © 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

**Keywords:** Meta-analysis; Duloxetine; Adverse events; Vibration of effects; Heterogeneity; Clinical trials

## 1. Introduction

Systematic reviews and meta-analyses are considered the most reliable form of evidence, informing decision-makers and clinicians [1], and significantly influencing evidence-based medicine [2]. These studies are expected to provide exhaustive, quantitative and reproducible syntheses of primary evidence. However, there is growing concern

<footnote><sup>a</sup>Adult Psychiatry Department, Rennes University Hospital, Rennes, France</footnote>

<footnote><sup>b</sup>Université de Rennes, University Rennes, CHU Rennes, Rennes, France</footnote>

<footnote><sup>c</sup>Centre d'investigation clinique de Rennes 1414, Service de pharmacologie clinique, Institut de recherche en santé, environnement et travail (IRSET), UMR S 1085, EHESP, Rennes, France</footnote>

<footnote><sup>d</sup>Institut Universitaire de France, Paris, France</footnote>

<footnote><sup>e</sup>Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany</footnote>

<footnote>Funding: This work was supported by a grant from the French Ministry of Health (National Hospital Clinical Research Program, PHRC-21-0074) to support data storage and management.</footnote>

<footnote n="1">¹ Both authors contributed equally to this work.</footnote>

<footnote>* Corresponding author: Pôle Hospitalo-Universitaire de Psychiatrie Adulte, CHU Pontchaillou 2, rue Henri Le Guilloux Cedex 9, Rennes 35033, France.</footnote>

<footnote>E-mail address: jean-charles.roy@charite.de (J.C. Roy).</footnote>
**What is new?**

**Key findings**

* Inconsistent and incomplete reporting of adverse events for duloxetine exists across different data sources (published articles, registries, study reports, etc).

**What this adds to what was known?**

* This study informs that publication and reporting bias, well-documented for efficacy outcomes, also impacts safety reporting for duloxetine.

**What is the implication and what should change now?**

* The fragmentation of information about safety issues in duloxetine complexifies the decision-making process for end users interested in gauging the balance between benefits and harms. Harm reporting should be made more accessible and harmonized across data sources.

that they are failing in several ways. The literature is now flooded with almost as many meta-analyses as primary studies. These meta-analyses are sometimes contradictory and show reproducibility issues [3]. Effect estimates can vary across distinct meta-analyses, depending on researchers' choices among justifiable methods or measurement. This phenomenon, known as the «vibration of effects» (VoE), has been observed in various meta-analyses across different fields [4–6] and could be even more important when investigating safety issues [6,7]. Additional VoE may arise from lack of rigor in study selection and outcomes extraction [8], leading to publication or reporting bias [2,9–11].

We explored this issue in a case study on duloxetine, an antidepressant approved by the European Medicines Agency (EMA) and the United States Food and Drug Administration for a wide range of indications, including major depressive disorder, general anxiety disorder (GAD), chronic musculoskeletal pain, diabetic peripheral neuropathic pain (DNP), fibromyalgia (FMG), and stress urinary incontinence (SUI). The literature on antidepressants is well-known for issues related to publication bias [12–14] and the proliferation of meta-analyses, many of which involve industry support and rarely report limitations [15]. Specifically, the literature on duloxetine is known for overlapping and redundant evidence syntheses, especially for major depression, with at least 43 published pooled analyses on the topic [16].

We, therefore, designed the present study to assess the quality of adverse event (AE) reporting in randomized placebo-controlled trials of duloxetine, the consistency of this reporting across different information sources

(published articles, clinical study registers, clinical study reports and data available in meta-analyses/pooled analyses), and its impact in terms of VoE.

**2. Methods**

## 2.1. Protocol, registration, and reporting

This ancillary study is based on systematic searches conducted as part of an ongoing individual patient data (IPD) meta-analysis [17], registered on PROSPERO (CRD42019130488). Before data extraction, our protocol was registered on the Open Science Framework (https://osf.io/5efc9; May 2, 2020). Changes to the initial protocol are noted in the Supplementary Methods. Reporting followed the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement [18].

## 2.2. Eligibility criteria

We included randomized controlled trial (RCT), double-blind controlled trial, and placebo-controlled trial evaluating duloxetine in adults diagnosed with MDD, GAD, chronic musculo-skeletal pain, DNP, FMG, or SUI.

## 2.3. Search strategy

We relied on trials identified during preliminary searches conducted in 2019 for our IPD meta-analysis [17], with an update in 2023 to capture all relevant studies. Given the overwhelming number of evidence syntheses on this topic, we did not search directly for RCTs. Instead, we employed a two-step strategy, similar to an umbrella review [19]. First, we searched for all existing meta-analyses/pooled analyses of duloxetine use in adults for all approved indications. Meta-analyses and pooled analyses were identified in PubMed using the following search terms: “Duloxetine AND Meta-Analysis [ptyp]” from inception to May 31, 2022. Second, we extracted relevant RCTs from the selected meta-analyses and pooled analyses.

## 2.4. Study selection

Three independent reviewers (AJ, KL, and PR) conducted the searches in a blinded manner, with a fourth reviewer (JCR) arbitrating in case of disagreement. For each included study we reported the author's name, study title, sponsor's name and ID, ClinicalTrial.gov number, end of study year, publication year, study country and duloxetine dose. For each study, we extracted serious adverse events (SAE), AEs, drop-outs (DOs), and drop-outs for safety reason (DOSR) from various sources: published articles, clinical study registers, clinical study reports and data available in meta-analyses or pooled analyses. For published studies, meta-analyses and pooled analyses, we inspected both the main manuscript and supplementary materials.
For study registries, we searched [ClinicalTrial.gov](http://ClinicalTrial.gov) and [Clinicaltrialregistries](http://Clinicaltrialregistries) website using NCT and EudraCT identification numbers. FDA drug approval packages were downloaded using “duloxetine” as search term. European Medicines Agency (EMA) assessment reports and with- drawn applications were selected by selecting human re- ports with no restriction on authorization status and using “duloxetine” as search term on the EMA website. For clin- ical study reports, we searched for synopses on industry websites selecting duloxetine as product, contacted the sponsor, and retrieved them from the Vivli data sharing platform.

## 2.5. Data extraction

For each trial, we extracted data on SAEs, non-SAEs, DOs and DOSRs for both the placebo and duloxetine groups. Data extraction from published articles used infor- mation from tables, figures and text. For meta-analyses and systematic reviews, we extracted data from figures, ta- bles, text, and supplementary materials. For clinical trial registries ([ClinicalTrial.gov](http://ClinicalTrial.gov), [clinicaltrialregisters.eu](http://clinicaltrialregisters.eu)), we extracted raw data reported in tables. Data from the FDA and EMA websites were obtained from text and ta- bles in approval packages and European public assessment reports. For clinical study reports, data were extracted from text, figures, and tables available on industry websites.

## 2.6. Outcomes

The primary outcome was the proportion of individuals with a SAE. Secondary outcomes were reporting of non- SAEs, DOs, and DOSRs using definitions endorsed by reg- ulatory agencies [20] (Glossary).

## 2.7. Quality assessment

Three researchers (JCR, AJ, and PR) independently as- sessed each trial for risk of bias, considering randomiza- tion, allocation concealment, blinding of assessors and participants, completeness of outcome assessment, selec- tive reporting and other potential sources of bias, following the Cochrane Collaboration's risk of bias tool (RoB2) [21], at the study level. Discrepancies were resolved through consensus. The risk of bias for each study is reported in the Supplementary Materials (Table S2).

## 2.8. Synthesis of results

First, a descriptive analysis was performed by computing the number and percentage of reported, unre- ported and unpublished data across the information sources (published articles, clinical study registers, clinical study reports and data available in meta-analyses/pooled ana- lyses), as well as identifying inconsistencies between these sources. If only partial information was available, such as

the most frequent AE or the total number of SAEs without specifying the number of individuals, data were considered unreported for that particular source.

To assess whether underreporting could contribute to a VoE, meta-analyses were conducted separately for each outcome and data source. These meta-analyses were con- trasted to a comprehensive meta-analysis using all available data and, in cases of inconsistencies, selected the data source reporting the largest proportion of individuals with an AE in the duloxetine group. All meta-analyses used a random-effect model, with the Peto odds ratio method [22] for SAEs, which performs reasonably when events are rare and intervention effects are low [23], and the Mantel-Haenszel method for all AEs. We used both methods for the SAEs, with risk differences for the Mantel-Haenszel method, as justified in the **Supplemen- tary Methods**. The proportion of total variability due to between-study heterogeneity was measured with the I² in- dex [24]. We considered that if studies explicitly reported the absence of events, they were counted as zero events. When events were not reported, the information was considered missing, and we did not impute the number of events as zero.

To examine the influence of medical conditions on AEs, meta-regressions were conducted by selecting the maximum number of AEs from each data source. Subgroup analyses were performed for each type of AE using a mixed-effect model with the Peto OR method for SAEs, and the Mantel-Haenszel method for all AEs. The omnibus QM-test [25] was applied to assess the moderating effect of medical conditions on the risk of AEs under duloxetine. All analyses were performed using R software (version 3.0; R core team, 2017), specifically the {Meta} package [26]. A two-sided significance threshold of *P* < .05 was applied to all statistical analyses.

# 3. Results

## 3.1. Literature searches

Our PubMed search identified 48 different meta-analysis (Supplementary Materials 1) including 109 unique RCTs (Figure 1). Of these, 40 were excluded due to incorrect pop- ulation or design, leaving 70 RCTs for inclusion (Supplementary Materials 2), involving a total of 24,330 patients.

## 3.2. Reporting of safety outcomes

The results of the descriptive analysis are summarized in Table S1 and displayed in Figure 2. Among the 70 included studies, 13 (18.6%) were unpublished, 25 (35.7%) were not found in trial registries, 15 (21.4%) were not reported in meta-analyses or pooled analyses, and 8 (11.4%) study re- ports could not be obtained. For 2 (2.9%), 2 (2.9%), 2 (2.9%), and 1 (1.4%) study, we were unable to retrieve
<figure><img src="image_2.png" /><figcaption>**Figure 1.** PRISMA flow Diagram of included studies. PRISMA, Preferred Reporting Items for Systematic reviews and Meta-Analyses.</figcaption></figure>

SAE, AE, DO, and DOSR, respectively, from any source, despite contacted authors or sponsors representatives. This affected the following five studies: alexmed116619163, F1J-MC-HMAH, F1J-MC-SAAL, F1J-MC-SBAV, and M06-850.

SAEs were identified in 43 studies (61.4%) from published articles (13 were unpublished and 13 lacked complete SAE data), 58 studies (82.9%) from study reports (3 did not report SAEs), 24 studies (34.3%) from registries (20 did not provide this information), and 21 studies (30.0%) from meta-analyses/pooled analyses. SAE data were consistent across sources in 26 studies (37.1%), inconsistent in 20 (28.6%), and either partially or not reported in 24 studies (34.3%).

AEs were found in 42 studies (60.0%) from published articles (14 did not report AEs), 58 studies (82.3%) from study reports (3 did not provide data), 26 studies (37.1%) from registries (18 did not provide data), and 33 studies (47.1%) from meta-analyses/pooled analyses. AE data were

consistent across sources in 16 studies (22.8%), inconsistent in 34 (48.6%), and either partially or not reported in 20 studies (28.6%).

DOs were identified in 53 studies (75.7%) from published articles (3 did not report DOs), 59 studies (84.3%) from study reports (2 did not provide data), 43 studies (61.4%) from registries (1 did not provide data), and 20 studies (28.6%) from meta-analyses/pooled analyses. DO data were consistent across sources in 27 studies (38.6%), inconsistent in 30 (42.9%), and either partially or not reported in 13 studies (18.6%).

DOSRs were found in 56 studies (80.0%) from published articles (all included DOSRs), 60 studies (85.7%) from study reports (1 did not provide data), 40 studies (57.1%) from registries (5 did not provide data), and 40 studies (57.1%) from meta-analyses/pooled analyses. DOSR data were consistent across sources in 38 studies (54.2%), inconsistent in 23 (32.8%), and either partially or not reported in nine studies (12.9%).
A

<figure><img src="image_3.png" /></figure>

Serious Adverse Events

B

<figure><img src="image_4.png" /></figure>

Adverse Events

C

<figure><img src="image_5.png" /></figure>

Drop Out

D

<figure><img src="image_6.png" /></figure>

Drop out for Safety Reasons

<figure><img src="image_7.png" /></figure>

**Figure 2.** Reporting and consistency of safety results across information sources for serious adverse events (A), adverse events (B), drop outs (C) and drop outs for safety reasons (D). The plots present the accessibility of the reports for each study and each information source, namely, publications, clinical study reports, published pooled analysis and study registries of authorities. The percentage of reported events (green), unreported or partially reported events in study documents (red) or of missing documents (gray), are reported. The consistency or inconsistency in the number of undesirable events between the data sources is displayed in yellow or blue, respectively. Legend: CLBP, Chronic Low Back Pain; DNP, Diabetic Neuropathic Pain; FMG, Fibromyalgia; GAD, General Anxiety Disorder; MDD, Major Depressive Disorder; OAP, osteoarthritis pain; SUI, Stress urinary incontinence.

### 3.3. Exploration of VoE across information sources

Summary measures of all meta-analyses examining VoE across the different information sources are presented in Figure 3. Forest plots and I² are presented in Supplementary Figures S1–S10 for all meta-analyses.

Despite variations in points estimates and their 95% prediction intervals (PIs), we did not find divergent results in the conclusions across the different information sources used for SAEs with the Peto or MH methods. The summary measures were the highest for study registries (Peto Method: OR = 1.32 [95% PI 0.48; 3.64], MH Method: RD = 0.002 [95% PI −0.009; 0.014]), and the lowest for pooled analyses (Peto Method; OR = 1.32 [95% PI 0.48; 3.64], MH Method: RD = 0.03 [95% PI −0.005; 0.011]).

These results were consistent with the summary measures found in the most comprehensive meta-analysis (48 studies, 23,174 patients). Consistent results indicating increased risk were observed across all sources for other outcomes, except for DOs, where no effect was found using results posted on registries, in contrast to the increased risk identified from other information sources. All meta-analyses showed significant heterogeneity.

### 3.4. Meta-regression on medical conditions

Results of the meta-regression exploring medical conditions as a potential source of heterogeneity are shown in Figure 4. We found no significant heterogeneity in the
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>**Figure 3.** Vibration of effects across the different information sources. Forest plots detailing summary measures of meta-analyses of safety outcomes for each data source separately (published articles, pooled analyses, study reports, study registries) and for a meta-analysis relying on all possible data sources. Meta-analyses compare duloxetine vs placebo. Odds ratio and risk differences are represented with their 95% confidence interval. Legend: # studies, number of nonzero weighted studies included in the meta-analysis; MH method, Mantel-Haenszel method; OR, odds-ratio; PI-95%, 95% prediction interval; RD, risk difference.</figcaption></figure>

OR for SAEs between medical conditions ($Q_M = 4.26$, $P = .64$, $R^2 = 1.39$%), indicating no increased risk for any specific condition. Similarly, no significant subgroup effect was found for AEs ($Q_M = 23.3$, $P = .0007$, $R^2 = 43.50$%). In contrast, significant heterogeneity in

the ORs was observed across medical conditions for DOs ($Q_M = 28.06$, $P < .0001$, $R^2 = 29.37$%) and DOSRs ($Q_M = 26.57$, $P = .0002$, $R^2 = 51.40$%), suggesting that the risk of DOs and DOSRs under duloxetine varies by medical conditions.

<figure><img src="image_13.png" /><figcaption>**Figure 4.** Meta-regression on medical condition. The effects of duloxetine on the number of adverse events for each medical condition, represented by their odds ratios and 95% confidence interval. Legend: CLBP, Chronic Low Back Pain; DNP, Diabetic Neuropathic Pain; FMG, Fibromyalgia; GAD, General Anxiety Disorder; MDD, Major Depressive Disorder; OAP, osteoarthritis pain; SUI, Stress urinary incontinence.</figcaption></figure>
# 4. Discussion

## 4.1. Summary of the main findings

None of the four information sources provided complete retrieval of all safety outcomes for RCTs of duloxetine in adults across various indications. Notably, SAEs and AEs were partially or not reported in approximately 40% of the published studies, compared to 20% for DOs and DOSRs. Inconsistencies between information sources ranged from 20% to 40% depending on the safety outcome analyzed, adding potential VoE. In our case study, we did not find strong evidence that such underreporting and inconsistencies result in substantial VoE or bias in meta-analyses: all sources consistently concluded that duloxetine significantly increases AEs and DOSRs but not SAE. While no evidence of bias was observed regarding conclusions, often inconclusive for rare events like SAEs, differences in point estimates suggest pooled analyses may underestimate harmful effects. Conversely, relying solely on results from study registries might miss an increase in DOs reported by other sources. Meta-regression identified heterogeneity in effects across medical conditions, with increased DOs and DOSRs associated with duloxetine in SUI, and higher DOSRs in CLBP.

## 4.2. Strengths and weaknesses of the study

Although we tried to make our searches as comprehensive as possible by comparing various sources of evidence, we were unable to obtain complete information for all studies. Data were missing for at least one AE in five studies, representing 1289 patients. We cannot exclude the possibility that the studies not reporting data for these outcomes were associated with unfavorable treatment results, as previously reported [13,27]. Therefore, it is possible that these missing data, even if few, may have influenced the meta-analysis conclusions.

Additionally, we focused on safety outcomes, such as SAEs or AEs, which are composite outcomes encompassing a wide range of AEs. We chose these outcomes because they were easier to collect and more widely reported than specific AEs. Targeting more specific AEs (eg, suicidal or cardiovascular risk) might have produced different results in identifying safety issues across various sources.

We also included a wide range of diseases, assuming that AEs would be similar across conditions. Our meta-analyses revealed significant heterogeneity, partly explained by conditions. For example, this heterogeneity translates into a higher risk of DOs with duloxetine in stress-urinary incontinence and osteoarthritis pain, not found in other conditions. Both the use of composite outcomes and the focus on safety issues may have blurred the line and limited identification of the VoE due to data sources. Future studies should focus on specific AEs in specific conditions to address these shortcomings.

Furthermore, while this case study provides valuable insights for duloxetine, caution is advised when generalizing these findings to other medications with different safety profiles or used in different clinical contexts.

Variability in treatment duration can significantly influence AE reporting and impact the estimates derived from meta-analyses using aggregated data. While it may be reasonable to disregard time-related information in certain cases [28], doing so poses a risk of drawing incorrect conclusions when treatment duration varies substantially between groups. While we provided the maximum treatment duration for each study in Supplementary Materials, the lack of participant-level data limited our ability to fully account for these differences. This limitation is particularly relevant for patients lost to follow-up, common in antidepressant trials [29]. Aggregated data, however, often fail to capture exact exposure duration, underscoring the value of time-to-event analyses using IPD for safety analysis.

The rarity of SAEs challenges meta-analyses, limiting power to detect signals: in our case study, the absence of an SAE signal may falsely reassure, highlighting the need for epidemiological data. In addition, the observed variability in heterogeneity between the Peto and Mantel-Haenszel methods in SAE underscores the nuanced influence of the statistical approach for rare-event analyses. Regarding VoE, we recommend that future studies prioritize larger and more comprehensive datasets for robust a priori power calculations.

## 4.3. Strengths and weaknesses in relation to other studies, discussion of important differences in results

Publication bias and reporting bias are well-known in antidepressant research regarding efficacy outcome [11,29]. Our study highlights that poor reporting affects important safety outcomes such as SAEs and AEs. Since the main focus of RCTs is on efficacy, safety outcomes may be overlooked or underreported as illustrated in case studies with quetiapine and gabapentin [30]. The underreporting of harms is prevalent in published clinical trials [31–33], as recently demonstrated with COVID-19 drugs [34]. Pooled analyses are known to mask negative efficacy results from individual trials [35]. While frequent for duloxetine [16], pooled analyses were among the worst sources for reporting harms in our study. When available, clinical study reports were the most complete information source, as described previously for antidepressants [36] and in other fields such as oncology [37]. However, study reports were difficult to retrieve, although the use of the Vivli data-sharing platform [38] significantly facilitated access. All in all, this fragmentation of information about safety issues complexifies the decision-making process for end-users interested in gauging the balance between benefits and harms [39] for a given drug, and makes it
difficult for meta-analyses to be exhaustive. It has been shown that the number of reviews with the potential for safety outcome reporting bias in at least one eligible study was high [40]. We also identified inconsistencies across data sources that could complicate the review process. Similar discrepancies were identified for esketamine in depression, where harm data in study publication were incomplete compared to registry data [41].

### 4.4. Meaning of the study: possible explanations and implications for clinicians and policymakers

Solutions to tackle the problem exist. First, the CONSORT guidelines have a specific extension for harm reporting for publications. Although developed in 2004, its enforcement by medical journals has not been as systematic as for the main CONSORT checklist, and compliance has been suboptimal [42]. The extension was updated in 2022 to improve harm reporting in published studies, and it is planned to be included in the main CONSORT checklist [43]. Hopefully, this will help improve compliance.

Beyond journal publication, posting results on registries is paramount, but compliance with these requirements still needs to improve [44,45]. Moreover, the process should be streamlined to avoid inconsistencies between information sources. For instance, clinical study reports should be routinely made available and linked to registry entries. One obstacle to the widespread and open sharing of study reports is that detailed descriptions of AEs in those reports often include a verbatim that could be considered indirectly identifying information. However, this does not apply for aggregated data which can be shared without restriction. In addition, access to the sensitive individual patient data can be planned and implemented even under the most comprehensive data protection mandates, such as the European General Data Protection Regulation [46].

## 5. Conclusion

Our study aimed to document the completeness and consistency of reporting of basic safety events for duloxetine across various information sources. Such gaps and fragmentation in the evidence base can make it more challenging to accurately and efficiently synthesize important information about the potential harms of this drug.

## Glossary

SAEs are defined as any AE that resulted in hospitalization (or prolongation of hospitalization) or any life-threatening risk including death or suicidal risk [20].

Non-SAEs are defined as any untoward medical occurrence in a subject to whom the treatment was administered

that is not necessarily causally related to the treatment in question and does not lead to hospitalization or any life-threatening risk [20].

Drop-outs are defined as the number of subjects who discontinued the trial before completion [20].

Drop-outs for safety reasons were defined as the number of patients who discontinued the trial because of the occurrence of an AE [20].

## CRediT authorship contribution statement

**P. Rolland:** Writing — review & editing, Investigation, Data curation. **A. Jutel:** Writing — original draft, Investigation, Data curation. **Le Douget:** Investigation, Data curation. **F. Naudet:** Writing — review & editing, Validation, Supervision, Resources, Methodology, Funding acquisition, Conceptualization. **J.C. Roy:** Writing — original draft, Visualization, Validation, Supervision, Software, Resources, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

Neither P.R., A.J., and K.D. have any relation with any pharmaceutical company nor did they receive any funding.

J.C.R. has no relation with any pharmaceutical company but received funding from the French ministry of research (National Hospital Clinical Research Program, PHRC-21-0074).

F.N. has no relation with any pharmaceutical company but received funding from the French National Research Agency (ANR-17-CE36-0010), the French ministry of health and the French ministry of research. He is a work package leader in the OSIRIS project (Open Science to Increase Reproducibility in Science). The OSIRIS project has received funding from the European Union's Horizon Europe research and innovation program under the grant agreement No. 101094725. He is a work package leader for the doctoral network MSCA-DN SHARE-CTD (HORIZON-MSCA-2022-DN-01 101,120,360), funded by the EU.

There are no competing interests for any other author.

## Supplementary data

Supplementary data to this article can be found online at
https://doi.org/10.1016/j.jclinepi.2025.111677.

## Data availability

The authors do not have permission to share data.
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