Dataset Information

Identifying Anticipated Events of Future Clinical Trials by Leveraging Data from the Placebo Arms of Completed Trials.

ABSTRACT:

Background

An important component of a systematic strategy for safety surveillance is prospective identification of anticipated serious adverse events (SAEs). Developing a structured approach to identify anticipated events and estimating their incidence can help align the safety strategy and the safety surveillance efforts.

Methods

We developed a novel approach to identify anticipated events for a hypothetical randomized, double-blind, controlled trial in subjects with bipolar disorder using the adverse events reported in the placebo arm of trials from the ClinicalTrials.gov database. We searched the ClinicalTrials.gov database for all trials on bipolar depression with similar inclusion/exclusion criteria and study duration as our hypothetical study. The frequencies of anticipated events in placebo arms were abstracted from each trial and 95% confidence intervals (CI) were calculated using the Clopper-Pearson method. Meta-analysis with a random effects model was performed to obtain a summary estimate and 95% CI for the events identified in more than one trial.

Results

A total of 129 clinical trials were initially identified, and 18 were ultimately selected as they met all the selection criteria. There were 69 unique anticipated SAEs identified, and 13 out of 69 were reported in at least 2 clinical trials. The top 5 anticipated SAEs for our study were: (1) hospitalization, psychiatric symptom (3.57%); (2) suicidal behavior, overdose (3.57%), (3) cholecystitis (2.86%); (4) fall (2.86%); (5) road traffic accident, injury (2.86%).

Conclusion

We successfully identified the anticipated events from registered trials that included a population similar to our trial. This method for identifying anticipated events could be applied to other disease areas.

SUBMITTER: Tan XL

PROVIDER: S-EPMC7864837 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

Identifying Anticipated Events of Future Clinical Trials by Leveraging Data from the Placebo Arms of Completed Trials.

Tan Xiang-Lin XL Kern David M DM Cepeda M Soledad MS

Therapeutic innovation & regulatory science 20201109 2

<h4>Background</h4>An important component of a systematic strategy for safety surveillance is prospective identification of anticipated serious adverse events (SAEs). Developing a structured approach to identify anticipated events and estimating their incidence can help align the safety strategy and the safety surveillance efforts.<h4>Methods</h4>We developed a novel approach to identify anticipated events for a hypothetical randomized, double-blind, controlled trial in subjects with bipolar dis ...[more]

PMID: 33165761

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Project description:Background:There is debate in the literature as to whether inclusion of a placebo arm may alter characteristics of antidepressant trials. However, previous research has focused on response rates of various antidepressants on average only, ignoring potential differences among drugs or other aspects of trial findings. Little is known about the impact of a placebo arm on all-cause dropout and dropout due to adverse events. Methods:We carried out a systematic review of published and unpublished double-blind randomized controlled trials (RCTs) for the acute treatment of unipolar major depression (update: January 2016). The probability of being allocated to placebo (?) was the exposure of interest, and we examined its influence on responders (efficacy), all-cause dropouts (acceptability) and dropouts due to adverse events (tolerability), while accounting for differences in drugs, trials and patient characteristics in multivariate random effects meta-regression. Results:We included 421 studies (68 305 participants) comparing 16 antidepressants or placebo; ? ranged from 20% to 50%. Response rate was lower [risk ratio (RR) 0.87; 95% confidence interval (CI) 0.83, 0.92] and all-cause dropout rate higher (RR 1.19; 95% CI 1.08, 1.31) for the same antidepressants in placebo-controlled trials compared with head-to-head trials. The probability of responding decreased by 3% (95% CI 2-5%) for every 10% increase in ?, whereas the risk of all-cause dropout increased by 4% (95% CI 1-7%). Tolerability was unaffected by ?. Response rate was inversely correlated with dropouts due to any cause (correlation coefficient -0.48; 95% CI -0.58, -0.36) and due to adverse events (-0.34; 95% CI -0.44, -0.23). Conclusions:For the same antidepressant, response rate was on average smaller and dropouts higher when placebo was included; however, no association was found with dropouts due to adverse events. Decreased patient expectations, larger dropout rates and use of inappropriate statistical methods to impute missing data may explain this phenomenon. The findings call for caution in the integration of randomized evidence involving placebo arms.

Project description:ObjectivesMeta-analysis based on individual patient data (IPD) from randomised trials is superior to using published summary data since it facilitates subgroup and multiple variable analyses. Guidelines and funders expect that researchers share IPD for bona fide analyses, but in practice, this is done variably. Here, we report the experience of obtaining IPD for two collaborative analysis studies.SettingTwo linked studies required IPD from published randomised trials. The leading researchers for eligible trials were approached and asked to share IPD including trial characteristics, patient demographics, baseline clinical data and outcome measures.ParticipantsParticipants in eligible randomised controlled trials included patients with or at risk of cognitive decline/vascular events.Primary and secondary outcome measuresNumbers (%) of trials where the leading researcher responded favourably/negatively or did not respond. If negative, reasons behind the response were collected. If positive, methods used to share IPD were recorded.ResultsAcross the two studies, 391 completed trials were identified. Email addresses for researchers were found for 313 (80%) of the trials. One hundred and forty-eight (47%) researchers did not respond despite being sent multiple emails. Following contact, positive initial responses were received from 92 researchers, resulting in IPD being shared for 78 trials. Eighty-seven (28%) researchers declined to share data; justifications were recorded. The median time from first request to accessing data in one study was 241 (IQR 383.3) days. IPD sources included: direct from researcher, via academic trial funders repository and a website requiring remote analysis of commercial data. Where data were shared, a variety of methods were used to transfer data.ConclusionSharing of IPD from trials is desirable and a requirement of many funding bodies. However, accessing IPD faces multiple challenges including refusals to share, delays in access to data and having to perform analyses on a remote website.Trial registrationNot applicable.

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Dataset Information

Identifying Anticipated Events of Future Clinical Trials by Leveraging Data from the Placebo Arms of Completed Trials.

Background

Methods

Results

Conclusion

Publications

Identifying Anticipated Events of Future Clinical Trials by Leveraging Data from the Placebo Arms of Completed Trials.

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