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:ImportanceAdverse events (AEs) after placebo treatment are common in randomized clinical drug trials. Systematic evidence regarding these nocebo responses in vaccine trials is important for COVID-19 vaccination worldwide especially because concern about AEs is reported to be a reason for vaccination hesitancy.ObjectiveTo compare the frequencies of AEs reported in the placebo groups of COVID-19 vaccine trials with those reported in the vaccine groups.Data sourcesFor this systematic review and meta-analysis, the Medline (PubMed) and Cochrane Central Register of Controlled Trials (CENTRAL) databases were searched systematically using medical subheading terms and free-text keywords for trials of COVID-19 vaccines published up to July 14, 2021.Study selectionRandomized clinical trials of COVID-19 vaccines that investigated adults aged 16 years or older were selected if they assessed solicited AEs within 7 days of injection, included an inert placebo arm, and provided AE reports for both the vaccine and placebo groups separately. Full texts were reviewed for eligibility by 2 independent reviewers.Data extraction and synthesisData extraction and quality assessment were performed independently by 2 reviewers, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline and using the Cochrane risk-of-bias tool. Meta-analyses were based on random-effects models.Main outcomes and measuresThe primary outcomes were the proportions of placebo recipients reporting overall, systemic, and local (injection-site) AEs as well as logarithmic odds ratios (ORs) to evaluate group differences. Outcomes were tested for significance using z tests with 95% CIs.ResultsTwelve articles with AE reports for 45 380 participants (22 578 placebo recipients and 22 802 vaccine recipients) were analyzed. After the first dose, 35.2% (95% CI, 26.7%-43.7%) of placebo recipients experienced systemic AEs, with headache (19.3%; 95% CI, 13.6%-25.1%) and fatigue (16.7%; 95% CI, 9.8%-23.6%) being most common. After the second dose, 31.8% (95% CI, 28.7%-35.0%) of placebo recipients reported systemic AEs. The ratio between placebo and vaccine arms showed that nocebo responses accounted for 76.0% of systemic AEs after the first COVID-19 vaccine dose and for 51.8% after the second dose. Significantly more vaccine recipients reported AEs, but the group difference for systemic AEs was small after the first dose (OR, -0.47; 95% CI, -0.54 to -0.40; P < .001; standardized mean difference, -0.26; 95% CI, -0.30 to -0.22) and large after the second dose (OR, -1.36; 95% CI, -1.86 to -0.86; P < .001; standardized mean difference, -0.75; 95% CI, -1.03 to -0.47).Conclusions and relevanceIn this systematic review and meta-analysis, significantly more AEs were reported in vaccine groups compared with placebo groups, but the rates of reported AEs in the placebo arms were still substantial. Public vaccination programs should consider these high rates of AEs in placebo arms.

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.

Project description:BackgroundInvestigators conducting randomized clinical trials often explore treatment effect heterogeneity to assess whether treatment efficacy varies according to patient characteristics. Identifying heterogeneity is central to making informed personalized healthcare decisions. Treatment effect heterogeneity can be investigated using subpopulation treatment effect pattern plot (STEPP), a non-parametric graphical approach that constructs overlapping patient subpopulations with varying values of a characteristic. Procedures for statistical testing using subpopulation treatment effect pattern plot when the endpoint of interest is survival remain an area of active investigation.MethodsA STEPP analysis was used to explore patterns of absolute and relative treatment effects for varying levels of a breast cancer biomarker, Ki-67, in the phase III Breast International Group 1-98 randomized clinical trial, comparing letrozole to tamoxifen as adjuvant therapy for postmenopausal women with hormone receptor-positive breast cancer. Absolute treatment effects were measured by differences in 4-year cumulative incidence of breast cancer recurrence, while relative effects were measured by the subdistribution hazard ratio in the presence of competing risks using O-E (observed-minus-expected) methodology, an intuitive non-parametric method. While estimation of hazard ratio values based on O-E methodology has been shown, a similar development for the subdistribution hazard ratio has not. Furthermore, we observed that the subpopulation treatment effect pattern plot analysis may not produce results, even with 100 patients within each subpopulation. After further investigation through simulation studies, we observed inflation of the type I error rate of the traditional test statistic and sometimes singular variance-covariance matrix estimates that may lead to results not being produced. This is due to the lack of sufficient number of events within the subpopulations, which we refer to as instability of the subpopulation treatment effect pattern plot analysis. We introduce methodology designed to improve stability of the subpopulation treatment effect pattern plot analysis and generalize O-E methodology to the competing risks setting. Simulation studies were designed to assess the type I error rate of the tests for a variety of treatment effect measures, including subdistribution hazard ratio based on O-E estimation. This subpopulation treatment effect pattern plot methodology and standard regression modeling were used to evaluate heterogeneity of Ki-67 in the Breast International Group 1-98 randomized clinical trial.ResultsWe introduce methodology that generalizes O-E methodology to the competing risks setting and that improves stability of the STEPP analysis by pre-specifying the number of events across subpopulations while controlling the type I error rate. The subpopulation treatment effect pattern plot analysis of the Breast International Group 1-98 randomized clinical trial showed that patients with high Ki-67 percentages may benefit most from letrozole, while heterogeneity was not detected using standard regression modeling.ConclusionThe STEPP methodology can be used to study complex patterns of treatment effect heterogeneity, as illustrated in the Breast International Group 1-98 randomized clinical trial. For the subpopulation treatment effect pattern plot analysis, we recommend a minimum of 20 events within each subpopulation.

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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