Some Contributions to Understanding the Heterogeneity of Treatment Effects in Stroke Trials
Creators: name: Ademola, Ayoola
Publisher: name: Cumming School of Medicine
Year: 2024
Subjects: subject: Heterogeneity of treatment effects
subject: Stroke trials
subject: Subgroup analysis
subject: The Instrument for Assessing the Credibility of Effect Modification Analyses (ICEMAN) checklist
subject: Subgroup identification methods
subject: Meta-analysis
subject: Pragmatic and explanatory studies
subject: Biostatistics

description: Background: Stroke is a neurological disease that is the third leading cause of death and the tenth-largest known cause of disability-adjusted life years in Canada. Fortunately, clinical trial evidence has identified a few treatments that improve patients’ outcomes, resulting in faster reperfusion, better functional outcomes, lower mortality rates, and improved quality of life. Despite the overall positive benefits of these interventions, there remain differences in the impact of the treatment at the individual level, with some patients experiencing positive benefits and others showing neutral or adverse effects of interventions. Such heterogeneity of treatment effects (HTE) could be attributed to differences in patients’ socio-demographic or clinical characteristics, study designs, inclusion/exclusion criteria, and geographic or regional healthcare systems. Conventional statistical approaches for accounting for within-study and between-study HTE have primarily relied on within-trial subgroup analysis and meta-analysis. However, these approaches are limited because they are based on restrictive distributional assumptions, which may only be tenable in some clinical trials. Methods: This dissertation investigates relevant methodologies for characterizing and accommodating treatment effects within- and between-study heterogeneity in stroke trials. The specific objectives of this dissertation are to: 1) assess the credibility of subgroup analyses reported in published stroke trials; 2) investigate the comparative performance of methods for subgroup identification in clinical trials with binary endpoints when there is no a priori knowledge of patients’ characteristics associated with HTE, and 3) examine the performance of random-effects models when synthesizing evidence from trials with different study design characteristics. This study uses a combination of knowledge synthesis methodology and computer simulations to address these objectives. For objective 1, we conducted a systematic review to examine the credibility of reported subgroups in stroke trials. We used the Instrument for Assessing the Credibility of Effect Modification Analyses (ICEMAN) checklist to evaluate the quality of the subgroup analyses conducted for each study. For Objectives 2 and 3, computer simulations were used to examine the comparative performance of subgroup identification methods for identifying relevant variables/biomarkers associated with HTE in clinical trials of binary endpoints and meta-analytic methods for synthesizing treatment effects obtained from explanatory and pragmatic trials, respectively. Results: The systematic review of reporting quality of subgroup analyses in stroke trials revealed that the credibility of reported subgroup analyses is poor, with most studies not providing a priori rationale for the type and number of subgroup analyses conducted. Among all the subgroup identification methods investigated, the model-based recursive partitioning (MOB) method had the best control of Type I and higher statistical power to detect HTE. The random-effects model based on t-distribution (robustRE) and the mixture random-effects model (mixRE) are more appropriate for meta-analysis data with substantial HTE. However, the conventional random-effects model (RE model) remains reliable for estimating pooled treatment effects in data with moderate HTE. Conclusion: Understanding and capturing treatment effect heterogeneity is critical for generating evidence about treatment effectiveness in clinical trials. More statistical methods that account for heterogeneity in the study population and design characteristics are recommended to analyze and synthesize evidence from clinical trials.