Estimands and their implications for evidence synthesis for oncology: A simulation study of treatment switching in meta-analysis
Rebecca Kathleen Metcalfe, Antonio Remiro-Azócar, Quang Vuong, Anders Gorst-Rasmussen, Oliver Keene, Shomoita Alam, Jay J. H. Park
TL;DR
This study explores how mixing different statistical approaches in cancer drug trials can lead to misleading results when analyzing patient survival data.
Contribution
The paper introduces a simulation-based evaluation of estimand alignment in meta-analyses involving treatment switching in oncology trials.
Findings
Pooling estimates targeting different estimands leads to pooled estimators that do not target any estimand of interest.
Mixing treatment policy and hypothetical estimand estimates can generate misleading results even under random effects models.
Adopting the estimands framework in meta-analysis improves alignment with clinical research questions.
Abstract
The ICH E9(R1) addendum provides guidelines on accounting for intercurrent events in clinical trials using the estimands framework. However, there has been limited attention to the estimands framework for meta-analysis. Using treatment switching, a well-known intercurrent event that occurs frequently in oncology, we conducted a simulation study to explore the bias introduced by pooling together estimates targeting different estimands in a meta-analysis of randomized clinical trials (RCTs) that allowed treatment switching. We simulated overall survival data of a collection of RCTs that allowed patients in the control group to switch to the intervention treatment after disease progression under fixed effects and random effects models. For each RCT, we calculated effect estimates for a treatment policy estimand that ignored treatment switching, and a hypothetical estimand that accounted…
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Taxonomy
TopicsMeta-analysis and systematic reviews · Statistical Methods in Clinical Trials · Radiomics and Machine Learning in Medical Imaging