ArticleStatistics in medicine2018
Design and estimation in clinical trials with subpopulation selection.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Adaptive enrichment trial designs using joint modelling of longitudinal and time-to-event data.Statistical methods in medical research · 2024Article
- A Two-Stage Covariate-Adjusted Response-Adaptive Enrichment Design.Statistics in biopharmaceutical research · 2024Article
- IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy.BMC medical research methodology · 2023Article
- Point estimation for adaptive trial designs I: A methodological review.Statistics in medicine · 2023Review
- Selecting predictive biomarkers from genomic data.PloS one · 2022Article
- Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs.Statistics in medicine · 2021Article
- Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection.Statistics in medicine · 2020Article
- Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.Statistics in biopharmaceutical research · 2020Article
- The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design.BMJ (Clinical research ed.) · 2020Article
- Article
- Design and estimation in clinical trials with subpopulation selection.Statistics in medicine · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Population heterogeneity is frequently observed among patients' treatment responses in clinical trials because of various factors such as clinical background, environmental, and genetic factors. Different subpopulations defined by those baseline factors can lead to differences in the benefit or safety profile of a therapeutic intervention. Ignoring heterogeneity between subpopulations can substantially impact on medical practice. One approach to address heterogeneity necessitates designs and analysis of clinical trials with subpopulation selection. Several types of designs have been proposed for different circumstances. In this work, we discuss a class of designs that allow selection of a predefined subgroup. Using the selection based on the maximum test statistics as the worst-case scenario, we then investigate the precision and accuracy of the maximum likelihood estimator at the end of the study via simulations. We find that the required sample size is chiefly determined by the subgroup prevalence and show in simulations that the maximum likelihood estimator for these designs can be substantially biased.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.