ArticleStatistics in biopharmaceutical research
A Multi-state Model for Designing Clinical Trials for Testing Overall Survival Allowing for Crossover after Progression.
Article in Statistics in biopharmaceutical research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
4 citing papers in PubMed.
- Censoring, Competing Events, and Multistate Models: Comment on Beyersmann et al. "Hazards Constitute Key Quantities for Analyzing, Interpreting and Understanding Time-to-Event Data".Biometrical journal. Biometrische Zeitschrift · 2026Article
- Improved estimation of overall survival and progression-free survival for state transition modeling.Journal of comparative effectiveness research · 2024Article
- Design and sample size considerations for Alzheimer's disease prevention trials using multistate models.Clinical trials (London, England) · 2019Article
- Application of multi-state models in cancer clinical trials.Clinical trials (London, England) · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In designing a clinical trial for comparing two or more treatments with respect to overall survival (OS), a proportional hazards assumption is commonly made. However, in many cancer clinical trials, patients pass through various disease states prior to death and because of this may receive treatments other than originally assigned. For example, patients may crossover from the control treatment to the experimental treatment at progression. Even without crossover, the survival pattern after progression may be very different than the pattern prior to progression. The proportional hazards assumption will not hold in these situations and the design power calculated on this assumption will not be correct. In this paper we describe a simple and intuitive multi-state model allowing for progression, death before progression, post-progression survival and crossover after progression and apply this model to the design of clinical trials for comparing the OS of two treatments. For given values of the parameters of the multi-state model, we simulate the required number of deaths to achieve a specified power and the distribution of time required to achieve the requisite number of deaths. The results may be quite different from those derived using the usual PH assumption.
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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.