AI Article Synopsis
- Recent phase 3 cancer trials emphasize the importance of evaluating overall survival, especially when there's frequent patient crossover between treatment groups.
- The conventional intention-to-treat analysis often weakens the ability to identify true survival effects without crossover, highlighting limitations in current analysis methods.
- The study proposes a new Bayesian power prior method using data from an external trial, which showed superior effectiveness in simulations, particularly when patients with better outcomes tend to crossover.
Article Abstract
In recent placebo-controlled randomized phase 3 oncology trials, evaluation of overall survival with frequent crossover is crucial for regulatory and pricing decisions. The problem is that an intention-to-treat based analysis causes a substantial loss of power to detect causal survival effect without crossover, and performance of existing methods is not satisfactory. In this article, our aims were to evaluate properties of the existing and a proposed Bayesian power prior method where data from an external trial is available. Simulation results suggested that proposed method was the most powerful under typical scenarios where patients with better prognosis are likely to crossover.
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| http://dx.doi.org/10.1080/10543406.2019.1695625 | DOI Listing |
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