Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study

Lejun Deng, Chih Yuan Hsu, Yu Shyr

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Background: Treatment switching, also called crossover, is common in clinical trials because of ethical concerns or other reasons. When it occurs and the primary objective is to identify treatment effects, the most widely used intention-to-treat analysis may lead to underpowered trials. Here, we presented an approach to preview power reductions and to estimate sample sizes required to achieve the desired power when treatment switching occurs in the intention-to-treat analysis. Methods: We proposed a simulation-based approach and developed an R package to perform power and sample sizes estimation in clinical trials with treatment switching. Results: We simulated a number of randomized trials incorporating treatment switching and investigated the impact of the relative effectiveness of the experimental treatment to the control, the switching probability, the switching time, and the deviation between the assumed and the real distributions for the survival time on power reductions and sample sizes estimation. The switching probability and the switching time are key determinants for significant power decreasing and thus sample sizes surging to maintain the desired power. The sample sizes required in randomized trials absence of treatment switching vary from around four-fifths to one-seventh of the sample sizes required in randomized trials allowing treatment switching as the switching probability increases. The power reductions and sample sizes increase with the decrease of switching time. Conclusions: The simulation-based approach not only provides a preview for power declining but also calculates the required sample size to achieve an expected power in the intention-to-treat analysis when treatment switching occurs. It will provide researchers and clinicians with useful information before randomized controlled trials are conducted.

Original languageEnglish
Article number49
JournalBMC Medical Research Methodology
Volume23
Issue number1
DOIs
Publication statusPublished - 2023 Dec

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Health Informatics

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