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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Randomized two-stage optimal design for interval-censored data.

Guogen Shan1

  • 1Department of Biostatistics, University of Florida, Gainesville, USA.

Journal of Biopharmaceutical Statistics
|December 10, 2021
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Summary
This summary is machine-generated.

This study introduces sample size calculations for interval-censored data, crucial for clinical trials. The choice of statistical test impacts sample size, especially concerning the proportional hazards assumption.

Keywords:
Interval-censored datarepeated measuressample sizesurvival datatwo-stage designs

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Interval-censored data, where event times are known only within intervals, are common in medical research.
  • Existing statistical tests for interval-censored data include logrank, proportional hazards, and Wilcoxon-type tests.
  • Accurate sample size calculation is essential for the validity and efficiency of clinical trials.

Purpose of the Study:

  • To propose novel sample size calculation methods for interval-censored data.
  • To evaluate these methods for parallel one-stage and two-stage clinical trial designs.
  • To compare the efficiency of different statistical tests under various assumptions.

Main Methods:

  • Developed sample size calculations based on Sun's logrank test, Finkelstein's proportional hazards test, and Peto and Peto's Wilcoxon-type test.
  • Investigated the impact of the proportional hazards assumption on sample size requirements.
  • Utilized a lung cancer clinical trial example to demonstrate the practical application of the proposed methods.

Main Results:

  • When the proportional hazards assumption holds, the proportional hazards and logrank tests require smaller sample sizes than the Wilcoxon-type test.
  • Conversely, when the proportional hazards assumption is violated, the Wilcoxon-type test demonstrates greater sample size efficiency.
  • The proposed calculations offer substantial sample size savings depending on the chosen test and adherence to the proportional hazards assumption.

Conclusions:

  • The choice of statistical test significantly influences sample size needs for interval-censored data.
  • The proportional hazards assumption is a critical factor in determining the most efficient test for sample size calculation.
  • These sample size calculation methods provide valuable tools for designing robust clinical trials with interval-censored data.