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Sample size calculation for studies with grouped survival data.

Zhiguo Li1, Xiaofei Wang1, Yuan Wu1

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.

Statistics in Medicine
|June 12, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new sample size formula for grouped survival data, improving accuracy in clinical trial design. The formula avoids distributional assumptions and approximations, offering a more reliable method for estimating required sample sizes.

Keywords:
grouped survival dataproportional hazards modelsample size calculation

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Grouped survival data are common in clinical studies where events are observed at discrete intervals.
  • Traditional methods for analyzing grouped data can lead to biased effect size estimates.
  • Existing sample size calculations often rely on restrictive assumptions or approximations.

Purpose of the Study:

  • To develop a novel sample size formula for studies with grouped survival endpoints.
  • To address limitations of previous sample size calculation methods for grouped data.
  • To provide a robust tool for designing clinical trials with interval-censored data.

Main Methods:

  • Developed a sample size formula based on the Prentice and Gloeckler maximum likelihood estimator for proportional hazards models.
  • The formula does not impose distributional assumptions or use variance approximations.
  • Requires estimates of hazard ratio and survival/censoring probabilities.

Main Results:

  • The proposed sample size formula is validated through a simulation study.
  • Demonstrated good performance in simulation, indicating reliability.
  • Illustrated practical application using examples from HIV, cancer, and drug toxicity studies.

Conclusions:

  • The new sample size formula offers an accurate and flexible approach for studies with grouped survival data.
  • It enhances the design of clinical trials by providing a more precise estimation of required sample sizes.
  • Applicable across various research areas including oncology and infectious diseases.