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Multivariate two-sample permutation tests for trials with multiple time-to-event outcomes.

Inger Persson1, Lukas Arnroth1, Måns Thulin1,2

  • 1Department of Statistics, Uppsala University, Uppsala, Sweden.

Pharmaceutical Statistics
|March 27, 2019
PubMed
Summary
This summary is machine-generated.

New permutation tests accurately analyze multiple time-to-event outcomes in clinical trials, especially with high censoring. These novel methods offer reliable group comparisons for complex survival data.

Keywords:
lifetime datamultivariate logrank testsurvival analysistwo-sample test

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Clinical trials frequently involve multiple time-to-event outcomes, necessitating robust statistical methods.
  • Existing two-sample tests for multivariate survival data may not always maintain the desired type I error rate.

Purpose of the Study:

  • To propose novel permutation tests for assessing group differences in multivariate time-to-event data.
  • To evaluate the performance of these tests compared to existing methods, particularly under varying degrees of censoring.

Main Methods:

  • Development and application of permutation tests designed for multivariate time-to-event data.
  • Comparative analysis through a simulation study assessing type I error rate and power.
  • Illustration of test utility using three publicly available clinical trial datasets.

Main Results:

  • The proposed permutation tests maintain the nominal type I error rate.
  • These tests demonstrate superior performance over competitors in scenarios with high levels of censored observations.
  • In low censoring situations, traditional tests like Hotelling's T-squared may perform better than survival-specific tests.

Conclusions:

  • Permutation tests provide a statistically sound approach for analyzing multivariate time-to-event data in clinical trials.
  • The proposed methods are particularly valuable when dealing with significant patient censoring.
  • Accessible R package implementations facilitate the practical application of these advanced statistical techniques.