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Meta-Analysis of Median Survival Times With Inverse-Variance Weighting.

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Summary
This summary is machine-generated.

This study introduces a method to meta-analyze median survival times by estimating standard errors from confidence intervals. The approach is effective for moderately large sample sizes but may be biased with small samples.

Keywords:
Brookmeyer–Crowley confidence intervalsaggregate datainverse‐variance weightingmedian survivalmeta‐analysistime‐to‐event outcomes

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Trial Analysis

Background:

  • Meta-analysis of time-to-event data often uses median survival times.
  • Standard meta-analysis methods require within-study standard errors, which are frequently unreported for median survival.
  • This limits the meta-analysis of median survival outcomes.

Purpose of the Study:

  • To develop and evaluate an inverse-variance weighted approach for meta-analyzing median survival times.
  • To estimate within-study standard errors from reported confidence intervals for median survival.

Main Methods:

  • An inverse-variance weighted meta-analysis method was proposed, estimating standard errors from confidence intervals.
  • Simulation studies were conducted to assess performance at study and meta-analytic levels.
  • The method was applied to a meta-analysis of non-small cell lung cancer trials.

Main Results:

  • The proposed method consistently estimates standard errors for median survival when using Brookmeyer-Crowley confidence intervals.
  • Performance was comparable to using true standard errors for moderately large sample sizes ( > 50).
  • Biased estimates of standard errors were observed with small effective sample sizes.

Conclusions:

  • The developed method provides a viable approach for meta-analyzing median survival times when standard errors are unreported.
  • Care must be taken when sample sizes are small, as bias may be introduced.
  • The method was successfully applied to evaluate survival benefits in non-small cell lung cancer therapies.