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Effective sample sizes for confidence intervals for survival probabilities.

F J Dorey1, E L Korn

  • 1Division of Orthopaedic Surgery, UCLA School of Medicine 90024.

Statistics in Medicine
|September 1, 1987
PubMed
Summary
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Estimating effective sample size is crucial for survival probability confidence intervals. Different methods, like Cutler-Ederer and Peto, yield distinct results, especially with censored data.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Methods

Background:

  • Accurate estimation of effective sample size is vital for constructing reliable confidence intervals in survival analysis.
  • Existing methods for effective sample size calculation may not adequately address the complexities of censored survival data.
  • The asymmetry inherent in censored data suggests a need for differentiated approaches to upper and lower confidence intervals.

Purpose of the Study:

  • To compare various methods for estimating effective sample size in the context of survival probability confidence intervals.
  • To evaluate the performance of different effective sample size estimators, including Cutler-Ederer and Peto methods, particularly with numerous censored observations.
  • To provide recommendations for the optimal use of effective sample sizes in constructing asymmetric confidence intervals for survival data.

Related Experiment Videos

Main Methods:

  • Comparative analysis of established effective sample size estimation methods: Cutler-Ederer, Peto et al., and a modified Cutler-Ederer approach.
  • Investigation of these methods in scenarios with a high proportion of censored observations preceding the time point of interest.
  • Application of recommended methods to real-world survival datasets to illustrate practical differences in confidence interval construction.

Main Results:

  • Different effective sample size estimators produce varying results, impacting the width and placement of confidence intervals.
  • The Cutler-Ederer effective sample size is recommended for upper confidence intervals, while the Peto effective sample size is suggested for lower confidence intervals.
  • Real data examples highlight significant differences in confidence intervals derived from distinct effective sample size calculations.

Conclusions:

  • The choice of effective sample size method significantly influences confidence interval construction for survival probabilities.
  • Asymmetric confidence intervals, utilizing tailored effective sample sizes (Cutler-Ederer for upper, Peto for lower), are more appropriate for censored survival data.
  • Caution is advised when applying simulation study findings directly to real-world survival data analysis problems.