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Practicable confidence intervals for current status data.

Byeong Yeob Choi1, Jason P Fine, M Alan Brookhart

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA.

Statistics in Medicine
|September 11, 2012
PubMed
Summary
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Improved confidence intervals (CIs) for binary isotonic regression and survival data are now available. A new R package offers practical computation of Wald-type and bootstrap CIs, enhancing small-sample performance.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Confidence intervals (CIs) for binary isotonic regression and current status survival data face practical limitations due to small-sample performance and computational challenges.
  • Existing methods like Wald-based, subsampling-based, and likelihood-ratio test (LRT)-based CIs have theoretical backing but limited practical use.
  • Previous research indicated subsampling and LRT methods outperform simple Wald methods in coverage probabilities for realistic sample sizes.

Purpose of the Study:

  • To improve the practical application of confidence intervals for binary isotonic regression and current status survival data.
  • To demonstrate that transformed Wald-based CIs achieve competitive performance with LRT-based methods in small to moderate sample sizes.
  • To validate the efficacy of nonparametric bootstrap CIs for the considered data-generating mechanisms.

Related Experiment Videos

Main Methods:

  • Utilizing transformations to enhance simple Wald-based confidence intervals.
  • Conducting simulation studies to compare the performance of different CI methods.
  • Implementing a nonparametric bootstrap approach for CI construction.
  • Developing an R package for computing Wald-type and bootstrap CIs.

Main Results:

  • Transformed Wald-based CIs exhibit improved performance in small and moderate sample sizes, rivaling the LRT-based method.
  • Nonparametric bootstrap methods provide approximately correct confidence intervals for the simulated data.
  • The developed R package facilitates the computation of these improved CIs.

Conclusions:

  • Transformed Wald-based and bootstrap confidence intervals offer practical and effective solutions for binary isotonic regression and current status survival data.
  • The new R package makes these advanced statistical methods accessible for real-world data analysis.
  • The study addresses the need for reliable and computationally feasible confidence intervals in survival analysis and related fields.