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A two-sample test for stochastic ordering with interval-censored data

G R Petroni1, R A Wolfe

  • 1Duke University Medical Center, Durham, North Carolina 27705.

Biometrics
|March 1, 1994
PubMed
Summary
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New statistical tests for discrete interval-censored data offer improved power for detecting differences in survival functions. These asymptotically nonparametric tests extend existing methods for right-censored data, showing promise in simulations.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Nonparametric Statistics

Background:

  • Interval-censored data presents unique challenges in survival analysis.
  • Existing methods for right-censored data require adaptation for interval-censored data.
  • Accurate estimation of survival functions is crucial for hypothesis testing.

Purpose of the Study:

  • To propose a new class of asymptotically nonparametric two-sample approximate tests for discrete interval-censored data.
  • To extend the Pepe and Fleming (1989) family of tests to accommodate interval-censored data.
  • To evaluate the performance of these new tests against existing methods.

Main Methods:

  • The proposed tests are based on an integrated weighted difference in survival function estimates.

Related Experiment Videos

  • Turnbull's (1976) non-parametric estimator for the survival function is utilized.
  • The sensitivity to the alternative of stochastic ordering is a key feature.
  • The impact of various weight functions on test performance is investigated.

    • Main Results:

    • Simulation results demonstrate that the new tests can be more powerful than omnibus likelihood ratio tests and Mantel's (1967) test.
    • This enhanced power is observed under various stochastically ordered alternatives for interval-censored data.
    • The proposed procedure, with a suitable weight function, shows good performance.
    • For small sample sizes (n=50) and moderate censoring (47-70%), the test size remains close to the nominal level.

    Conclusions:

    • The newly developed class of tests provides a valuable tool for analyzing discrete interval-censored survival data.
    • These tests offer a powerful alternative to existing methods, particularly when stochastic ordering is the alternative of interest.
    • The methodology is robust, maintaining appropriate size even with small sample sizes and moderate censoring.