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An R-Based Landscape Validation of a Competing Risk Model
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Bootstrap and second-order tests of risk difference.

Chris J Lloyd1

  • 1University of Melbourne, Carlton, Australia. c.lloyd@mbs.edu

Biometrics
|November 17, 2009
PubMed
Summary
This summary is machine-generated.

Bootstrap P-values are recommended for small sample clinical trial data. This method offers superior accuracy, power, and stability compared to standard approximate tests and higher-order asymptotics for noninferiority margin testing.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Standard approximate statistical tests (score, likelihood ratio) exhibit limitations in clinical trials with small count data.
  • These limitations include discrepancies in results, inaccurate type-1 error rates, and non-monotonic inferences, even with large sample sizes.
  • Existing exact inference methods can produce unstable confidence sets and are sensitive to null parameter values.

Purpose of the Study:

  • To evaluate two modern approaches for small sample inference in clinical trials: higher-order asymptotics and parametric bootstrap.
  • To compare these methods against standard tests for assessing if a difference in probabilities exceeds a noninferiority margin.
  • To provide recommendations for the most reliable statistical inference method in challenging small sample scenarios.

Main Methods:

  • Evaluation of higher-order asymptotics (Reid, 2003) involving adjustments to likelihood ratio statistics.
  • Assessment of parametric bootstrap (Lee and Young, 2005) through exact significance calculations.
  • Extensive numerical studies comparing the performance of these methods under various conditions.

Main Results:

  • Bootstrap P-values demonstrated practical consistency across different test statistics.
  • The bootstrap method exhibited excellent type-1 error accuracy and enhanced statistical power.
  • Bootstrap P-values showed significantly less erratic variation concerning the null parameter (noninferiority margin).

Conclusions:

  • Parametric bootstrap P-values are recommended as the superior method for small sample inference in clinical trials.
  • This approach addresses the key limitations of standard approximate tests and higher-order asymptotics.
  • The bootstrap method offers reliable and robust statistical inference for noninferiority testing with small count data.