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An R-Based Landscape Validation of a Competing Risk Model
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Published on: September 16, 2022

Sample size calculations for ROC studies: parametric robustness and Bayesian nonparametrics.

Dunlei Cheng1, Adam J Branscum, Wesley O Johnson

  • 1Institute for Health Care Research and Improvement, Baylor Health Care System, Dallas, TX 75206, USA. dunleic@baylorhealth.edu

Statistics in Medicine
|December 6, 2011
PubMed
Summary
This summary is machine-generated.

Sample size calculations for ROC studies can be inaccurate if normal distributions are wrongly assumed. Flexible Bayesian nonparametric models offer more robust sample size determination for skewed or multimodal data.

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Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Statistical Modeling

Background:

  • Receiver Operating Characteristic (ROC) studies commonly employ sample size calculations based on the assumption of normal distributions for test scores in diseased and nondiseased groups.
  • This standard approach may lead to inaccurate sample size requirements when the actual data distributions deviate from normality, such as in cases of skewed or multimodal data.

Purpose of the Study:

  • To investigate the robustness of sample size calculations under the standard two-group normal model when data distributions are non-normal (skewed or multimodal).
  • To compare sample size requirements derived from the standard normal model with those from a flexible nonparametric Dirichlet process mixture model.
  • To highlight the utility of flexible models for ROC data analysis and their importance in study design, especially when nonstandard distributional shapes are anticipated.

Main Methods:

  • Evaluated sample size requirements using a standard two-group normal model and a flexible nonparametric Dirichlet process mixture model.
  • Investigated the robustness of sample size calculations under mis-specified normal models for skewed and multimodal data distributions.
  • Developed a Bayesian nonparametric approach for sample size determination and sensitivity analysis, extending to comparative studies with two continuous tests.
  • Implemented a simulation-based procedure using WinBUGS and R software packages, with example code provided.

Main Results:

  • Standard normal model-based sample size calculations may be unreliable when actual data distributions are skewed or multimodal.
  • Flexible nonparametric models provide more appropriate distributional assumptions and thus more accurate sample size estimations for nonstandard data shapes.
  • The Bayesian nonparametric approach offers a tool for sensitivity analysis of sample size calculations based on the traditional two-group normal model.

Conclusions:

  • Flexible Bayesian nonparametric models are crucial for accurate sample size determination in ROC studies when nonstandard data distributions are expected.
  • Relying solely on the normal distribution assumption can compromise the validity of ROC study designs and power calculations.
  • The proposed methodology enhances the reliability of study design by accommodating complex data distributions in ROC analysis.