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Optimal sampling ratios in comparative diagnostic trials.

Ting Dong1, Liansheng Larry Tang1, William F Rosenberger1

  • 1George Mason University, Fairfax, USA.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|June 21, 2014
PubMed
Summary
This summary is machine-generated.

Choosing the right case-control sampling ratio is crucial for efficient diagnostic trials. This study presents an adaptive method to determine the optimal ratio, maximizing statistical power and minimizing sample size without needing pilot data.

Keywords:
Area under the curveDiagnostic accuracyPartial area under the curvePowerReceiver operating characteristic curveTwo-stage design

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Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Statistical Modeling

Background:

  • Subjective selection of case-control sampling ratios in diagnostic trials may not be optimal for statistical power or sample size.
  • Existing methods for determining optimal ratios often lack adaptability or require preliminary data.

Purpose of the Study:

  • To derive optimal sampling ratios for case-control studies in diagnostic trials.
  • To propose an adaptive two-stage procedure for estimating the optimal ratio without pilot data.
  • To enhance statistical efficiency in comparative diagnostic research.

Main Methods:

  • Derivation of explicit expressions for optimal sampling ratios based on common variance structures of receiver operating characteristic (ROC) curve summary statistics.
  • Development of a two-stage adaptive procedure to estimate the optimal ratio.
  • Validation through theoretical proofs, extensive simulations, and a real-world cancer diagnostic study.

Main Results:

  • Demonstration that subjective sampling ratios are often inefficient.
  • Successful development and validation of an adaptive method for optimal ratio estimation.
  • The proposed method effectively balances statistical power and sample size requirements.

Conclusions:

  • The proposed adaptive sampling ratio method offers a statistically efficient approach for comparative diagnostic trials.
  • This method eliminates the need for pilot studies, simplifying trial design.
  • The findings are applicable to various diagnostic studies, including cancer diagnostics.