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When are summary ROC curves appropriate for diagnostic meta-analyses?
F M Chappell1, G M Raab, J M Wardlaw
1School of Nursing Midwifery and Social Care, Napier University, Edinburgh, UK. francesca.chappell@ed.ac.uk
Statistics in Medicine
|July 11, 2009
Summary
This study addresses challenges in interpreting diagnostic test accuracy meta-analyses. It suggests alternative univariate analyses when summary receiver operating characteristic (SROC) models are unreliable due to underpowered data.
Area of Science:
- Medical Statistics
- Diagnostic Test Evaluation
- Systematic Reviews
Background:
- Diagnostic test accuracy is frequently assessed using systematic reviews.
- Statistical methods for analyzing diagnostic test data have advanced.
- The summary receiver operating characteristic (SROC) curve is a common method, often fitted using bivariate random-effects models.
Purpose of the Study:
- To highlight practical issues in interpreting and presenting data from diagnostic test accuracy meta-analyses.
- To propose alternative analytical approaches when SROC models are problematic.
- To provide guidance for analysts dealing with such data.
Main Methods:
- Focus on practical problems in fitting and interpreting summary receiver operating characteristic (SROC) models.
- Discuss situations where meta-analyses are underpowered for reliable SROC parameter estimation.
- Introduce and characterize problems with bivariate random-effects models.
- Propose using two univariate meta-analyses of true and false positive rates (TPRs and FPRs) as an alternative.
- Present an algorithm to guide analysts.
Main Results:
- Identified potential underpowering issues in meta-analyses affecting SROC parameter reliability.
- Highlighted situations where the SROC model may be inappropriate for diagnostic test data.
- Demonstrated the utility of univariate meta-analyses for TPR and FPR when SROC models fail.
- Provided practical guidance and an algorithm for choosing appropriate statistical methods.
Conclusions:
- The interpretation and presentation of diagnostic test meta-analyses require careful consideration of statistical model appropriateness.
- Univariate meta-analyses of true and false positive rates offer a viable alternative when SROC models are unreliable.
- Freely available R functions are provided to support these analyses.
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