Seminar #67: Meta-Analysis and Diagnostic Tests
Content: Meta-analysis is the quantitative combination of results from multiple research studies. Meta-analysis is a relatively new field in Statistics, and standards for the proper data analysis are still evolving. Meta-analysis of studies of diagnostic tests, in particular, is especially controversial, with many conflicting approaches for computing an overall estimate from the individual sensitivity or specificity values from these studies. In the first half of this talk, I will review the general methods for the quantitative combination of results in a meta-analysis, and work out two examples using R and the meta library. In the second half, I will use data from a meta-analysis of 20 studies of endovaginal ultrasonography for detecting endometrial cancer to illustrate and critically evaluate several competing approaches for quantitatively combining results from diagnostic studies. All the data sets used in this presentation come from journal articles where the full free text is available on the web.
Teaching strategies: Didactic lectures and small group exercises.
Objectives: In this class you will learn how to:
- compute fixed and random effects models using R and the meta library;
- display results of individual diagnostic studies on a Summary Receiver Operating Characteristic plot;
- combine estimates of sensitivity and specificity directly and on a log odds scale; and
- compute the diagnostic odds ratio and graphically evaluate heterogeneity.
Contents
- Abstract
- Where can I find this handout?
- Do the pieces fit together? Meta-analyses and systematic overviews.
- Guidelines for meta-analysis models
- Definition: Sensitivity
- Definition: Specificity
- Meta-analysis for a diagnostic test