Cochran's Q test was useful to assess heterogeneity in likelihood ratios in studies of diagnostic accuracy

J Clin Epidemiol. 2015 Mar;68(3):299-306. doi: 10.1016/j.jclinepi.2014.09.005. Epub 2014 Oct 23.


Objectives: Empirical evaluations have demonstrated that diagnostic accuracy frequently shows significant heterogeneity between subgroups of patients within a study. We propose to use Cochran's Q test to assess heterogeneity in diagnostic likelihood ratios (LRs).

Study design and setting: We reanalyzed published data of six articles that showed within-study heterogeneity in diagnostic accuracy. We used the Q test to assess heterogeneity in LRs and compared the results of the Q test with those obtained using another method for stratified analysis of LRs, based on subgroup confidence intervals. We also studied the behavior of the Q test using hypothetical data.

Results: The Q test detected significant heterogeneity in LRs in all six example data sets. The Q test detected significant heterogeneity in LRs more frequently than the confidence interval approach (38% vs. 20%). When applied to hypothetical data, the Q test would be able to detect relatively small variations in LRs, of about a twofold increase, in a study including 300 participants.

Conclusion: Reanalysis of published data using the Q test can be easily performed to assess heterogeneity in diagnostic LRs between subgroups of patients, potentially providing important information to clinicians who base their decisions on published LRs.

Keywords: Bayes theorem; Data interpretation, statistical; Diagnostic techniques and procedures/statistics and numerical data; Likelihood functions; Predictive value of tests; Sensitivity and specificity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computer Simulation
  • Data Interpretation, Statistical*
  • Diagnostic Techniques and Procedures / standards*
  • Diagnostic Techniques and Procedures / statistics & numerical data
  • Health Status Indicators*
  • Humans
  • Likelihood Functions*
  • Meta-Analysis as Topic
  • Predictive Value of Tests