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Objective: To explore designs for evaluating the prognostic and predictive value of medical tests and their effect on patient outcome., Study Design: Theoretical analysis with examples from the medical literature., Results: For evaluating the prognostic value of a test, one can include the test at baseline in prognostic studies. To evaluate the value of test in predicting treatment outcome, the test results can be used as baseline information in randomized controlled trials of treatment. To compare the prognostic or predictive value of two or more tests, the test result combinations can be used as baseline information. To evaluate the effect on patient outcome, randomized controlled trials of test strategies are an option. Randomization can apply to all tested or be restricted to specific subgroups, such as those with discordant test results, to increase the efficiency of trials., Conclusion: The prognostic and predictive value of medical tests can and should be evaluated, to demonstrate the test's ability to guide clinical decision making and to improve patient outcome. Various randomized designs can be used to evaluate the effects on testing on patient outcome.

Diagnostic testing can be used to discriminate subjects with a target disorder from subjects without it. Several indicators of diagnostic performance have been proposed, such as sensitivity and specificity. Using paired indicators can be a disadvantage in comparing the performance of competing tests, especially if one test does not outperform the other on both indicators. Here we propose the use of the odds ratio as a single indicator of diagnostic performance. The diagnostic odds ratio is closely linked to existing indicators, it facilitates formal meta-analysis of studies on diagnostic test performance, and it is derived from logistic models, which allow for the inclusion of additional variables to correct for heterogeneity. A disadvantage is the impossibility of weighing the true positive and false positive rate separately. In this article the application of the diagnostic odds ratio in test evaluation is illustrated.