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Most Downloaded Articles
- An Introduction to Causal Inference by Pearl, Judea
- Principal Stratification: A Broader Vision by Shrier, Ian/ Kaufman, Jay S./ Platt, Robert W. and Steele, Russell J.
- Sensitivity Analysis for Causal Inference under Unmeasured Confounding and Measurement Error Problems by Díaz, Iván and van der Laan, Mark J.
- Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study by Xiao, Yongling/ Abrahamowicz, Michal and Moodie, Erica E. M.
- Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions by Díaz, Iván and van der Laan, Mark J.
Evaluating a New Marker for Risk Prediction Using the Test Tradeoff: An Update
1National Cancer Institute
2Katholieke Universiteit Leuven and Erasmus MC
Citation Information: The International Journal of Biostatistics. Volume 8, Issue 1, Pages 1–37, ISSN (Online) 1557-4679, DOI: 10.1515/1557-4679.1395, March 2012
- Published Online:
Most of the methodological literature on evaluating an additional marker for risk prediction involves purely statistical measures of classification performance. A disadvantage of a purely statistical measure is the difficulty in deciding the improvement in the measure that would make inclusion of the additional marker worthwhile. In contrast, a medical decision making approach can weigh the cost or harm of ascertaining an additional marker against the benefit of a higher true positive rate for a given false positive rate that may be associated with risk prediction involving the additional marker. An appealing form of the medical decision making approach involves the risk threshold, which is the risk at which the expected utility of treatment and no treatment is the same. In this framework, a readily interpretable evaluation of the net benefit of an additional marker is the test tradeoff corresponding to the risk threshold. The test tradeoff is the minimum number of tests for a new marker that need to be traded for a true positive to yield an increase in the net benefit of risk prediction with the additional marker. For a sensitivity analysis the test tradeoff is computed over multiple risk thresholds. This article updates the theory and estimation of the test tradeoff. An example is provided.