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Accommodating covariates roc analysis

Finally, for factors that contribute to discrimination, we propose combining the marker and covariate information, and ask how much discriminatory accu- racy improves with the addition of the marker to the covariates (incremental value).These methods follow naturally when representing the ROC curve as a summary of the distribution of case marker observations, standardized with respect to the control distribution.Cross-sectional survey of women responding to Whooley questions asked at their first antenatal appointment.

Finally, for factors that contribute to discrimination, we propose combining the marker and covariate information, and we ask how much discriminatory accuracy improves (in incremental value) with the addition of the marker to the covariates.Population prevalence was 27% (95% CI 22–32): 11% (95% CI 8–14) depression; 15% (95% CI 11–19) anxiety disorders; 2% (95% CI 1–4) obsessive–compulsive disorder; 0.8% (95% CI 0–1) post-traumatic stress disorder; 2% (95% CI 0.4–3) eating disorders; 0.3% (95% CI 0.1–1) bipolar disorder I, 0.3% (95% CI 0.1–1%) bipolar disorder II; 0.7% (95% CI 0–1) borderline personality disorder.For identification of depression, likelihood ratios were 8.2 (Whooley) and 9.8 (EPDS).Export reference: Persistent link: https://Econ Papers.repec.org/Re PEc:tsj:stataj:v:9:y:2009:i:1:p:17-39 Ordering information: This journal article can be ordered from Statistics for this article Stata Journal is currently edited by H. Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is commonly summarized using the receiver operating characteristic (ROC) curve.Oftentimes, factors other than disease affect marker observations.For example, levels of prostate-specific antigen (PSA), a biomarker widely used to screen men for prostate cancer, tend to increase with age.Finally, we provide practical recommendations for determining when and how to adjust for covariates, and we include links to software that can be used to implement these techniques.The classification accuracy of a continuous marker, , is its ability to distinguish between two groups defined by a binary outcome.A popular topic in medical research today is the development of markers to classify subjects as diseased or disease free, as high or low risk, or in terms of treatment response or another future event.These markers may be the results of, for example, genetic or proteomic evaluations, imaging techniques, bacterial culture, or risk factor information.

386 comments

  1. In many settings, covariates should be incorporated into the ROC analysis. First, there are covariates which impact the marker distribution among controls.

  2. Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is commonly.

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