Just as there are many markers of oxidative stress, the rapid growth of biotechnology means that researchers increasingly must consider which screening or diagnostic test to use in their research. My work with ROC curves is aimed at providing evidence-based approaches for making these choices. The ROC curve simultaneously plots the proportion of both abnormal and normal subjects correctly diagnosed at various test cutoff points. This graphical display facilitates the selection of an optimal threshold and enables easy comparison of the abilities of different tests. Increasingly, ROC curves are used in population based settings as opposed to settings where individuals have been pre-screened to some degree. However, ROC curve methods were not developed to account for common problems such as missing data, measurement error, linear combinations, confounding, referral bias, LODs, and other challenges. We have proposed estimators of the mean of a K-sample U-statistic (of which the area under the ROC curve (AUC) is a special case) when data on the outcomes of interest are missing in some sampled units and auxiliary variables are available in the entire sample. The proposed estimators exploit the information available in the auxiliaries without requiring assumptions about the joint distribution of the auxiliaries and outcomes. The properties of the proposed estimators are derived from general results on efficient semi-parametric estimation of the mean of a K-sample U-statistic with missing at random outcomes, observed auxiliary variables and known missingness probabilities. Random measurement error can attenuate a biomarkers ability to discriminate between diseased and non-diseased populations. We present an approach for estimating the Youden index, the AUC and its associated optimal cut-point for a normally distributed biomarker that corrects for normally distributed random measurement error. We also developed confidence intervals for these corrected estimates using the delta method and coverage probability through simulation of a variety of situations. Applying these techniques to the biomarker thiobarbituric acid reaction substance (TBARS), a measure of oxidative stress that has been proposed as a discriminating measurement for infertility, yields a 50% increase in diagnostic effectiveness at the optimal cut-point. This result may lead to biomarkers that were once naively considered ineffective becoming useful diagnostic devices. Since multiple markers are often available, we considered combining them to improve diagnostic accuracy. The linear combinations derived by Su and Liu (1993) that maximize the AUC may have unsatisfactorily low sensitivity over a certain range of desired specificity. We considered maximization of sensitivity over a range of specificity, and presented alternative linear combinations that have higher sensitivity over a range of high (or low) specificity. Additionally, we evaluated covariate effects on this linear combination assuming that the multiple markers or a transformation thereof, follow a multivariate normal distribution. We estimated the ROC curve of this linear combination of markers adjusted for covariates and approximate confidence intervals for the corresponding AUC. Another frequently encountered problem in studies that evaluate new diagnostic tests is that not all patients undergo disease verification due to the expense and/or invasiveness of the test. In fact, the decision to subject patients to verification testing often depends on the results of the new test and other predictors of disease status. For diagnostic tests where AUC estimation is based only on patients with verified disease status, the usual estimators are biased. We developed estimators that adjust for this bias. When information on disease status is missing, it is necessary either to model the missing data or the process leading to the missingness to obtain well-behavedestimators of the AUC. We have described a doubly robust estimator that is unbiased when the model for disease or the missingness is correct. This estimator does not require EM-type iterations and is easy to compute using standard software. It can accommodate both discrete and continuous markers and allows for the possibility that selection to verification is non-ignorable. In addition, the doubly robust estimator offers more protection against model misspecification than other currently available methods. We have applied the methods described above to show that TBARS, has discriminating abilities above and beyond chance. This work has yielded 23 publications in peer reviewed journals including Biometrika and the Journal of the American Statistical Association.
| Vernet, Céline; Philippat, Claire; Calafat, Antonia M et al. (2018) Within-Day, Between-Day, and Between-Week Variability of Urinary Concentrations of Phenol Biomarkers in Pregnant Women. Environ Health Perspect 126:037005 |
| Van Domelen, Dane R; Mitchell, Emily M; Perkins, Neil J et al. (2018) Logistic regression with a continuous exposure measured in pools and subject to errors. Stat Med 37:4007-4021 |
| Pollack, Anna Z; Mumford, Sunni L; Krall, Jenna R et al. (2018) Exposure to bisphenol A, chlorophenols, benzophenones, and parabens in relation to reproductive hormones in healthy women: A chemical mixture approach. Environ Int 120:137-144 |
| Schildcrout, Jonathan S; Schisterman, Enrique F; Aldrich, Melinda C et al. (2018) Outcome-related, Auxiliary Variable Sampling Designs for Longitudinal Binary Data. Epidemiology 29:58-66 |
| Sjaarda, Lindsey A; Ahrens, Katherine A; Kuhr, Daniel L et al. (2018) Pilot study of placental tissue collection, processing, and measurement procedures for large scale assessment of placental inflammation. PLoS One 13:e0197039 |
| Schildcrout, Jonathan S; Schisterman, Enrique F; Mercaldo, Nathaniel D et al. (2018) Extending the Case-Control Design to Longitudinal Data: Stratified Sampling Based on Repeated Binary Outcomes. Epidemiology 29:67-75 |
| Ananth, Cande V; Schisterman, Enrique F (2018) Reply. Am J Obstet Gynecol 218:366-367 |
| Harel, Ofer; Mitchell, Emily M; Perkins, Neil J et al. (2018) Multiple Imputation for Incomplete Data in Epidemiologic Studies. Am J Epidemiol 187:576-584 |
| Lash, Timothy L; Schisterman, Enrique F (2018) New Designs for New Epidemiology. Epidemiology 29:76-77 |
| Sun, BaoLuo; Perkins, Neil J; Cole, Stephen R et al. (2018) Inverse-Probability-Weighted Estimation for Monotone and Nonmonotone Missing Data. Am J Epidemiol 187:585-591 |
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