New diagnostic tests are developed quickly, and existing diagnostic tests are often rapidly improved after being introduced into practice. Unfortunately, inaccurate and biased evaluations of a test's statistical properties, often the result of a poorly designed or poorly analyzed study, leads to their premature dissemination and to physicians using unreliable tests to make critical treatment decisions. Perhaps the most common cause for the misevalu- ation of diagnostic tests is verification bias. Verification bias occurs when the verification of a patient's disease status depends on the result of the proposed test or certain patient characteristics associated with disease status. Statistical methods that correct for verification bias are underdeveloped and seldom used, and this application proposes a novel statistical strategy for addressing verification bias that is generalizable and accessible to non- statisticians (with appropriate software package). Even when only a select subset of low-risk negative-screening patients can undergo invasive or costly disease verification, the proposed method will still yield a valid (and cost- efficient) strategy for evaluating the statistical properties of the diagnostic test under consideration. Specifically, this proposal addresses the following four problems.
(Aim 1 :) The development of a novel doubly robust esti- mator for sensitivity, specificity, and positive and negative predictive values that can be used in the presence of verification bias. The estimators are doubly robust in the sense that the actual estimate is correct (i.e., consistent) in moderately large samples if either the model for true disease status or the model for verification status (but not necessarily both) is correct.
(Aim 2 :) To extend the methods developed in Aim 1 to tests and biomarkers that yield continuous or ordinal outcomes and where the area under a receiver operator characteristic curve is used to measure diagnostic accuracy.
(Aim 3 :) We 'reverse'our approach to develop a model for predicting dis- ease status, from patient's characteristic and diagnosis, in the presence of verification bias.
(Aim 4 :) To develop and freely distribute an assessable a software package that will implement these methods for statisticians and clinical researchers alike. Finally, the clinical implications of this proposed research are wide-ranging as much of medicine is diagnostic in nature. These methods have great potential to improve the statistical evaluation of diagnostic tests, which will in turn yield significant improvement in the ability of our physicians to make accurate diagnoses.
Screening tests for disease rely on commonly accepted measures that represent each test's gold standard to diagnose true disease status. The gold standard test may, however, be too expensive or too invasive to consider implementing for every subject in a study. Verification bias may arise when the verification of the disease status depends on the result of the screening test. This application proposes to develop novel doubly robust estimators to evaluate the accuracy and efficiency of the screening tests in the presence of verification bias.
|Chen, Qingxia; Nian, Hui; Zhu, Yuwei et al. (2016) Too many covariates and too few cases? - a comparative study. Stat Med 35:4546-4558|
|Chen, Qingxia; Zeng, Donglin; Ibrahim, Joseph G et al. (2015) Quantifying the average of the time-varying hazard ratio via a class of transformations. Lifetime Data Anal 21:259-79|
|Xu, Hua; Aldrich, Melinda C; Chen, Qingxia et al. (2015) Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. J Am Med Inform Assoc 22:179-91|
|Talbot, H Keipp; Nian, Hui; Zhu, Yuwei et al. (2015) Clinical effectiveness of split-virion versus subunit trivalent influenza vaccines in older adults. Clin Infect Dis 60:1170-5|
|Chen, Qingxia; Wu, Huiyun; Ware, Lorraine B et al. (2014) A Bayesian Approach for the Cox Proportional Hazards Model with Covariates Subject to Detection Limit. Int J Stat Med Res 3:32-43|
|Chen, Qingxia; May, Ryan C; Ibrahim, Joseph G et al. (2014) Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates. Stat Med 33:4560-76|
|Chen, Qingxia; Ibrahim, Joseph G (2014) A note on the relationships between multiple imputation, maximum likelihood and fully Bayesian methods for missing responses in linear regression models. Stat Interface 6:315-324|
|Zhang, Yuanye; Chen, Ming-Hui; Ibrahim, Joseph G et al. (2014) Bayesian gamma frailty models for survival data with semi-competing risks and treatment switching. Lifetime Data Anal 20:76-105|
|Chen, Qingxia; Chen, Ming-Hui; Ohlssen, David et al. (2013) Bayesian modeling and inference for clinical trials with partial retrieved data following dropout. Stat Med 32:4180-95|
|Smith, J C; Denny, J C; Chen, Q et al. (2013) Lessons learned from developing a drug evidence base to support pharmacovigilance. Appl Clin Inform 4:596-617|
Showing the most recent 10 out of 13 publications