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Testing Hypotheses About Medical Test Accuracy: Considerations For Design And Inference, Adam J. Branscum, Dunlei Cheng, J Jack Lee
Testing Hypotheses About Medical Test Accuracy: Considerations For Design And Inference, Adam J. Branscum, Dunlei Cheng, J Jack Lee
Dunlei Cheng
Developing new medical tests and identifying single biomarkers or panels of biomarkers with superior accuracy over existing classifiers promotes lifelong health of individuals and populations. Before a medical test can be routinely used in clinical practice, its accuracy within diseased and non-diseased populations must be rigorously evaluated. We introduce a method for sample size determination for studies designed to test hypotheses about medical test or biomarker sensitivity and specificity. We show how a sample size can be determined to guard against making type I and/or type II errors by calculating Bayes factors from multiple data sets simulated under null and/or …
Sample Size Calculations For Roc Studies: Parametric Robustness And Bayesian Nonparametrics, Dunlei Cheng, Adam J. Branscum, Wesley O. Johnson
Sample Size Calculations For Roc Studies: Parametric Robustness And Bayesian Nonparametrics, Dunlei Cheng, Adam J. Branscum, Wesley O. Johnson
Dunlei Cheng
Methods for sample size calculations in ROC studies often assume independent normal distributions for test scores among the diseased and non-diseased populations. We consider sample size requirements under the default two-group normal model when the data distribution for the diseased population is either skewed or multimodal. For these two common scenarios we investigate the potential for robustness of calculated sample sizes under the mis-specified normal model and we compare to sample sizes calculated under a more flexible nonparametric Dirichlet process mixture model. We also highlight the utility of flexible models for ROC data analysis and their importance to study design. …
Accounting For Response Misclassification And Covariate Measurement Error Improves Powers And Reduces Bias In Epidemiologic Studies, Dunlei Cheng, Adam J. Branscum, James D. Stamey
Accounting For Response Misclassification And Covariate Measurement Error Improves Powers And Reduces Bias In Epidemiologic Studies, Dunlei Cheng, Adam J. Branscum, James D. Stamey
Dunlei Cheng
Purpose: To quantify the impact of ignoring misclassification of a response variable and measurement error in a covariate on statistical power, and to develop software for sample size and power analysis that accounts for these flaws in epidemiologic data. Methods: A Monte Carlo simulation-based procedure is developed to illustrate the differences in design requirements and inferences between analytic methods that properly account for misclassification and measurement error to those that do not in regression models for cross-sectional and cohort data. Results: We found that failure to account for these flaws in epidemiologic data can lead to a substantial reduction in …
A Bayesian Approach To Sample Size Determination For Studies Designed To Evaluate Continuous Medical Tests, Dunlei Cheng, Adam J. Branscum, James D. Stamey
A Bayesian Approach To Sample Size Determination For Studies Designed To Evaluate Continuous Medical Tests, Dunlei Cheng, Adam J. Branscum, James D. Stamey
Dunlei Cheng
We develop a Bayesian approach to sample size and power calculations for cross-sectional studies that are designed to evaluate and compare continuous medical tests. For studies that involve one test or two conditionally independent or dependent tests, we present methods that are applicable when the true disease status of sampled individuals will be available and when it will not. Within a hypothesis testing framework, we consider the goal of demonstrating that a medical test has area under the receiver operating characteristic (ROC) curve that exceeds a minimum acceptable level or another relevant threshold, and the goals of establishing the superiority …
Bayesian Approach To Average Power Calculations For Binary Regression Models With Misclassified Outcomes, Dunlei Cheng, James D. Stamey, Adam J. Branscum
Bayesian Approach To Average Power Calculations For Binary Regression Models With Misclassified Outcomes, Dunlei Cheng, James D. Stamey, Adam J. Branscum
Dunlei Cheng
We develop a simulation-based procedure for determining the required sample size in binomial regression risk assessment studies when response data are subject to misclassification. A Bayesian average power criterion is used to determine a sample size that provides high probability, averaged over the distribution of potential future data sets, of correctly establishing the direction of association between predictor variables and the probability of event occurrence. The method is broadly applicable to any parametric binomial regression model including, but not limited to, the popular logistic, probit, and complementary log-log models. We detail a common medical scenario wherein ascertainment of true disease …