Browsing by Subject "Misclassification."
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Item Bayesian and likelihood-based interval estimation for the risk ratio using double sampling with misclassified binomial data.(2011-01-05T19:44:19Z) Rahardja, Dewi Gabriela.; Young, Dean M.; Statistical Sciences.; Baylor University. Dept. of Statistical Sciences.We consider the problem of point and interval estimation for the risk ratio using double sampling with two-sample misclassified binary data. For such data, it is well-known that the actual data model is unidentifiable. To achieve model identifiability, then, we obtain additional data via a double-sampling scheme. For the Bayesian paradigm, we devise a parametric, straight-forward algorithm for sampling from the joint posterior density for the parameters, given the data. We then obtain Bayesian point and interval estimators of the risk ratio of two-proportion parameters. We illustrate our algorithm using a real data example and conduct two Monte Carlo simulation studies to demonstrate that both the point and interval estimators perform well. Additionally, we derive three likelihood-based confidence intervals (CIs) for the risk ratio. Specifically, we first obtain closed-form maximum likelihood estimators (MLEs) for all parameters. We then derive three CIs for the risk ratio: a naive Wald interval, a modified Wald interval, and a Fieller-type interval. For illustration purposes, we apply the three CIs to a real data example. We also perform various Monte Carlo simulation studies to assess and compare the coverage probabilities and average lengths of the three CIs. A modified Wald CI performs the best of the three CIs and has near-nominal coverage probabilities.Item Bayesian approaches for design of psychometric studies with underreporting and misclassification.(2013-05-15) Falley, Brandi.; Stamey, James D.; Statistical Sciences.; Baylor University. Dept. of Statistical Sciences.Measurement error problems in binary regression are of considerable interest among researchers, especially in epidemiological studies. Misclassification can be considered a special case of measurement error specifically for the situation when measurement is the categorical classification of items. Bayesian methods offer practical advantages for the analysis of epidemiological data including the possibility of incorporating relevant prior scientific information and the ability to make inferences that do not rely on large sample assumptions. Because of the high cost and time constraints for clinical trials, researchers often need to determine the smallest sample size that provides accurate inferences for a parameter of interest. Although most experimenters have employed frequentist methods, the Bayesian paradigm offers a wide variety of methodologies and are becoming increasingly more popular in clinical trials because of their flexibility and their ease of interpretation. We will simultaneously estimate efficacy and safety where the safety variable is subject to underreporting. We propose a Bayesian sample size determination method to account for the underreporting and appropriately power the study. We will allow efficacy and safety to be independent, as well as dependent using a regression model. For both models, we will allow the safety variable to be underreported.Item Bayesian methods to estimate the accuracy of diagnostic tests in meta-analysis models.(2014-09-05) Knorr, Jack S.; Seaman, John Weldon, 1956-; Stamey, James D.; Statistical Sciences.; Baylor University. Dept. of Statistical Sciences.With the growing number of studies looking at the performance of diagnostic tests, combining the studies into a meta-analysis becomes an important and increasingly viable area of statistics, especially within the medical field. We begin by developing a hierarchical Bayesian prior structure to estimate prevalences and misclassi cation rates for a single diagnostic test. We provide the results from a simulation study which shows that this model has desirable operating characteristics. We then adapt the model to analyze a scenario in which the collected studies come from two populations, one of which having a known higher prevalence of the trait of interest. Next, we adapt the model from a previous article which constructs an estimate to the summary receiver operating characteristics curve for a diagnostic test. We develop a procedure to elicit prior distributions from an expert and to provide feedback once the priors are obtained. The model is demonstrated in detail and results are reported. We conclude by finding the necessary sample size to compare two diagnostic tests while using a meta-analysis to help power the study. Here we consider a brand new diagnostic test being compared to two established tests in a network meta-analysis. We present a model that provides a sample size needed to compare sensitivities and specificities in a reasonable computing time.Item Bayesian topics in biostatistics : treatment selection, sample size, power, and misclassification.(2011-12-19) Doty, Tave Parker.; Tubbs, Jack Dale.; Stamey, James D.; Statistical Sciences.; Baylor University. Dept. of Statistical Sciences.Bayesian methodology is implemented to investigate three problems in biostatistics. The first problem considers using biomarkers to select optimal treatments for individual patients. A Bayesian adaptation of the selection impact (SI) curve developed by Pepe and Song (2004) is investigated. The second problem considers a Bayesian approach for determining specific sample sizes to achieve a desired range of power for fixed-dose combination drug trials. Sidik and Jonkman (2003) developed a sample size formula using the intersection-union test for testing the efficacy of combination drugs. Our results are compared to their frequentist approach. The third problem considers response misclassification in fixed-dose combination drug trials under two scenarios: when the sensitivity and specificity are known, and when the sensitivity and specificity are unknown but have specified informative prior structures.