Estimation of disease prevalence, true positive rate, and false positive rate of two screening tests when disease verification is applied on only screen-positives: a hierarchical model using multi-center data.

Objectives: A model is proposed to estimate and compare cervical cancer screening test properties for third world populations when only subjects with a positive screen receive the gold standard test. Two fallible screening tests are compared, VIA and VILI.

Methods: We extend the model of Berry et al.

Bayesian inference of risk ratio of two proportions using a double sampling scheme.

We consider Bayesian point and interval estimation for a risk ratio of two proportion parameters using two independent samples of binary data subject to misclassification. In order to obtain model identifiability, we apply a double sampling scheme. For the identifiable model, we propose a Bayesian method for statistical inference for a two proportion risk ratio.

Bayesian sample-size determination for two independent Poisson rates.

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 sample-size determination methods, the Bayesian paradigm offers a wide variety of sample-size determination methodologies. Bayesian sample-size determination methods are becoming increasingly more popular in clinical trials because of their flexibility and easy interpretation inferences.

Performance and sample size requirements of Bayesian methods for binary outcomes in fixed-dose combination drug studies.

We develop a Bayesian analysis for the study of fixed-dose combinations of two or more drugs. The approach described here does not require knowledge of the dose-response relationships of the components or large sample approximations. We provide a procedure to estimate sample size in this context.

A Bayesian approach to adjust for diagnostic misclassification between two mortality causes in Poisson regression.

Response misclassification of counted data biases and understates the uncertainty of parameter estimators in Poisson regression models. To correct these problems, researchers have devised classical procedures that rely on asymptotic distribution results and supplemental validation data in order to estimate unknown misclassification parameters. We derive a new Bayesian Poisson regression procedure that accounts and corrects for misclassification for a count variable with two categories.

Bayesian estimation of intervention effect with pre- and post-misclassified binomial data.

We consider studies in which an enrolled subject tests positive on a fallible test. After an intervention, disease status is re-diagnosed with the same fallible instrument. Potential misclassification in the diagnostic test causes regression to the mean that biases inferences about the true intervention effect.

Bayesian inference for a correlated 2 x 2 table with a structural zero.

We develop a new Bayesian approach to interval estimation for both the risk difference and the risk ratio for a 2 x 2 table with a structural zero using Markov chain Monte Carlo (MCMC) methods. We also derive a normal approximation for the risk difference and a gamma approximation for the risk ratio. We then compare the coverage and interval width of our new intervals to the score-based intervals over various parameter and sample-size configurations.

Bayesian sample-size determination for inference on two binomial populations with no gold standard classifier.

We consider the impact of test properties on the required sample size for the Bayesian design problem for comparing two proportions with error-prone data. Specifically, we examine four cases: a single diagnostic test and two independent diagnostic tests, both when the test properties are identical across populations and when they differ. Interval-based and moment-based sample-size determination criteria are contrasted using Monte Carlo simulation methods.