Bayesian small-sample estimation of a multinomial distribution based on its apparent distribution and a matrix of misclassification probabilities is considered.

Bayesian small-sample estimation of misclassified multinomial data

We consider the problem of variable selection for logistic regression when the dependent variable is measured imperfectly, under both differential and non-differential misclassification. An MCMC sa...

Bayesian model selection for logistic regression with misclassified outcomes

The Poisson assumption is popular when data arises in the form of counts. In many applications such counts are fallible. Little research has been done on the Poisson distribution when both false positives and false negatives are present. We present a model in this paper that corrects for misclassification of count data. Bayesian estimators are developed. We provide the actual posterior distributions via integration. Markov Chain Monte Carlo results, which are more convenient for large sample sizes, are utilized for inference.

Estimation of Poisson rates with misclassified counts

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. We consider an application in which Strongyloides infections are compared in two populations.

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

Methodological aspects of multivariate statistical models to describe and to assess data from computer-analyzed corneal topographers (CACT) are considered. The data generated by repeated curvature mappings of calibrated steel balls are discussed in detail with the objective of formulating the basic questions and suggesting directions to assess data from clinical applications of CACT. The interpretation of seemingly straightforward concepts such as accuracy and precision is revisited with the objective of understanding its meaning in future clinical and experimental applications of CACT. Some of the statistical problems related to the analysis of corneal astigmatism based on CACT data are also discussed.

Multivariate assessment of computer-analyzed corneal topographers

Bayesian analysis is applied to the number of cases screened positive to estimate the disease prevalence and to predict the number of future cases with disease. The analysis makes use of additional experimental information about the test’s sensitivity and specificity and of prior information on the prevalence of disease. Prior and posterior probability distributions of disease prevalence are conjugate mixtures of Beta densities and can be expressed in exact algebraic form.

Bayesian analysis of prevalence from the results of small screening samples

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Bayesian analysis of prevalence from the results of small screening samples