In this chapter we continue the discussion of diagnostic testing by considering instruments that are based on continuous outcomes. We also introduce additional diagnostic information in the form of covariates and additional tests. Our primary goal is to generalize the notion of PVP and PVN to a predictive probability of infection/disease conditional on all available diagnostic information. In a...

Markov chain Monte Carlo (MCMC) techniques represent an extremely flexible and powerful approach to Bayesian modeling. This work illustrates the application of such techniques to time-dependent reliability of components with repair. The WinBUGS package is used to illustrate, via examples, how Bayesian techniques can be used for parametric statistical modeling of time-dependent component reliabi...

Rare events population (φ) is hard-to-reach, sparsely distributed and clustered; an Adaptive Cluster Sampling (ACS) the design to collect information from φ. Researchers Policy Makers have modelled φ in ACS with homogeneity assumptions. This study heterogeneity among networks within network units. Data International Institute of Tropical Agriculture on Culcasia Scandens, understory plant simula...

Background: Cost-effectiveness analysis often requires information on the effectiveness of interventions on multiple outcomes, and commonly these take the form of competing risks. Nevertheless, methods for synthesis of randomized controlled trials with competing risk outcomes are limited. Objective: The aim of this study was to develop and illustrate flexible evidence synthesis methods for tria...

Smoothing of observed measures of healthcare provider performance is well known to lead to advantages in terms of predictive ability. However, with routinely collected longitudinal data there is the opportunity to smooth either between units, across time or both. Hierarchical generalized linear models with time as a covariate and hierarchical time series models each result in such two-way or ‘b...

Ischemic heart disease (or Coronary Artery Disease) is the most common cause of death in various countries, characterized by reduced blood supply to heart. Statistical models make an impact evaluating risk factors that are responsible for mortality and morbidity during IHD (Ischemic disease). In general, geometric or Poisson distributions can underestimate zero-count probability hence it diffic...

The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain convergence. Model comparison and fit issues in the ...