Objective: To estimate the pretest probability of Cushing’s syndrome (CS) diagnosis by a Bayesian approach using intuitive clinical judgment. Materials and methods: Physicians were requested, in seven endocrinology meetings, to answer three questions: “Based on your personal expertise, after obtaining clinical history and physical examination, without using laboratorial tests, what is your prob...

Over the last decade the use of generalized linear models (GLMs) in actuarial statistics received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh & Nelder (1989). Traditional GLMs however model a sample of independent random variables. Since actuaries very often have repeated measurements or longitudinal data (i.e. repeated measurements over time)...

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 ...

Survival Model is widely used in medical field and biostatistics. This model can be used to identify the risk factors of an event and can handle the situation when risk factors change with time. Timing of an event frequently depends on the location (spatial) called as spatial survival model. In the development, survival modeling also included random effects models (frailty) to overcome the hete...

Bayesian methods for medical test accuracy are presented, beginning with the basic measures for tests with binary scores: true positive fraction, false positive fraction, positive predictive values, and negative predictive value. The Bayesian approach is taken because of its efficient use of prior information, and the analysis is executed with a Bayesian software package WinBUGS®. The ROC (rece...

ELICITOR is graphical elicitation software created to elicit normal prior distributions for a Bayesian logistic regression model. Motivated by a real need to include expert knowledge in presence–absence models in ecology, this research describes a synthesis of theory from statistics, psychology and ecology. The aim was to build elicitation software that would be user friendly to environmental s...

Over the last decade the use of generalized linear models (GLMs) in modelling actuarial data received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh & Nelder (1989). Standard GLMs however model a sample of independent random variables. Since actuaries very often have repeated measurements or longitudinal data (i.e. repeated measurements over time...

Health economic decision models are subject to various forms of uncertainty, including uncertainty about the parameters of the model and about the model structure. These uncertainties can be handled within a Bayesian framework, which also allows evidence from previous studies to be combined with the data. As an example, we consider a Markov model for assessing the cost-effectiveness of implanta...

Measurement error in sensitive question is pervasive, therefore, biasing the estimation of most statistical models. The objective of this paper is to correct for measurement error in the number of life-time sexual partners by treating it as a missing data problem and using multiple imputation technique to synthesize this underlying “true” attribute. Bayesian Poisson model with diffuse Gaussian ...

distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract This paper develops Bayesian analysis for Constant Stress Accelerated Life Test (CSALT) under Type-II censoring scheme. Failure times are assumed to distribute as the three-parameter Generalized Logistic ...