BayesPPD: An R Package for Bayesian Sample Size Determination Using the Power and Normalized Power Prior for Generalized Linear Models

Normalized Power Prior Bayesian Analysis Normalized Power Prior Bayesian Analysis

Power and Sample Size Determination for Linear Models

This presentation describes the steps involved in performing sample size analyses for a variety of linear models, both univariate and multivariate. As an analyst you must gather and synthesize the information needed, but you should be able to rely on the analytical tools to accommodate the numerous ways in which you can characterize and solve problems. Examples illustrate these principles and r...

Bayesian Sample size Determination for Longitudinal Studies with Continuous Response using Marginal Models

Introduction Longitudinal study designs are common in a lot of scientific researches, especially in medical, social and economic sciences. The reason is that longitudinal studies allow researchers to measure changes of each individual over time and often have higher statistical power than cross-sectional studies. Choosing an appropriate sample size is a crucial step in a successful study. A st...

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

On power and sample size calculations for likelihood ratio tests in generalized linear models.

A direct extension of the approach described in Self, Mauritsen, and Ohara (1992, Biometrics 48, 31-39) for power and sample size calculations in generalized linear models is presented. The major feature of the proposed approach is that the modification accommodates both a finite and an infinite number of covariate configurations. Furthermore, for the approximation of the noncentrality of the n...