A random effects meta-analysis model with Box-Cox transformation

A random effects meta-analysis model with Box-Cox transformation

BACKGROUND In a random effects meta-analysis model, true treatment effects for each study are routinely assumed to follow a normal distribution. However, normality is a restrictive assumption and the misspecification of the random effects distribution may result in a misleading estimate of overall mean for the treatment effect, an inappropriate quantification of heterogeneity across studies and...

Accelerated Life Testing Model Building with Box-cox Transformation

In accelerated life testing, the nominal life time is often related to stress levels by an acceleration equation. Three particular models that have been used frequently in the past are the power law model, the Arrhenius model and the Eyring model. In this paper we suggest choosing a model from a model family which includes the three particular models as special cases. This family is deened by a...

Box-Cox Transformation: An Overview Box-Cox Transformations: An Overview

A nonlinear isobologram model with Box-Cox transformation to both sides for chemical mixtures.

The linear logistical isobologram is a commonly used and powerful graphical and statistical tool for analyzing the combined effects of simple chemical mixtures. In this paper a nonlinear isobologram model is proposed to analyze the joint action of chemical mixtures for quantitative dose-response relationships. This nonlinear isobologram model incorporates two additional new parameters, Ymin and...

A Bayesian Semi-Parametric Model for Random Effects Meta-Analysis

In meta-analysis there is an increasing trend to explicitly acknowledge the presence of study variability through random effects models. That is, one assumes that for each study, there is a study-specific effect and one is observing an estimate of this latent variable. In a random effects model, one assumes that these study-specific effects come from some distribution, and one can estimate the ...