Journal article
Logistic Box-Cox Regression to Assess the Shape and Median Effect under Uncertainty about Model Specification
31 Jan 2019
Abstract
The shape of the relationship between a continuous exposure variable and a
binary disease variable is often central to epidemiologic investigations. This
paper investigates a number of issues surrounding inference and the shape of
the relationship. Presuming that the relationship can be expressed in terms of
regression coefficients and a shape parameter, we investigate how well the
shape can be inferred in settings which might typify epidemiologic
investigations and risk assessment. We also consider a suitable definition of
the median effect of exposure, and investigate how precisely this can be
inferred. This is done both in the case of using a model acknowledging
uncertainty about the shape parameter and in the case of ignoring this
uncertainty and using a two-step method, where in step one we transform the
predictor and in step two we fit a simple linear model with transformed
predictor. All these investigations require a family of exposure-disease
relationships indexed by a shape parameter. For this purpose, we employ a
family based on the Box-Cox transformation.
Metrics
9 Record Views
Details
- Title
- Logistic Box-Cox Regression to Assess the Shape and Median Effect under Uncertainty about Model Specification
- Creators
- Li XingXuekui ZhangIgor BurstynPaul Gustafson
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Environmental and Occupational Health
- Identifiers
- 991019203440004721