Author(s):
1. Lacey Gunter:
Gunter Statistical Consulting, Provo, UT 84604, USA
2. Michael Chernick:
Lankenau Institute for Medical Research, Wynnewood, PA 19096, USA
3. Jiajing Sun:
Management School, The University of Liverpool, Liverpool L69 7ZH, United Kingdom
Abstract:
In this paper, we compare the method of Gunter et al. (2011) for variable selection in treatment comparison analysis (an approach to regression analysis where treatment-covariate interactions are deemed important) with a simple stepwise selection method that we introduce. The stepwise method has several advantages, most notably its generalization to regression models that are not necessarily linear, its simplicity and its intuitive nature. We show that the new simple method works surprisingly well compared to the more complex method when compared in the linear regression framework. We use four generative models (explicitly detailed in the paper) for the simulations and compare spuriously identified interactions and where applicable (generative models 3 and 4) correctly identified interactions. We also apply the new method to logistic regression and Poisson regression and illustrate its performance in Table 2 in the paper. The simple method can be applied to other types of regression models including various other generalized linear models, Cox proportional hazard models and nonlinear models.
Page(s):
363-380
DOI:
DOI not available
Published:
Journal: Pakistan Journal of Statistics and Operation Research, Volume: 7, Issue: 2, Year: 2011
Keywords:
Qualitative interactions
,
Variable selection
,
Stepwise selection
,
Treatmentcovariate interactions
,
Prescriptive variables
References:
References are not available for this document.
Citations
Citations are not available for this document.