Overall Exposure Effects Estimation for Tobit Regression Models

• Activity Number: 410
• Type: Topic Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Wei Wang*+ and Michael Griswold
• Companies: University of Mississippi Medical Center and University of Mississippi Medical Center
• Keywords: Tobit model ; marginalized model ; model-predicted values ; overall exposure effects

Abstract

The Tobit model, also known as a censored regression model to account for left- and/or right-censoring in the dependent variable, has been used in many areas of applications, including dental health, medical research and economics. The reported Tobit model coefficient allows estimation and inference of an exposure effect on the latent dependent variable. However, this model does not directly provide overall exposure effects estimation on the original outcome scale. We propose a marginalized approach to model the truncated dependent variable mean directly, allowing direct estimation of a homogeneous exposure effect. A second approach uses model-predicted values for each person in a designated reference group under different exposure statuses to estimate covariate-adjusted overall exposure effects. Both methods allow easier translation of exposure effects than the traditional Tobit model. The methods are applied to a clinical trial assessing subjective comfort scores of Test and Control Contact Lenses. Simulation studies show good bias and robustness properties for both approaches. Robustness diminishes when there is exposure group imbalance for a covariate with a substantial effect.