Adjusting for Confounding by Neighborhood Using a Proportional Odds Model and Complex Survey Data

Adjusting for confounding by neighborhood using a proportional odds model and complex survey data.

In social epidemiology, an individual's neighborhood is considered to be an important determinant of health behaviors, mediators, and outcomes. Consequently, when investigating health disparities, researchers may wish to adjust for confounding by unmeasured neighborhood factors, such as local availability of health facilities or cultural predispositions. With a simple random sample and a binary...

Practice of Epidemiology Adjusting for Confounding by Neighborhood Using a Proportional Odds Model and Complex Survey Data

In social epidemiology, an individual’s neighborhood is considered to be an important determinant of health behaviors, mediators, and outcomes. Consequently, when investigating health disparities, researchers may wish to adjust for confounding byunmeasured neighborhood factors, such as local availability of health facilities or cultural predispositions. Withasimple randomsampleandabinaryoutcome...

Adjusting for confounding by neighborhood using generalized linear mixed models and complex survey data.

When investigating health disparities, it can be of interest to explore whether adjustment for socioeconomic factors at the neighborhood level can account for, or even reverse, an unadjusted difference. Recently, we proposed new methods to adjust the effect of an individual-level covariate for confounding by unmeasured neighborhood-level covariates using complex survey data and a generalization...

Model Diagnostics for Proportional and Partial Proportional Odds Models

Variable selection for proportional odds model.

In this paper we study the problem of variable selection for the proportional odds model, which is a useful alternative to the proportional hazards model and might be appropriate when the proportional hazards assumption is not satisfied. We propose to fit the proportional odds model by maximizing the marginal likelihood subject to a shrinkage-type penalty, which encourages sparse solutions and ...