On the role of marginal confounder prevalence - implications for the high-dimensional propensity score algorithm.

ABSTRACT:

Purpose

The high-dimensional propensity score algorithm attempts to improve control of confounding in typical treatment effect studies in pharmacoepidemiology and is increasingly being used for the analysis of large administrative databases. Within this multi-step variable selection algorithm, the marginal prevalence of non-zero covariate values is considered to be an indicator for a count variable's potential confounding impact. We investigate the role of the marginal prevalence of confounder variables on potentially caused bias magnitudes when estimating risk ratios in point exposure studies with binary outcomes.

Methods

We apply the law of total probability in conjunction with an established bias formula to derive and illustrate relative bias boundaries with respect to ma

SUBMITTER: Schuster T 

PROVIDER: S-EPMC5072887 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature