Inverse probability weighting with error-prone covariates.

ABSTRACT: Inverse probability-weighted estimators are widely used in applications where data are missing due to nonresponse or censoring and in the estimation of causal effects from observational studies. Current estimators rely on ignorability assumptions for response indicators or treatment assignment and outcomes being conditional on observed covariates which are assumed to be measured without error. However, measurement error is common for the variables collected in many applications. For example, in studies of educational interventions, student achievement as measured by standardized tests is almost always used as the key covariate for removing hidden biases, but standardized test scores may have substantial measurement errors. We provide several expressions for a weighting function that can yi

SUBMITTER: McCaffrey DF 

PROVIDER: S-EPMC4006991 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature