Addressing errors in multiple variables using generalized raking and cumulative probability models
Routinely collected data, such as electronic health record (EHR) data, are frequently used for biomedical research, but these data are prone to errors, which can bias study findings. Validating data in subsamples of records can reduce bias, and the efficiency of estimates can be improved by incorporating in analyses both the error-prone data available on the entire cohort and the validated data available on the subsample. One approach to incorporate both data sources is with generalized raking, which calibrates validation sampling weights using error-prone data from the entire cohort. Motivated by an EHR study of maternal weight gain during pregnancy with a validation subsample, we develop and illustrate generalized raking techniques for cumulative probability models (CPMs). CPMs are robust, rank-based and semiparametric models for continuous, ordinal, or mixed type outcome data. We develop efficient generalized raking estimators for CPMs, evaluate their performance relative to competing methods, and demonstrate the utility and strengths of generalized raking with CPMs in a study that examines factors associated with weight gain during pregnancy.