Inflation of type I error rates due to differential misclassification in EHR-derived outcomes: Empirical illustration using breast cancer recurrence.

ABSTRACT:

Purpose

Many outcomes derived from electronic health records (EHR) not only are imperfect but also may suffer from exposure-dependent differential misclassification due to variability in the quality and availability of EHR data across exposure groups. The objective of this study was to quantify the inflation of type I error rates that can result from differential outcome misclassification.

Methods

We used data on gold-standard and EHR-derived second breast cancers in a cohort of women with a prior breast cancer diagnosis from 1993 to 2006 enrolled in Kaiser Permanente Washington. We simulated an exposure that was independent of the true outcome status. A surrogate outcome was then simulated with varying sensitivity and specificity according to exposure status. We estimated t

SUBMITTER: Chen Y 

PROVIDER: S-EPMC6716793 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature