A strategy for validation of variables derived from large-scale electronic health record data.

ABSTRACT:

Purpose

Standardized approaches for rigorous validation of phenotyping from large-scale electronic health record (EHR) data have not been widely reported. We proposed a methodologically rigorous and efficient approach to guide such validation, including strategies for sampling cases and controls, determining sample sizes, estimating algorithm performance, and terminating the validation process, hereafter referred to as the San Diego Approach to Variable Validation (SDAVV).

Methods

We propose sample size formulae which should be used prior to chart review, based on pre-specified critical lower bounds for positive predictive value (PPV) and negative predictive value (NPV). We also propose a stepwise strategy for iterative algorithm development/validation cycles, updating sampl

SUBMITTER: Liu L 

PROVIDER: S-EPMC9615095 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature