Accounting for Misclassification in Electronic Health Records-derived Exposures Using Generalized Linear Finite Mixture Models
Exposures derived from EHRs may be misclassified, leading to biased estimates of their association with outcomes of interest: finite mixture models can correct biases with no loss of efficiency.
Source: RAND Research Health and Health Care - Category: Health Management Authors: Rebecca A. Hubbard; Eric A. Johnson; Jessica Chubak; Karen J. Wernli; Aruna Kamineni; Timothy Bogart; Carolyn M. Rutter Source Type: research
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