An analytic framework for exploring sampling and observation process biases in genome and phenome-wide association studies using electronic health records.
Stat Med
; 39(14): 1965-1979, 2020 06 30.
Article
em En
| MEDLINE
| ID: mdl-32198773
ABSTRACT
Large-scale association analyses based on observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, challenges due to nonprobability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. The extent of the bias introduced by ignoring these factors is not well-characterized. In this paper, we develop an analytic framework for characterizing the bias expected in disease-gene association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis approach, this framework can be used to obtain plausible values for parameters of interest given summary results from standard analysis. We develop an online tool for performing this sensitivity analysis. Simulations demonstrate promising properties of the proposed method. We apply our approach to study bias in disease-gene association studies using electronic health record data from the Michigan Genomics Initiative, a longitudinal biorepository effort within The University Michigan health system.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Estudo de Associação Genômica Ampla
/
Registros Eletrônicos de Saúde
Tipo de estudo:
Risk_factors_studies
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos