A validation sampling approach for consistent estimation of adverse drug reaction risk with misclassified right-censored survival data.
Stat Med
; 37(27): 3887-3903, 2018 11 30.
Article
em En
| MEDLINE
| ID: mdl-30084171
ABSTRACT
Patient electronic health records, viewed as continuous-time right-censored survival data, can be used to estimate adverse drug reaction risk. Temporal outcome misclassification may occur as a result of errors in follow-up. These errors can be due to a failure to observe the incidence time of the adverse event of interest (due to misdiagnosis or nonreporting, etc) or an actual misdiagnosis of a competing adverse event. As the misclassifying event is often unobservable in the original data, we apply an internal validation sampling approach to produce consistent estimation in the presence of such errors. We introduce a univariate survival model and a cause-specific hazards model in which misclassification may also manifest as a diagnosis of an alternate adverse health outcome other than that of interest. We develop a method of maximum likelihood estimation of the model parameters and establish consistency and asymptotic normality of the estimators using standard results. We also conduct simulation studies to numerically investigate the finite sample properties of these estimators and the impact of ignoring the misclassification error.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Análise de Sobrevida
/
Estudos de Amostragem
/
Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Canadá