Reverse causation biases weighted cumulative exposure model estimates, but can be investigated in sensitivity analyses.
J Clin Epidemiol
; 161: 46-52, 2023 09.
Article
en En
| MEDLINE
| ID: mdl-37437786
ABSTRACT
OBJECTIVES:
To examine the effects of reverse causation on estimates from the weighted cumulative exposure (WCE) model that is used in pharmacoepidemiology to explore drug-health outcome associations, and to identify sensitivity analyses for revealing such effects. STUDY DESIGN ANDSETTING:
314,099 patients with diabetes under Clalit Health Services, Israel, were followed over 2002-2012. The association between metformin and pancreatic cancer (PC) was explored using a WCE model within the framework of discrete-time Cox regression. We used computer simulations to explore the effects of reverse causation on estimates of a WCE model and to examine sensitivity analyses for revealing and adjusting for reverse causation. We then applied those sensitivity analyses to our data.RESULTS:
Simulation demonstrated bias in the weighted cumulative exposure model and showed that sensitivity analysis could reveal and adjust for these biases. In our data, a positive association was observed (hazard ratio (HR) = 3.24, 95% confidence interval (CI) 2.24-4.73) with metformin exposure in the previous 2 years. After applying sensitivity analysis, assuming reverse causation operated up to 4 years before cancer diagnosis, the association between metformin and PC was no longer apparent.CONCLUSION:
Reverse causation can cause substantial bias in the WCE model. When suspected, sensitivity analyses based on causal analysis are advocated.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Diabetes Mellitus
/
Metformina
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Año:
2023
Tipo del documento:
Article