Aihua Liu1,2,3, Michal Abrahamowicz1,2, Jack Siemiatycki3,4. 1. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada. 2. Division of Clinical Epidemiology, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada. 3. Division of Population Health, CRCHUM Research Center, Montreal, Quebec, Canada. 4. Department of Social and Preventive Medicine, University of Montreal, Montreal, Quebec, Canada.
Abstract
PURPOSE: Pharmaco-epidemiology increasingly investigates drug-drug or drug-covariate interactions. Yet, conditions for confounding of interactions have not been elucidated. We explored the conditions under which the estimates of interactions in logistic regression are affected by confounding bias. METHODS: We rely on analytical derivations to investigate the conditions and then use simulations to confirm our analytical results and to quantify the impact of selected parameters on the bias of the interaction estimates. RESULTS: Failure to adjust for a risk factor U results in a biased estimate of the interaction between exposures E1 and E2 on a binary outcome Y if the association between U and E1 varies depending on the value of E2. The resulting confounding bias increases with increase in the following: (i) prevalence of confounder U; (ii) strength of U-Y association; and (iii) heterogeneity in the association of E1 with U across the strata of E2. A variable that is not a confounder for the main effects of E1 and E2 may still act as an important confounder for their interaction. CONCLUSIONS: Studies of interactions should attempt to identify-as potential confounders-those risk factors whose associations with one of the exposures in the interaction term may be modified by the other exposure.
PURPOSE: Pharmaco-epidemiology increasingly investigates drug-drug or drug-covariate interactions. Yet, conditions for confounding of interactions have not been elucidated. We explored the conditions under which the estimates of interactions in logistic regression are affected by confounding bias. METHODS: We rely on analytical derivations to investigate the conditions and then use simulations to confirm our analytical results and to quantify the impact of selected parameters on the bias of the interaction estimates. RESULTS: Failure to adjust for a risk factor U results in a biased estimate of the interaction between exposures E1 and E2 on a binary outcome Y if the association between U and E1 varies depending on the value of E2. The resulting confounding bias increases with increase in the following: (i) prevalence of confounder U; (ii) strength of U-Y association; and (iii) heterogeneity in the association of E1 with U across the strata of E2. A variable that is not a confounder for the main effects of E1 and E2 may still act as an important confounder for their interaction. CONCLUSIONS: Studies of interactions should attempt to identify-as potential confounders-those risk factors whose associations with one of the exposures in the interaction term may be modified by the other exposure.
Authors: Harsukh Benipal; Anne Holbrook; J Michael Paterson; James Douketis; Gary Foster; Lehana Thabane Journal: BMJ Open Date: 2020-09-22 Impact factor: 2.692