Modeling time-varying exposure using inverse probability of treatment weights

Journal article

For estimating the causal effect of treatment exposure on the occurrence of adverse events, inverse pro bability weights (IPW) can be used in marginal structural models to correct for time-dependent confounding. The R package ipw allows IPW estimation by modeling the relationship between the exposure and confounders via several regression models, among which is the Cox model. For right-censored data and time-dependent exposures such as treatment switches, the ipw package allows a single switch, assuming that patients are treated once and for all. However, to accommodate multiple switches, we extend this package by implementing a function that allows for multiple and intermittent exposure status in the estimation of IPW using a survival model. This extension allows for the whole exposure treatment trajectory in the estimation of IPW. The impact of the estimated weights on the estimated causal effect, with both methods, is assessed in a simulation study. Then, the function is illustrated on a real dataset from a nationwide prospective observational cohort including patients with inflammatory bowel disease. In this study, patients received one or multiple medications (thiopurines, methotrexate, and anti-TNF) over time. We used a Cox marginal structural model to assess the effect of thiopurines exposure on the cause-specific hazard for cancer incidence considering other treatments as confounding factors. To this end, we used our extended function which is available online in the Supporting Information.

Authors: Grafféo, N; Latouche, A; Geskus, R; Chevret, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Biometrical Journal More from this journal
• Volume: 60
• Issue: 2
• Pages: 323-332
• Publication date: 2017-12-17
• Acceptance date: 2017-10-30
• DOI: 10.1002/bimj.201600223
• EISSN: 1521-4036
• ISSN: 0323-3847
• Keywords: R package; treatment sSwitch; causal inference; marginal structural models; inverse probability weighting