ArticleBMC medical research methodology2025
Instrumental variable approaches for estimating time-varying treatment effects in comparative effectiveness research.
Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe increased availability of large-scale longitudinal data offers important opportunities to assess the causal effects of health interventions. In this setting, Instrumental Variable (IV) approaches have the potential to reduce the risk of bias from confounding due to unmeasured variables. However, there has been a lack of attention given to the development of IV approaches in settings when both the instrument and the potential confounders vary over time. In this paper we critically evaluate two instrumental variable approaches in time-varying settings.
methodsThe paper extends an existing g-estimation method that incorporates time-fixed IVs and compares it to an inverse probability weighting approach that incorporates time-varying IVs. A simulation study investigates the relative performance of these two approaches under varying scenarios. These methods are applied to a retrospective cohort from the US National Databank for Rheumatic Diseases, evaluating the sustained use of Adalimumab (Humira) versus other biologics on the health-related quality of life of patients with Rheumatoid Arthritis. Our case study considers physicians preference for Adalimumab as an instrument.
resultsThe g-estimation approach provided unbiased, precise estimates of treatment effects, across a wide range of scenarios, including weak IVs and complex time-varying confounding mechanisms. The performance of the weighting approach was reasonable in scenarios with a moderate or strong time-varying IV, but deteriorated with weak IV strength. Both methods suggest that sustained treatment with Adalimumab does not improve the health-related quality of life of rheumatoid patients, compared to other biologics, but the g-estimation approach led to narrower confidence intervals.
conclusionThe proposed IV-based g-estimation approach can be reliably used in the estimation of time-varying treatments if a valid time-varying IV is available. The weighting approach offers an accessible alternative but was found to work well only when the IVs are strongly associated with treatment assignment, which is relatively unlikely in real-world applications.
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