Background: In many applications of instrumental variable (IV) methods, the treatments of interest are intrinsically time-varying and outcomes of interest are failure time outcomes. A common example is Mendelian randomization (MR), which uses genetic variants as proposed IVs. In this article, we present a novel application of g-estimation of structural nested cumulative failure models (SNCFTMs), which can accommodate multiple measures of a time-varying treatment when modelling a failure time outcome in an IV analysis. Methods: A SNCFTM models the ratio of two conditional mean counterfactual outcomes at time k under two treatment strategies which differ only at an earlier time m. These models can be extended to accommodate inverse probability of censoring weights, and can be applied to case-control data. We also describe how the g-estimates of the SNCFTM parameters can be used to calculate marginal cumulative risks under nondynamic treatment strategies. We examine the performance of this method using simulated data, and present an application of these models by conducting an MR study of alcohol intake and endometrial cancer using longitudinal observational data from the Nurses’ Health Study. Results: Our simulations found that estimates from SNCFTMs which used an IV approach were similar to those obtained from SNCFTMs which adjusted for confounders, and similar to those obtained from the g-formula approach when the outcome was rare. In our data application, the cumulative risk of endometrial cancer from age 45 to age 72 under the “never drink” strategy (4.0%) was similar to that under the “always ½ drink per day” strategy (4.3%). Conclusions: SNCFTMs can be used to conduct MR and other IV analyses with time-varying treatments and failure time outcomes.
Bibliographical noteFunding Information:
The Nurses’ Health Study is supported by grants from the National Cancer Institute [UM1 CA186107; P01 CA87969; R01 CA49449]. J.S. is supported by a UCB Fellowship. S.A.S. is supported by a NWO/ZonMW Veni grant . I.D.V. is supported by the Brigham Research Institute through the Fund to Sustain Research Excellence. M.A.H. is supported by the National Institutes of Health (NIH) [R37 AI102634]. These funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation.
© 2021, The Author(s).