IEU Seminar - Stijn Vansteelandt from Ghent University

18 December 2018, 1.00 PM - 18 December 2018, 2.00 PM

IEU Seminar - Stijn Vansteelandt from Ghent University

Title: Instrumental variable estimation of time-to-event endpoints

Abstract: Bias due to unobserved confounding can seldom be ruled out with certainty when estimating the causal effect of a nonrandomized treatment. The instrumental variable (IV) design offers, under certain assumptions, the opportunity to tame confounding bias, without directly observing all confounders. The IV approach is very well developed in the context of linear regression and also for certain generalized linear models with a nonlinear link function. However, IV methods are not as well developed for regression analysis with a censored survival outcome. In this talk, I will review recent developments on IV estimation of time-to-event endpoints under additive hazards models as well as Cox models. I will discuss two-stage methods (Tchetgen Tchetgen et al., 2015) that have the advantage of being simple, but the disadvantage of demanding parametric assumptions on the distribution of the exposure, amongst others. I will additionally discuss more efficient g-estimation approaches under additive hazards models (Martinussen et al., 2017) and Cox models (Martinussen, Sorensen and Vansteelandt, 2018) that are less demanding in terms of assumptions. Formal conditions are given justifying each strategy, and the methods are illustrated in a novel application to a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. In that context, I will also discuss how to accommodate the problem of left truncation and survivor bias which affects many Mendelian randomization studies of time-to-event endpoints (Vansteelandt, Walter and Tchetgen Tchetgen, 2018; Vansteelandt, Dukes and Martinussen, 2018).

References: Tchetgen Tchetgen, E.J., Walter, S., Vansteelandt, S., Martinussen, T. and Glymour, M. (2015). Instrumental variable estimation in a survival context. Epidemiology, 26, 402-410.

Martinussen, T., Vansteelandt, S., Tchetgen Tchetgen, E.J. and Zucker, D.M. (2017). Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models. Biometrics, 73, 1140-1149.

Vansteelandt, S., Walter, S. and Tchetgen Tchetgen, E. (2018). Eliminating Survivor Bias in Two-stage Instrumental Variable Estimators. Epidemiology, 29, 536-541.

Vansteelandt, S., Dukes, O. and Martinussen, M. (2018). Survivor bias in Mendelian randomisation analysis. Biostatistics, 19, 426-443.

Martinussen, T., Sorensen, D. and Vansteelandt, S. (in press). Instrumental variables estimation under a structural Cox model. Biostatistics.

Biography: Stijn Vansteelandt is Professor in the Department of Applied Mathematics, Computer Science and Statistics, and Professor of Statistical Methodology in the Department of Medical Statistics at the London School of Hygiene and Tropical Medicine. His primary research focuses on the development of semi-parametric statistical methods for causal inference. He has authored over 150 peer-reviewed publications in international journals on a variety of topics in biostatistics, epidemiology and medicine, such as the analysis of longitudinal and clustered data, missing data, mediation and moderation/interaction, instrumental variables, family-based genetic association studies, analysis of outcome-dependent samples and phylogenetic inference. He is Co-Editor of Biometrics, the leading flagship journal of the International Biometrics Society, and has previously served as Associate Editor for the journals Biometrics, Biostatistics, Epidemiology, Epidemiologic Methods and the Journal of Causal Inference.

All welcome