TY - JOUR
T1 - Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models.
JF - Biometrics
Y1 - 2017
A1 - Martinussen, Torben
A1 - Vansteelandt, Stijn
A1 - Eric J. Tchetgen Tchetgen
A1 - David M Zucker
KW - Survey Methodology
AB - The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this article, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study.
VL - 73
IS - 4
U1 - http://www.ncbi.nlm.nih.gov/pubmed/28493302?dopt=Abstract
ER -
TY - JOUR
T1 - Instrumental variable estimation in a survival context
JF - Epidemiology
Y1 - 2015
A1 - Eric J. Tchetgen Tchetgen
A1 - Stefan Walter
A1 - Vansteelandt, Stijn
A1 - Martinussen, Torben
A1 - M. Maria Glymour
KW - Health Conditions and Status
KW - Methodology
AB - 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 article, we develop the IV approach for regression analysis in a survival context, primarily under an additive hazards model, for which we describe 2 simple methods for estimating causal effects. The first method is a straightforward 2-stage regression approach analogous to 2-stage least squares commonly used for IV analysis in linear regression. In this approach, the fitted value from a first-stage regression of the exposure on the IV is entered in place of the exposure in the second-stage hazard model to recover a valid estimate of the treatment effect of interest. The second method is a so-called control function approach, which entails adding to the additive hazards outcome model, the residual from a first-stage regression of the exposure on the IV. 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. We also establish that analogous strategies can also be used under a proportional hazards model specification, provided the outcome is rare over the entire follow-up.
PB - 26
VL - 26
IS - 3
N1 - Times Cited: 1 0 1
U4 - Methodology/instrumental variable/statistical analysis/Diabetes/Mortality/Proportional Hazards Models
ER -