Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models
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Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models. / Martinussen, Torben; Vansteelandt, Stijn; Tchetgen Tchetgen, Eric J.; Zucker, David M.
In: Biometrics, Vol. 73, No. 4, 12.2017, p. 1140-1149.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models
AU - Martinussen, Torben
AU - Vansteelandt, Stijn
AU - Tchetgen Tchetgen, Eric J.
AU - Zucker, David M.
N1 - © 2017, The International Biometric Society.
PY - 2017/12
Y1 - 2017/12
N2 - 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.
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.
U2 - 10.1111/biom.12699
DO - 10.1111/biom.12699
M3 - Journal article
C2 - 28493302
VL - 73
SP - 1140
EP - 1149
JO - Biometrics
JF - Biometrics
SN - 0006-341X
IS - 4
ER -
ID: 195962657