TY - JOUR
T1 - On adjustment for auxiliary covariates in additive hazard models for the analysis of randomized experiments
AU - Vansteelandt, S.
AU - Martinussen, Torben
AU - Tchetgen, E. J Tchetgen
PY - 2014/3
Y1 - 2014/3
N2 - We consider additive hazard models (Aalen, 1989) for the effect of a randomized treatment on a survival outcome, adjusting for auxiliary baseline covariates. We demonstrate that the Aalen least-squares estimator of the treatment effect parameter is asymptotically unbiased, even when the hazard's dependence on time or on the auxiliary covariates is misspecified, and even away from the null hypothesis of no treatment effect. We furthermore show that adjustment for auxiliary baseline covariates does not change the asymptotic variance of the estimator of the effect of a randomized treatment. We conclude that, in view of its robustness against model misspecification, Aalen least-squares estimation is attractive for evaluating treatment effects on a survival outcome in randomized experiments, and the primary reasons to consider baseline covariate adjustment in such settings could be interest in subgroup effects or the need to adjust for informative censoring or baseline imbalances. Our results also shed light on the robustness of Aalen least-squares estimators against model misspecification in observational studies.
AB - We consider additive hazard models (Aalen, 1989) for the effect of a randomized treatment on a survival outcome, adjusting for auxiliary baseline covariates. We demonstrate that the Aalen least-squares estimator of the treatment effect parameter is asymptotically unbiased, even when the hazard's dependence on time or on the auxiliary covariates is misspecified, and even away from the null hypothesis of no treatment effect. We furthermore show that adjustment for auxiliary baseline covariates does not change the asymptotic variance of the estimator of the effect of a randomized treatment. We conclude that, in view of its robustness against model misspecification, Aalen least-squares estimation is attractive for evaluating treatment effects on a survival outcome in randomized experiments, and the primary reasons to consider baseline covariate adjustment in such settings could be interest in subgroup effects or the need to adjust for informative censoring or baseline imbalances. Our results also shed light on the robustness of Aalen least-squares estimators against model misspecification in observational studies.
KW - Additive hazard model
KW - Model misspecification
KW - Randomized experiment
KW - Robustness
KW - Survival time
UR - http://www.scopus.com/inward/record.url?scp=84897745953&partnerID=8YFLogxK
U2 - 10.1093/biomet/ast045
DO - 10.1093/biomet/ast045
M3 - Journal article
AN - SCOPUS:84897745953
VL - 101
SP - 237
EP - 244
JO - Biometrika
JF - Biometrika
SN - 0006-3444
IS - 1
ER -