Multiplicative versus additive modelling of causal effects using instrumental variables for survival outcomes - a comparison.

ABSTRACT: Instrumental variables (IVs) methods have recently gained popularity since, under certain assumptions, they may yield consistent causal effect estimators in the presence of unmeasured confounding. Existing simulation studies that evaluate the performance of IV approaches for time-to-event outcomes tend to consider either an additive or a multiplicative data-generating mechanism (DGM) and have been limited to an exponential constant baseline hazard model. In particular, the relative merits of additive versus multiplicative IV models have not been fully explored. All IV methods produce less biased estimators than naïve estimators that ignore unmeasured confounding, unless the IV is very weak and there is very little unmeasured confounding. However, the mean squared error of IV estimators may

SUBMITTER: John ER 

PROVIDER: S-EPMC11800712 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature