Positivity violations in marginal structural survival models with time-dependent confounding: a simulation study on IPTW-estimator performance
arxiv(2024)
摘要
In longitudinal observational studies, marginal structural models (MSMs) are
a class of causal models used to analyze the effect of an exposure on the
(survival) outcome of interest while accounting for exposure-affected
time-dependent confounding. In the applied literature, inverse probability of
treatment weighting (IPTW) has been widely adopted to estimate MSMs. An
essential assumption for IPTW-based MSMs is the positivity assumption, which
ensures that each individual in the population has a non-zero probability of
receiving each exposure level within confounder strata. Positivity, along with
consistency, conditional exchangeability, and correct specification of the
weighting model, is crucial for valid causal inference through IPTW-based MSMs
but is often overlooked compared to confounding bias. Positivity violations can
arise from subjects having a zero probability of being exposed/unexposed
(strict violations) or near-zero probabilities due to sampling variability
(near violations). This article discusses the effect of violations in the
positivity assumption on the estimates from IPTW-based MSMs. Building on the
algorithms for simulating longitudinal survival data from MSMs by Havercroft
and Didelez (2012) and Keogh et al. (2021), systematic simulations under
strict/near positivity violations are performed. Various scenarios are explored
by varying (i) the size of the confounder interval in which positivity
violations arise, (ii) the sample size, (iii) the weight truncation strategy,
and (iv) the subject's propensity to follow the protocol violation rule. This
study underscores the importance of assessing positivity violations in
IPTW-based MSMs to ensure robust and reliable causal inference in survival
analyses.
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