Nonparametric efficient causal estimation of the intervention-specific expected number of recurrent events with continuous-time targeted maximum likelihood and highly adaptive lasso estimation
arxiv(2024)
摘要
Longitudinal settings involving outcome, competing risks and censoring events
occurring and recurring in continuous time are common in medical research, but
are often analyzed with methods that do not allow for taking post-baseline
information into account. In this work, we define statistical and causal target
parameters via the g-computation formula by carrying out interventions directly
on the product integral representing the observed data distribution in a
continuous-time counting process model framework. In recurrent events settings
our target parameter identifies the expected number of recurrent events also in
settings where the censoring mechanism or post-baseline treatment decisions
depend on past information of post-baseline covariates such as the recurrent
event process. We propose a flexible estimation procedure based on targeted
maximum likelihood estimation coupled with highly adaptive lasso estimation to
provide a novel approach for double robust and nonparametric inference for the
considered target parameter. We illustrate the methods in a simulation study.
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