Continuous-time targeted minimum loss-based estimation of intervention-specific mean outcomes

TL;DR

This paper extends the TMLE framework to continuous-time settings for estimating intervention-specific mean outcomes, enabling analysis of time-varying effects with subject-specific event timings.

Contribution

It introduces a continuous-time TMLE method with a novel influence curve derivation and an adaptive lasso approach for nuisance parameter estimation, improving flexibility and efficiency.

Findings

Estimator solves the efficient influence curve equation

Asymptotic linearity and efficiency established under minimal conditions

Applicable to complex longitudinal data with irregular observation times

Abstract

This paper studies the generalization of the targeted minimum loss-based estimation (TMLE) framework to estimation of effects of time-varying interventions in settings where both interventions, covariates, and outcome can happen at subject-specific time-points on an arbitrarily fine time-scale. TMLE is a general template for constructing asymptotically linear substitution estimators for smooth low-dimensional parameters in infinite-dimensional models. Existing longitudinal TMLE methods are developed for data where observations are made on a discrete time-grid. We consider a continuous-time counting process model where intensity measures track the monitoring of subjects, and focus on a low-dimensional target parameter defined as the intervention-specific mean outcome at the end of follow-up. To construct our TMLE algorithm for the given statistical estimation problem we derive an expression for the efficient influence curve and represent the target parameter as a functional of intensities and conditional expectations. The high-dimensional nuisance parameters of our model are estimated and updated in an iterative manner according to separate targeting steps for the involved intensities and conditional expectations. The resulting estimator solves the efficient influence curve equation. We state a general efficiency theorem and describe a highly adaptive lasso estimator for nuisance parameters that allows us to establish asymptotic linearity and efficiency of our estimator under minimal conditions on the underlying statistical model.