Estimating the causal effects of multiple intermittent treatments with application to COVID-19

TL;DR

This paper introduces a novel continuous-time weighting method and joint marginal structural survival model to estimate causal effects of multiple time-varying treatments, demonstrated on COVID-19 data, improving bias reduction in observational studies.

Contribution

The paper develops a continuous-time weighting strategy and joint marginal structural model for complex longitudinal treatment data, enhancing causal inference in survival analysis.

Findings

Better bias reduction compared to conventional methods

Effective estimation of causal effects in COVID-19 treatments

Handles irregularly spaced time intervals without discretization

Abstract

To draw real-world evidence about the comparative effectiveness of multiple time-varying treatments on patient survival, we develop a joint marginal structural survival model and a novel weighting strategy to account for time-varying confounding and censoring. Our methods formulate complex longitudinal treatments with multiple start/stop switches as the recurrent events with discontinuous intervals of treatment eligibility. We derive the weights in continuous time to handle a complex longitudinal dataset without the need to discretize or artificially align the measurement times. We further use machine learning models designed for censored survival data with time-varying covariates and the kernel function estimator of the baseline intensity to efficiently estimate the continuous-time weights. Our simulations demonstrate that the proposed methods provide better bias reduction and nominal coverage probability when analyzing observational longitudinal survival data with irregularly spaced time intervals, compared to conventional methods that require aligned measurement time points. We apply the proposed methods to a large-scale COVID-19 dataset to estimate the causal effects of several COVID-19 treatments on the composite of in-hospital mortality and ICU admission.