Novel g-computation algorithms for time-varying actions with recurrent and semi-competing events

TL;DR

This paper introduces two new g-computation algorithms designed to accurately estimate causal effects in studies with semi-competing events and time-varying actions, addressing a gap in existing methods.

Contribution

The authors develop and validate novel g-computation estimators that handle semi-competing events and time-varying confounding simultaneously, improving accuracy over existing methods.

Findings

Simulations show little bias and proper confidence interval coverage for the new estimators.

The estimators outperform existing methods across various sample sizes.

Application indicates small reductions in hypertension prevalence and mortality risk with smoking prevention.

Abstract

Background: A core aspect of epidemiology is determining the impacts of potential public health interventions over time. With long follow-up periods, epidemiologists may need to consider semi-competing events, in which a terminal event, like death, precludes a non-terminal event, like hypertension. Time-varying confounding poses an additional challenge when studying time-varying interventions or actions. Existing methods do not simultaneously address semi- competing events and time-varying confounding. Methods: We propose two novel g-computation algorithms for causal effects with semi- competing events and time-varying actions. To explore performance of our novel g-computation estimators, we conducted a Monte Carlo simulation study. We then applied our estimator to investigate how cigarette smoking prevention throughout young and middle adulthood might impact prevalent hypertension using data from Waves III (aged 18-26 years) - VI (aged 39-51 years) of the National Longitudinal Study of Adolescent to Adult Health. Results: Our simulations show that the novel g-computation estimators had little bias and appropriate confidence interval coverage. They outperformed existing alternative estimators across sample sizes. In the illustrative application, the novel estimator identified a small reduction in prevalence of hypertension and risk of death in midlife had all cigarette smoking been prevented across follow-up compared to the observed smoking patterns. Conclusion: As long-running cohorts progress in age, death within the study sample will become an increasing concern for studies of aging-related outcomes, life course analyses, and investigations into chronic disease development. Our novel g-computation estimators provide a simultaneous solution.