G-computation for policy-relevant effects of interventions on time-to-event outcomes

Alexander Breskin, Andrew Edmonds, Stephen R. Cole, Daniel Westreich, Jennifer Cocohoba, Mardge H. Cohen, Seble G. Kassaye, Lisa R. Metsch, Anjali Sharma, Michelle S. Williams, Adaora A. Adimora

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Parametric g-computation is an analytic technique that can be used to estimate the effects of exposures, treatments and interventions; it relies on a different set of assumptions than more commonly used inverse probability weighted estimators. Whereas prior work has demonstrated implementations for binary exposures and continuous outcomes, use of parametric g-computation has been limited due to difficulty in implementation in more typical complex scenarios. Methods: We provide an easy-to-implement algorithm for parametric g-computation in the setting of a dynamic baseline intervention of a baseline exposure and a time-to-event outcome. To demonstrate the use of our algorithm, we apply it to estimate the effects of interventions to reduce area deprivation on the cumulative incidence of sexually transmitted infections (STIs: Gonorrhea, chlamydia or trichomoniasis) among women living with HIV in the Women's Interagency HIV Study. Results: We found that reducing area deprivation by a maximum of 1 tertile for all women would lead to a 2.7% [95% confidence interval (CI): 0.1%, 4.3%] reduction in 4-year STI incidence, and reducing deprivation by a maximum of 2 tertiles would lead to a 4.3% (95% CI: 1.9%, 6.4%) reduction. Conclusions: As analytic methods such as parametric g-computation become more accessible, epidemiologists will be able to estimate policy-relevant effects of interventions to better inform clinical and public health practice and policy.

Original languageEnglish (US)
Pages (from-to)2021-2029
Number of pages9
JournalInternational journal of epidemiology
Volume49
Issue number6
DOIs
StatePublished - Dec 1 2020

Keywords

  • Causal inference
  • G-computation
  • HIV
  • area deprivation
  • sexually transmitted infections
  • survival analysis

ASJC Scopus subject areas

  • Epidemiology

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