Alexander Breskin1,2, Andrew Edmonds2, Stephen R Cole2, Daniel Westreich2, Jennifer Cocohoba3, Mardge H Cohen4, Seble G Kassaye5, Lisa R Metsch6, Anjali Sharma7, Michelle S Williams8, Adaora A Adimora2,9. 1. NoviSci Inc, Durham, NC, USA. 2. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 3. Department of Clinical Pharmacy, University of California, San Francisco, CA, USA. 4. Stroger Hospital, Cook County Bureau of Health Services, Chicago, IL, USA. 5. Department of Medicine, Georgetown University Medical Center, Washington, DC, USA. 6. Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA. 7. Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA. 8. Department of Population Health Science, and the Cancer Center and Research Institute, University of Mississippi Medical Center, Jackson, MS, USA. 9. Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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.
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.
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