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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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>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\u2019s Interagency HIV Study.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ije\/dyaa156","type":"journal-article","created":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T19:21:08Z","timestamp":1596568868000},"page":"2021-2029","source":"Crossref","is-referenced-by-count":28,"title":["G-computation for policy-relevant effects of interventions on time-to-event outcomes"],"prefix":"10.1093","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5568-2510","authenticated-orcid":false,"given":"Alexander","family":"Breskin","sequence":"first","affiliation":[{"name":"NoviSci Inc, Durham, NC, USA"},{"name":"Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Edmonds","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen R","family":"Cole","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Westreich","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Cocohoba","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, University of California, San Francisco, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mardge 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