{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T07:15:54Z","timestamp":1775805354797,"version":"3.50.1"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Johns Hopkins University Center","award":["1P30AI094189"],"award-info":[{"award-number":["1P30AI094189"]}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P01-AA029544"],"award-info":[{"award-number":["P01-AA029544"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01-AI170240"],"award-info":[{"award-number":["R01-AI170240"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01-CA250851"],"award-info":[{"award-number":["R01-CA250851"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>A challenge to research in big data is the inherent computational intensity of analyses, particularly when using rigorous methods to address biases. We demonstrate the use of sampling methods in big data to estimate parameters using fewer resources. Our motivating question was whether lung cancer incidence differs by baseline HIV status, using a cohort of nearly 30 million Medicaid beneficiaries. We targeted three parameters (with listed estimator): incidence rate ratio (IRR, Poisson model), HR (Cox model), and risk ratio (RR, Kaplan\u2013Meier). We controlled for confounders using inverse probability weighting. We ran analyses using the full sample and several sampling schemes: divide-and-recombine (10, 20, 50 samples), subcohort, and case-cohort. We compared point estimates, standard errors, computation time, and memory used. We observed 1113 incident lung cancer diagnoses among 180\u2009980 beneficiaries with HIV and 33\u2009106 diagnoses among 29\u2009179\u2009940 beneficiaries without HIV. Findings were similar across target parameters. The subcohort and case-cohort approaches had estimates closer to the full sample and were faster and less memory intensive than divide-and-recombine, especially when estimating the risk ratio. Including nonsampled cases in the case-cohort resulted in increases in computation time and memory relative to the subcohort approach.<\/jats:p>","DOI":"10.1093\/aje\/kwaf268","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T13:13:29Z","timestamp":1764854009000},"page":"1129-1135","source":"Crossref","is-referenced-by-count":0,"title":["Sampling for computational efficiency when conducting analyses in big data"],"prefix":"10.1093","volume":"195","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7177-1847","authenticated-orcid":false,"given":"Jacqueline E","family":"Rudolph","sequence":"first","affiliation":[{"name":"Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University , Baltimore, MD ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University , Baltimore, MD ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karine","family":"Yenokyan","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University , Baltimore, MD ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqiang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Medicine, School of Medicine, Johns Hopkins University , Baltimore, MD ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eryka","family":"Wentz","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University , Baltimore, MD ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keri L","family":"Calkins","sequence":"additional","affiliation":[{"name":"Mathematica , Ann Arbor, MI ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Corinne E","family":"Joshu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University , Baltimore, MD ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2355-612X","authenticated-orcid":false,"given":"Bryan","family":"Lau","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University , Baltimore, MD ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"2026041002350486600_ref1","volume-title":"Handbook of Big Data","author":"B\u00fchlmann","year":"2019"},{"issue":"1","key":"2026041002350486600_ref2","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1097\/QAI.0000000000003318","article-title":"Comparing cancer incidence in an observational cohort of Medicaid beneficiaries with and without HIV, 2001-2015","volume":"95","author":"Rudolph","year":"2024","journal-title":"J Acquir Immune Defic Syndr 1999"},{"key":"2026041002350486600_ref3","volume-title":"Medicaid and People with HIV","author":"Roberts","year":"27, 2023"},{"issue":"5","key":"2026041002350486600_ref4","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1161\/CIRCOUTCOMES.113.000359","article-title":"Propensity score methods for confounding control in nonexperimental research","volume":"6","author":"Brookhart","year":"2013","journal-title":"Circ Cardiovasc Qual Outcomes"},{"key":"2026041002350486600_ref5","volume-title":"meta: General Package for Meta-Analysis","author":"Schwarzer","year":"12, 2024"},{"key":"2026041002350486600_ref6","volume-title":"mclapply function\u2014RDocumentation","author":"R Core Team"},{"issue":"3","key":"2026041002350486600_ref7","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1097\/EDE.0000000000001469","article-title":"The case for case\u2013cohort: an applied epidemiologist\u2019s guide to reframing case\u2013cohort studies to improve usability and flexibility","volume":"33","author":"O\u2019Brien","year":"2022","journal-title":"Epidemiology."},{"key":"2026041002350486600_ref8","volume-title":"jerudolph13\/big_data_samp","author":"Rudolph","year":"28, 2025"},{"issue":"20","key":"2026041002350486600_ref9","doi-asserted-by":"publisher","first-page":"3089","DOI":"10.1002\/sim.2174","article-title":"Adjusted Kaplan\u2013Meier estimator and log-rank test with inverse probability of treatment weighting for survival data","volume":"24","author":"Xie","year":"2005","journal-title":"Stat Med"},{"key":"2026041002350486600_ref10","doi-asserted-by":"publisher","DOI":"10.32614\/cran.package.survival","volume-title":"Survival: Survival Analysis","author":"Therneau","year":"22, 2001"},{"issue":"374","key":"2026041002350486600_ref11","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1080\/01621459.1981.10477650","article-title":"Censored data and the bootstrap","volume":"76","author":"Efron","year":"1981","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"2026041002350486600_ref12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v016.i09","article-title":"Object-Oriented Computation of Sandwich Estimators","volume":"95","author":"Zeileis","year":"2006","journal-title":"J Stat Soft"},{"issue":"1","key":"2026041002350486600_ref18","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v095.i01","article-title":"Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R","volume":"95","author":"Zeileis","year":"2020","journal-title":"Journal of Statistical Software"},{"issue":"2","key":"2026041002350486600_ref13","doi-asserted-by":"publisher","first-page":"dyae030","DOI":"10.1093\/ije\/dyae030","article-title":"M-estimation for common epidemiological measures: introduction and applied examples","volume":"53","author":"Ross","year":"2024","journal-title":"Int J Epidemiol"},{"key":"2026041002350486600_ref14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/9781118445112.stat08068","volume-title":"Wiley StatsRef: Statistics Reference Online","author":"Daniel","year":"2018","edition":"1st ed."},{"key":"2026041002350486600_ref15","doi-asserted-by":"publisher","volume-title":"A Scalable Bootstrap for Massive Data","author":"Kleiner","DOI":"10.48550\/ARXIV.1112.5016"},{"key":"2026041002350486600_ref16","volume-title":"A Practical Benchmark of duckplyr. Schochastics\u2014All Things R","author":"Schoch","year":"10, 2025"},{"issue":"1","key":"2026041002350486600_ref17","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1097\/EDE.0000000000000409","article-title":"Selection bias due to loss to follow up in cohort studies","volume":"27","author":"Howe","year":"2016","journal-title":"Epidemiol Camb Mass"}],"container-title":["American Journal of Epidemiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/aje\/advance-article-pdf\/doi\/10.1093\/aje\/kwaf268\/65761223\/kwaf268.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/aje\/article-pdf\/195\/4\/1129\/65761223\/kwaf268.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/aje\/article-pdf\/195\/4\/1129\/65761223\/kwaf268.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:35:10Z","timestamp":1775802910000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/aje\/article\/195\/4\/1129\/8369118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,5]]},"references-count":18,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12,5]]},"published-print":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1093\/aje\/kwaf268","relation":{},"ISSN":["0002-9262","1476-6256"],"issn-type":[{"value":"0002-9262","type":"print"},{"value":"1476-6256","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,4]]},"published":{"date-parts":[[2025,12,5]]}}}