{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:28:19Z","timestamp":1772836099979,"version":"3.50.1"},"reference-count":35,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T00:00:00Z","timestamp":1588550400000},"content-version":"unspecified","delay-in-days":124,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Epidemiol. Infect."],"published-print":{"date-parts":[[2020]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With a case-crossover design, a case's exposure during a risk period is compared to the case's exposures at referent periods. The selection of referents for this self-controlled design is determined by the referent selection strategy (RSS). Previous research mainly focused on systematic bias associated with the RSS. We additionally focused on how RSS determines the number of referents per risk, sensitivity to overdispersion and time-varying confounding.<\/jats:p><jats:p>We illustrated the consequences of different RSS using a simulation study informed by data on meteorological variables and Legionnaires\u2019 disease. By randomising the events and exposure time series, we explored statistical power associated with time-stratified and fixed bidirectional RSS and their susceptibility to systematic bias and confounding bias. In addition, we investigated how a high number of events on the same date (e.g. outbreaks) affected coefficient estimation. As illustrated by our work, referent selection alone can be insufficient to control for a time-varying confounding bias. In contrast to systematic bias, confounding bias can be hard to detect. We studied potential solutions: varying the model parameters and link-function, outlier-removal and aggregating the input-data over smaller areas. Our simulation study offers a framework for researchers looking to detect and to avoid bias in case-crossover studies.<\/jats:p>","DOI":"10.1017\/s0950268820000916","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T09:47:20Z","timestamp":1588585640000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":6,"title":["Optimising the case-crossover design for use in shared exposure settings"],"prefix":"10.1017","volume":"148","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5637-4613","authenticated-orcid":false,"given":"T.","family":"Braeye","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1881-0637","authenticated-orcid":false,"given":"N.","family":"Hens","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2020,5,4]]},"reference":[{"key":"S0950268820000916_ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10096-015-2363-4"},{"key":"S0950268820000916_ref32","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/68.3.703"},{"key":"S0950268820000916_ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.diagmicrobio.2016.03.022"},{"key":"S0950268820000916_ref29","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0b013e3181632c24"},{"key":"S0950268820000916_ref28","doi-asserted-by":"publisher","DOI":"10.1097\/01.ede.0000152915.58564.d3"},{"key":"S0950268820000916_ref27","doi-asserted-by":"publisher","DOI":"10.1002\/sim.960"},{"key":"S0950268820000916_ref25","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0000000000000904"},{"key":"S0950268820000916_ref24","unstructured":"24. gnm: Generalized Nonlinear Models version 1.1-0 from CRAN. 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