{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T01:00:29Z","timestamp":1778720429441,"version":"3.51.4"},"reference-count":42,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The use of inappropriate methods for estimating the effects of covariates in survival data with frailty leads to erroneous conclusions in medical research. This study evaluated the performance of 13 survival regression models in assessing the factors associated with the timing of complications in implant-supported dental restorations in a Swedish cohort. Data were obtained from randomly selected cohort (n = 596) of Swedish patients provided with dental restorations supported in 2003. Patients were evaluated over 9 years of implant loss, peri-implantitis or technical complications. Best Model was identified using goodness, AIC and BIC. The loglikelihood, the AIC and BIC were consistently lower in flexible parametric model with frailty (df = 2) than other models. Adjusted hazard of implant complications was 45% (adjusted Hazard Ratio (aHR) = 1.449; 95% Confidence Interval (CI): 1.153\u20131.821, p = 0.001) higher among patients with periodontitis. While controlling for other variables, the hazard of implant complications was about 5 times (aHR = 4.641; 95% CI: 2.911\u20137.401, p&lt;0.001) and 2 times (aHR = 2.338; 95% CI: 1.553\u20133.519, p&lt;0.001) higher among patients with full- and partial-jaw restorations than those with single crowns. Flexible parametric survival model with frailty are the most suitable for modelling implant complications among the studied patients.<\/jats:p>","DOI":"10.1371\/journal.pone.0245111","type":"journal-article","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T22:45:40Z","timestamp":1610059540000},"page":"e0245111","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["Performance evaluation of survival regression models in analysing Swedish dental implant complication data with frailty"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9184-8258","authenticated-orcid":true,"given":"Adeniyi Francis","family":"Fagbamigbe","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karolina","family":"Karlsson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Derks","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Max","family":"Petzold","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"key":"pone.0245111.ref001","first-page":"1","article-title":"Comparison of the flexible parametric survival model and Cox model in estimating Markov transition probabilities using real-world data","volume":"13","author":"X Du","year":"2018","journal-title":"PLoS One"},{"key":"pone.0245111.ref002","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.exger.2008.10.005","article-title":"Accelerated failure time models provide a useful statistical framework for aging research","volume":"44","author":"WR Swindell","year":"2009","journal-title":"Exp. 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