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Performance evaluation of survival regression models in analysing Swedish dental implant complication data with frailty

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  • Adeniyi Francis Fagbamigbe
  • Karolina Karlsson
  • Jan Derks
  • Max Petzold

Abstract

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): 1.153–1.821, 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: 2.911–7.401, p

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  • Adeniyi Francis Fagbamigbe & Karolina Karlsson & Jan Derks & Max Petzold, 2021. "Performance evaluation of survival regression models in analysing Swedish dental implant complication data with frailty," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-16, January.
    • Handle: RePEc:plo:pone00:0245111
    DOI: 10.1371/journal.pone.0245111
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    References listed on IDEAS

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    1. Nicola Orsini, 2013. "Review of Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model by Patrick Royston and Paul C. Lambert," Stata Journal, StataCorp LP, vol. 13(1), pages 212-216, March.
    2. Xudong Du & Mier Li & Ping Zhu & Ju Wang & Lisha Hou & Jijie Li & Hongdao Meng & Muke Zhou & Cairong Zhu, 2018. "Comparison of the flexible parametric survival model and Cox model in estimating Markov transition probabilities using real-world data," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-13, August.
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    1. Adeniyi Francis Fagbamigbe & Emma Norrman & Christina Bergh & Ulla-Britt Wennerholm & Max Petzold, 2021. "Comparison of the performances of survival analysis regression models for analysis of conception modes and risk of type-1 diabetes among 1985–2015 Swedish birth cohort," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-23, June.

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