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<0.001) and 2 times (aHR = 2.338; 95% CI: 1.553-3.519, p<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.
Highlights
Survival regression methods are commonly used to explore heterogeneity among subjects in medical research [1] and to estimate prognostic factors for survival [2,3,4,5,6]
We considered the (i) Cox proportional hazard models for frailty, (ii) Multilevel mixed-effects parametric survival models for proportional hazard and accelerated failure times and (iii) Flexible parametric survival regression models with frailty generally referred to as the Royston-Parmar (RP) models and their equivalents without frailty
The Cox proportional hazard (PH) model with frailty is an extension of the Cox PH model developed in 1972 which assumed that hazards are multiplicatively proportional to baseline hazards [5] as shown in Eq (1)
Summary
Survival regression methods are commonly used to explore heterogeneity among subjects in medical research [1] and to estimate prognostic factors for survival [2,3,4,5,6]. One of the major challenges in survival analysis modelling is clustering among followed subjects, otherwise known as frailty [7,8]. The concept of frailty is an issue of discourse in statistical modelling, including survival analysis. Frailty is a group-specific latent random effect that multiplies.
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