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Actually, this reasoning lies behind the development of random effect ("frailty") models in survival analysis [ 31].
Such models allow us to fully consider the correlation between the two processes using a shared random effect (frailty).
This analysis will then be confirmed using a Cox regression analysis taking into account the center effect (frailty Cox model).
A significant LRT suggests that a random effect (frailty) accounts for the within-player correlation between injuries.
Others used the random effect frailty model or the conditional frailty model for such recurrent event data analysis [ 6, 7].
The frailty model includes a random effect (frailty) to account for the within-subject correlation between injuries and so is a more general model, with fewer assumptions.
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In the Bayesian setting random effects (frailty terms) are treated like regression coefficients (9 Λ(t i | x i ) = Λ 0(t i ) exp{ β ' x i + α j (i )}.
This class of statistical techniques also includes nonparametric methods for comparing groups (e.g., log-rank test), semiparametric methods for assessing potential fixed effects of categorical and continuous covariates (e.g., Cox proportional hazards model and extensions) and for addressing random effects (frailty models), and fully parametric methods for all of these purposes.
Briefly, all data from individual participants were combined in a single meta-analysis based on a simple Cox proportional regression model stratified by trial, containing random effects (frailty models) to assess the impact of stent type for instance, cobalt-chromium everolimus eluting stent versus bare metal stent on outcome measures.
This used random effects (frailties) to take account of clustering [ 15] and adjusted for the covariates in the models described above and admission method (emergency/elective). Figure 1 shows the flow of practice and individual recruitment and analysis.
Specifically, the Cox regression included random effects (frailties) at the site level, which were assumed to be gamma distributed [ 16] to allow for correlation of length of stays within site.
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