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Title: Monte Carlo modified profile likelihood in survival models for clustered censored data Authors:  Claudia Di Caterina - University of Padova (Italy) [presenting]
Abstract: The main focus of the analysts who deal with clustered survival data is usually not on the clustering variables, and hence the group-specific parameters are treated as nuisance. If a fixed effects formulation is preferred and the total number of clusters is large relative to the single-group sizes, classical parametric frequentist techniques relying on the profile likelihood are often misleading. The use of alternative tools, such as modifications to the profile likelihood or integrated likelihoods, for making accurate inference on a parameter of interest can be complicated by the presence of nonstandard modelling assumptions. We propose to employ Monte Carlo simulation in order to approximate the modified profile likelihood in general regression settings for survival data with unspecified censoring mechanism. Particularly, a nonparametric bootstrap is encompassed in the procedure to estimate the latter.