Monte Carlo modified profile likelihood in models for clustered data

01/08/2018
by   Claudia Di Caterina, et al.
0

The main focus of the analysts who deal with clustered 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 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 and/or sampling assumptions. We show here how to employ Monte Carlo simulation in order to approximate the modified profile likelihood in some of these unconventional frameworks. The proposed solution is widely applicable and is shown to retain the usual properties of the modified profile likelihood. The approach is examined in two istances particularly relevant in applications, i.e. missing-data models and survival models with unspecified censoring distribution. The effectiveness of the proposed solution is validated via simulation studies and two clinical trial applications.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/08/2018

Monte Carlo modified profile likelihood for panel data models

The main focus of the analysts who deal with panel data is usually not o...
research
03/11/2021

Likelihood-based missing data analysis in multivariate crossover trials

For gene expression data measured in a crossover trial, a multivariate m...
research
05/20/2019

Stratified sampling and resampling for approximate Bayesian computation

Approximate Bayesian computation (ABC) is computationally intensive for ...
research
07/01/2020

Scalable Monte Carlo Inference and Rescaled Local Asymptotic Normality

Statisticians are usually glad to obtain additional data, but Monte Carl...
research
05/07/2023

Profile likelihoods for parameters in Gaussian geostatistical models

Profile likelihoods are rarely used in geostatistical models due to the ...
research
09/16/2019

Convergent stochastic algorithm for parameter estimation in frailty models using integrated partial likelihood

Frailty models are often the model of choice for heterogeneous survival ...
research
03/01/2019

Are profile likelihoods likelihoods? No, but sometimes they can be

We contribute our two cents to the ongoing discussion on whether profile...

Please sign up or login with your details

Forgot password? Click here to reset