mexhaz: An R Package for Fitting Flexible Hazard-Based Regression Models for Overall and Excess Mortality with a Random Effect
Dublin Core
Title
mexhaz: An R Package for Fitting Flexible Hazard-Based Regression Models for Overall and Excess Mortality with a Random Effect
Subject
adaptive Gauss-Hermite quadrature, excess hazard, flexible models, frailty models,
time-dependent effects, C.
time-dependent effects, C.
Description
We present mexhaz, an R package for fitting flexible hazard-based regression models
with the possibility to add time-dependent effects of covariates and to account for a twolevel hierarchical structure in the data through the inclusion of a normally distributed
random intercept (i.e., a log-normally distributed shared frailty). Moreover, mexhazbased models can be fitted within the excess hazard setting by allowing the specification
of an expected hazard in the model. These models are of common use in the context of
the analysis of population-based cancer registry data.
Follow-up time can be entered in the right-censored or counting process input style, the
latter allowing models with delayed entries. The logarithm of the baseline hazard can be
flexibly modeled with B-splines or restricted cubic splines of time. Parameters estimation
is based on likelihood maximization: in deriving the contribution of each observation to
the cluster-specific conditional likelihood, Gauss-Legendre quadrature is used to calculate
the cumulative hazard; the cluster-specific marginal likelihoods are then obtained by integrating over the random effects distribution, using adaptive Gauss-Hermite quadrature.
Functions to compute and plot the predicted (excess) hazard and (net) survival (possibly
with cluster-specific predictions in the case of random effect models) are provided. We
illustrate the use of the different options of the mexhaz package and compare the results
obtained with those of other available R packages.
with the possibility to add time-dependent effects of covariates and to account for a twolevel hierarchical structure in the data through the inclusion of a normally distributed
random intercept (i.e., a log-normally distributed shared frailty). Moreover, mexhazbased models can be fitted within the excess hazard setting by allowing the specification
of an expected hazard in the model. These models are of common use in the context of
the analysis of population-based cancer registry data.
Follow-up time can be entered in the right-censored or counting process input style, the
latter allowing models with delayed entries. The logarithm of the baseline hazard can be
flexibly modeled with B-splines or restricted cubic splines of time. Parameters estimation
is based on likelihood maximization: in deriving the contribution of each observation to
the cluster-specific conditional likelihood, Gauss-Legendre quadrature is used to calculate
the cumulative hazard; the cluster-specific marginal likelihoods are then obtained by integrating over the random effects distribution, using adaptive Gauss-Hermite quadrature.
Functions to compute and plot the predicted (excess) hazard and (net) survival (possibly
with cluster-specific predictions in the case of random effect models) are provided. We
illustrate the use of the different options of the mexhaz package and compare the results
obtained with those of other available R packages.
Creator
Hadrien Charvat
Source
https://www.jstatsoft.org/article/view/v098i14
Publisher
National Cancer Center Japan
Date
May 2021
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Hadrien Charvat, “mexhaz: An R Package for Fitting Flexible Hazard-Based Regression Models for Overall and Excess Mortality with a Random Effect,” Repository Horizon University Indonesia, accessed February 5, 2025, https://repository.horizon.ac.id/items/show/8200.