timeregLC

A Time-Dependent Structural Model Between Latent Classes and Competing Risks Outcomes

Installation Guide

# install.packages("devtools")
devtools::install_github("tengfei-emory/timeregLC")
library(timeregLC)

Currently timeregLC supports R version >= 3.5.0.

Example: analyze a simulated dataset

Data simulation

Function simulation can be used to generate a dataset with baseline covariates and competing risks.

# The following example specifies all required parameters and generate a dataset with three latent classes.

# Regression parameter of latent class effect in the structural competing risks model
lambda <- c(0.5,0.5,-1)

# Latent class proportion
pi = c(0.3,0.35,0.35)

# Mean vectors for the three classes: (1,1), (2.5,2.5) and (4,4)
mu = matrix(c(1,1,2.5,2.5,4,4),nrow=2,ncol=3)

# Covariance matrices for the three classes (as a list)
sigma1 = matrix(c(0.36,0.27,0.27,0.81),2,2)
sigma2 = matrix(c(0.49,0.504,0.504,0.64),2,2)
sigma3 = matrix(c(0.25,0.225,0.225,0.25),2,2)
sigma = list(sigma1,sigma2,sigma3)

# Parameter associated with competing risks distribution
p.cif = 0.66

# Lower bound and upper bound for uniformly distributed censoring time
cl=0.19
cu=1.09

# Main function of simulation. Here sample size is set as 500.
dat=simulation(500,pi,mu,sigma,lambda,p.cif,cl,cu)

Specifically, it returns a data frame of 3 latent classes with 2 baseline covariates (Y.1 and Y.2), time of competing risks (ftime), and failure types (fstatus). Failure types include type 1, 2, censored 0, or missing NA.

Model fitting

The analysis for the dataset dat can be conducted by running timereg function:

library(timereg)
# create an event object 
event = Event(0,dat$ftime,dat$fstatus)

# specify the baseline covariate matrix
covariates=cbind(dat$Y.1,dat$Y.2)

# run main algorithm
fit.timeregLC <- timeregLC(event,covariates,inference=T,C=3,d=1,timepoints=NULL,
                    control.optim=list(reltol=.00001,strategy=2,itermax=1000,trace=F),
                    verbose=T)

The output list fit.timeregLC contains the following information:

lambda: time-dependent point estimates at specified time points

Sigma: time-dependent estimates for the asymptotic covariance estimates

Please refer to the documentation associated with the package for more details. Please report issues under this GitHub repository (tengfei-emory/timeregLC).

References

Fei, T, Hanfelt, J, Peng, L. Evaluating the association between latent classes and competing risks outcomes with multi-phenotype data. Biometrics. 2021. https://doi.org/10.1111/biom.13563