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https://github.com/tengfei-emory/timereglc
A Time-Dependent Structural Model Between Latent Classes and Competing Risks Outcomes
https://github.com/tengfei-emory/timereglc
competing-risks latent-class-analysis
Last synced: 14 days ago
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A Time-Dependent Structural Model Between Latent Classes and Competing Risks Outcomes
- Host: GitHub
- URL: https://github.com/tengfei-emory/timereglc
- Owner: tengfei-emory
- License: gpl-3.0
- Created: 2019-05-05T11:31:45.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-09-23T03:57:14.000Z (over 3 years ago)
- Last Synced: 2024-11-05T03:43:01.617Z (2 months ago)
- Topics: competing-risks, latent-class-analysis
- Language: R
- Homepage:
- Size: 46.9 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# timeregLC
A Time-Dependent Structural Model Between Latent Classes and Competing Risks Outcomes
# Installation Guide
```{r}
# 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.
```{r}
# 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:
```{r}
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