jmc_0: Joint Modelling for Continuous outcomes

Description

Joint modeling of longitudinal continuous data and competing risks

Usage

jmc_0(
  p,
  yfile,
  cfile,
  mfile,
  point = 20,
  maxiterations = 1e+05,
  do.trace = FALSE,
  type_file = TRUE
)

Arguments

The dimension of fixed effects (include intercept) in yfile.

yfile

Y matrix for longitudinal measurements in long format. For example, for a subject with n measurements, there should be n rows for this subject. The # of rows in y matrix is the total number of measurements for all subjects in the study. The columns in Y should start with the longitudinal outcome (column 1), the covariates for the random effects, and then the covariates for the fixed effects.

cfile

C matrix for competing risks failure time data. Each subject has one data entry, so the number of rows equals to the number of subjects. The survival / censoring time is included in the first column, and the failure type coded as 0 (censored events), 1 (risk 1), or 2 (risk 2) is given in the second column. Two competing risks are assumed. The covariates are included in the third column and on.

mfile

M vector to indicate the number of longitudinal measurements per subject. The number of rows equals to the number of subjects.

point

Quadrature points used in the EM procedure.Default is 20.

maxiterations

Maximum values of iterations. Default is 100000.

do.trace

Print detailed information of each iteration. Default is false, i.e., not to print the iteration details.

type_file

Types of inputs. Default is true, i.e. data files with headers. If set to "F", inputs are changed to data matrixes or data.frames (with headers)

Value

Object of class JMcmprsk with elements

`vcmatrix`	The variance-covariance matrix for all the parameters. The parameters are in the order: \(\beta\), \(\sigma^2\), \(\gamma\), \(\nu\), and \(\Sigma\). The elements in \(\Sigma\) are output in the order along the main diagonal line, then the second main diagonal line, and so on.
`betas`	The point estimates of \(\beta\).
`se_betas`	The standard error estimate of \(\beta\).
`gamma_matrix`	The point estimate of \(\gamma\).
`se_gamma_matrix`	The standard error estimate of \(\gamma\).
`v_estimate`	The point estimate of \(\nu\).
`se_v_estimate`	The standard error estimate of \(\nu\).
`sigma2_val`	The point estimate of \(\sigma^2\).
`se_sigma2_val`	The standard error estimate of \(\sigma^2\).
`sigma_matrix`	The point estimate of \(\Sigma\) (only the upper triangle portion of the matrix is output).
`se_sigma`	The standard error estimate of \(\Sigma\).The standard errors are given in this order: main diagonal, the second main diagonal, and so on.
`loglike`	Log Likelihood.

References

Elashoff, Robert M., Gang Li, and Ning Li. "A joint model for longitudinal measurements and survival data in the presence of multiple failure types." Biometrics 64.3 (2008): 762-771.

Examples

Run this code

# NOT RUN {
# A toy example on a dataset called from file paths
require(JMcmprsk)
set.seed(123)
yfile=system.file("extdata", "jmcsimy.txt", package = "JMcmprsk")
cfile=system.file("extdata", "jmcsimc.txt", package = "JMcmprsk")
mfile=system.file("extdata", "jmcsimm.txt", package = "JMcmprsk")
jmc_0fit = jmc_0(p=4, yfile, cfile, mfile, point=6, do.trace = FALSE)
# }
# NOT RUN {
# A toy example on data frames/matrices
require(JMcmprsk)
set.seed(123)
data(lung)
lungY <- lung[, c(2:11)]
lungC <- unique(lung[, c(1, 12, 13, 6:10)])
lungC <- lungC[, -1]
lungM <- data.frame(table(lung$ID))
lungM <- as.data.frame(lungM[, 2])
res1=jmc_0(p=8, lungY, lungC, lungM, point=20, do.trace = FALSE, type_file = FALSE)
res1
# }