PRIMEplus.LRT {PRIMEplus}

Likelihood Ratio Test

Description

PRIMEplus likelihood ratio test

Usage

 PRIMEplus.LRT(data, effect_p, beta0, time.var="X", trt.var="trt", 
       status.var="event_status", id.var="id", t1=1, lambda0=NULL, 
       stopTol=1e-4, maxiter=100000, print=0)

Arguments

data

Data frame or matrix containing a time-to-event variable (time.var), a treatment variable (trt.var), and a censoring variable (status.var).

effect_p

Vector of proportions for groups of responders in the treatment arm at baseline (see details).

beta0

Vector or matrix of initial estimates for the log-hazard ratios (see details).

time.var

Time-to-event variable name in data. The default is "X".

trt.var

Binary treatment variable name in data coded as 0 for controls and 1 for subjects that received treatment.

status.var

Name of the binary censoring variable in data coded as 0 for censored subjects and 1 for subjects that experienced an event.

id.var

NULL or subject id variable in data. The default is "id".

t1

Delayed duration. The default is 1.

lambda0

NULL or vector of initial estimates for the baseline hazards corrsponding to the ordered event times.

stopTol

Stopping tolerance. The default is 1e-4.

maxiter

Maximum number of iterations. The default is 100000.

print

0-2 to print information. Larger values will print more information. The default is 0.

Details

The EM algorithm is sensitive to the initial values of the log-hazard ratios (beta0), so different initial estimates should be tried to ensure the maximum log-likelihood is obtained. Thus, beta0 can be a vector or matrix, where in the case of a matrix, each row corresponds to a different set of intial estimates. Each set of initial estimates must contain distinct non-zero values. The length and order of effect_p must be the same as the columns of beta0. Both of these should contain information only for the groups of responders. For example, if there are full responders and partial responders, then effect_p would be a vector of length two, and beta0 would be a vector of length two or a matrix with two columns.

Value

A list containing the objects:

Name	Description
converged	TRUE if EM algorithm converged
beta	final log(hazard ratio) estimates of responders versus controls
lambda	final estimates of baseline hazards
probResponder	estimated probability of a subject being a responder
loglike	log-likelihood value at the final estimates
loglike.marg	marginal log-likelihood value at the final estimates
loglike.marg.0	marginal log-likelihood value under the null hypothesis
LRT	likelihood-ratio test statistic
pvalue	p-value of the likelihood ratio test

Author(s)

Zhenzhen Xu <[e-mail]>, Yongsoek Park <[e-mail]> and Bin Zhu <[e-mail]>

See Also

PRIMEplus.EM

Examples

  data(data, package="PRIMEplus")
  effp  <- c(0.3, 0.3)
  beta0 <- c(log(0.4), log(0.6))
  ret   <- PRIMEplus.LRT(data, effp, beta0)
  ret$LRT 
  ret$pvalue