GLDreg (version 1.1.2)

GLD.lm.surv: This function fits a GLD Accelerated Failure Time Model for Survival Data

Description

Similar to GLD.lm, this function fits an Accelerated Failure Time Model using RS/FKML GLDs.

Usage

GLD.lm.surv(formula, censoring, data, param, maxit = 20000, fun, 
            method = "Nelder-Mead", diagnostics = TRUE, range = c(0.01, 0.99), 
            init = NULL, alpha = 0.05, censor.type = "right", adj.int = TRUE, 
            GLD.adj = FALSE, adj.censor = TRUE, keep.uncen = TRUE)

Value

Message

Short description of estimation method used and whether the result converged

Bias Correction

Bias correction used to ensure the line has zero mean residuals

Estimated parameters

A set of estimate coefficients from GLD regression

Fitted

Predicted response value from model

Residual

Residual of model

formula

Formula used in the model

param

Specify whether RS/FKML/FMKL GLD was used

y

The response variable

x

The explanatory variable(s)

fun

GLD fitting function used in the computation process, outputted for internal programming use

censoring

Censoring data

AIC.full

AIC results

BIC.full

BIC results

censor.gld.values

Result of GLD fit, including censoring

Arguments

formula

A symbolic expression of the model to be fitted, similar to the formula argument in lm, see formula for more information

censoring

1=Event, 0= Censored

data

Dataset containing variables of the model

param

Can be "rs", "fmkl" or "fkml"

maxit

Maximum number of iterations for numerical optimisation

fun

If param="fmkl" or "fkml", this can be one of fun.RMFMKL.ml.m, fun.RMFMKL.ml, for maximum likelihood estimation (*.ml.m is a faster implementation of *.ml).

If param="rs", this can be one of fun.RPRS.ml.m, fun.RPRS.ml, for maximum likelihood estimation (*.ml.m is a faster implementation of *.ml).

method

Defaults to "Nelder-Mead" algorithm, can also be "SANN" but this is a lot slower and may not as good

diagnostics

Defaults to TRUE, which computes Kolmogorov-Smirnoff test, Kolmogorov-Smirnoff Resample test, Data drive smooth test and do QQ plot on non censored data.

range

The is the quantile range to plot the QQ plot, defaults to 0.01 and 0.99 to avoid potential problems with extreme values of GLD which might be -Inf or Inf.

init

Choose a different set of initial values to start the optimisation process. This can either be full set of parameters including GLD parameter estimates, or it can just be the coefficient estimates of the regression model.

alpha

Significant level of goodness of fit test.

censor.type

Can be" right" of "left censored.

adj.int

Adjust intercept in final output?

GLD.adj

Adjust GLD fitted to have theoretical zero mean?

adj.censor

Adjust censoring?

keep.uncen

Keep uncensored values?

Author

Steve Su

References

Su (2021) "Flexible Parametric Accelerated Failure Time Model" Journal of Biopharmaceutical Statistics Volume 31, 2021 - Issue 5

See Also

GLD.lm.full, GLD.quantreg, GLD.lm, GLD.lm.full.surv

Examples

if (FALSE) {

# Note the actg.rs1 differs from GLD.lm.full.surv because adj.int is set as 
# TRUE in GLD.lm.surv by default but adj.int is set as FALSE in 
# GLD.lm.full.surv by default

actg.rs1<-GLD.lm.surv(log(time)~factor(txgrp)+hemophil+cd4+priorzdv+age,
censoring=actg[which(actg$txgrp!=3 & actg$txgrp!=4),]$censor, 
data=actg[which(actg$txgrp!=3 & actg$txgrp!=4),],
param="rs",fun=fun.RPRS.ml.m)

actg.rs2<-GLD.lm.surv(log(time)~factor(txgrp)+hemophil+cd4+priorzdv+age,
censoring=actg[which(actg$txgrp!=3 & actg$txgrp!=4),]$censor, 
data=actg[which(actg$txgrp!=3 & actg$txgrp!=4),],
param="rs",fun=fun.RPRS.ml.m,adj.int=FALSE)

}