AEHMLE: Relative Survival AH model.

In AHSurv: Flexible Parametric Accelerated Hazards Models

Relative Survival AH model.

Description

The flexible parametric accelerated excess hazards (AEH) model's maximum likelihood estimation, log-likelihood, and information criterion. Baseline hazards:NGLL, GLL, KW,EW, MLL, PGW, GG, MKW, Log-logistic, Weibull, Log-normal, Burr-XII, and Gamma

Usage

AEHMLE(
  init,
  time,
  delta,
  n,
  basehaz,
  z,
  hp.obs,
  method = "Nelder-Mead",
  maxit = 1000,
  log = FALSE
)

Arguments

init	: initial points for optimisation
time	: survival times
delta	: vital indicator (0-alive,1 - dead)
n	: The number of the observations of the data set
basehaz	: baseline hazard structure including baseline (NGLLAEH,GLLAEH,EWAEH,KWAEH,MLLAEH, PGWAEH,GGAEH,MKWAEH,LLAEH,WAEH,GAEH, LNAEH,BXIIAEEH)
z	: design matrix for covariates (p x n), p >= 1
hp.obs	: population hazards (for uncensored individuals)
method	:"nlminb" or a method from "optim"
maxit	:The maximum number of iterations. Defaults to 1000
log	:log scale (TRUE or FALSE)

Format

By default the function calculates the following values:

• AIC: Akaike Information Criterion;

• CAIC: Consistent Akaikes Information Criterion;

• BIC: Bayesian Information Criterion;

• BCAIC: Bozdogan’s Consistent Akaike Information Criterion;

• HQIC: Hannan-Quinn information criterion;

• par: maximum likelihood estimates;

• Value: value of the likelihood function;

• Convergence: 0 indicates successful completion and 1 indicates that the iteration limit maxit.

Value

a list containing the output of the optimisation (OPT) and the information criterion including (AIC, BIC, CAIC, BCAIC, and HQIC).

Author(s)

Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, [e-mail]

Examples

data(bmt)
time<-bmt$Time
delta<-bmt$Status
z<-bmt$TRT
AEHMLE(init = c(1.0,0.5,1.0,0.5),time = time,delta = delta,n=nrow(z),
basehaz = "GLLAEH",z = z,hp.obs=0.6,method = "Nelder-Mead",
maxit = 1000)

AHSurv documentation built on June 2, 2022, 5:06 p.m.