pssm (version 1.1.1)

pssm.simulate: Simulates pssm analysis

Description

Simulates survival and progression model

Usage

pssm.simulate(nruns, theta1 = 0.2, theta2 = 0.2, 
phaz.progression = log(-log(0.3)/4) * rep(1, 5), 
phaz.survival = log(-log(0.15)/4) * rep(1, 15), accrual = 3, followup = 2, 
m = 5, n = 500, rescale = 1,etime=4.5,seed=NULL)

Arguments

nruns

number of simulations

theta1

Coefficient for treatment covariate for time to progression

theta2

Coefficient for treatment covariate for survival after progression

phaz.progression

log-hazard vector for progression

phaz.survival

log-hazard vector for survival

accrual

accrual time

followup

follow up time

m

number of intervals, maximum of times

n

number of samples

rescale

Factor to multiply times by

etime

The time that survival is evaluated

seed

Seed for the random number generator if you don't want the data that is analyzed to change.

Value

A three entry list

objects

A nruns by 5 list of "pssm" objects which result from five estimates: 1-Both survival and progression, 2-progression only, 3-tumor free progression, 4-survival on treatment 0, 5-survival on treatment 1. Note that the tumor-free progression rates are estimated assuming that progression occurs at the first time it is detected.

ests

estimates for treatment effects followed by death rate and progression rate

se.ests

standard errors for the treatment effects

%% ...

See Also

pssm-class, pssm-package, pssm.generate.data, pssm.object, pssm, pssm.survivalcurv, plot-methods, pssm.power

Examples

# NOT RUN {
##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.
#creates 1 simulations with 50 observations
pss=pssm.simulate(nruns=1,theta1=.2,theta2=.2,phaz.progression=log(-log(.3)/4)*rep(1,3),
  phaz.survival=log(-log(.15)/4)*rep(1,6),accrual=1,followup=2,,m=3,n=50,rescale=1)
#Summary of run 1
summary(pss$objects[[1]][[1]])
# }