StabCont.surv: parameter stability test for continuous partitioning variable

In LongCART: Recursive Partitioning for Longitudinal Data and Right Censored Data Using Baseline Covariates

parameter stability test for continuous partitioning variable

Description

Performs parameter stability test (Kundu, 2020) with continuous partitioning variable to determine whether the parameters of exponential time-to-event distribution and exponential censoring distribution remain same across all distinct values of given continupus partitioning variable.

Usage

StabCont.surv(data, timevar, censorvar, splitvar, 
              time.dist="exponential", cens.dist="NA", event.ind=1, print=FALSE)

Arguments

data	name of the dataset. It must contain variable specified for timevar (indicating follow-up times), censorvar (indicating censoring status) and the caterogrical partitioning variable of interest specified in splitvar. Note that, only numerically coded categorical variable should be specified.
timevar	name of the variable with follow-up times.
censorvar	name of the variable with censoring status.
time.dist	name of time-to-event distribution. It can be one of the following distributions: "exponential", "weibull", "lognormal" or "normal".
cens.dist	name of censoring distribution. It can be one of the following distributions: "exponential", "weibull", "lognormal", "normal" or "NA". If specified "NA", then parameter instability test corresponding to censoring distribution will not be performed.
event.ind	value of the censoring variable indicating event.
splitvar	the continuous partitioning variable of interest.
print	if TRUE, then additional information including estimated parameters, score function and its variance will be printed.

Details

StabCont.surv() performs the following omnibus test

H_0:theta_{(g)}=theta_0 vs. H_1: theta_{(g)} ^= theta_0, for all g

where, theta_{(g)} is the true value of theta for subjects with X=C_g. theta includes all the parameters of time to event distribution and also parameters of censoring distribution, if specified. C_g is the any value realized by continuous partitioning variable X.

Exponential distribution: f(t)=lambda*exp(-lambda*t)

Weibull distribution: f(t)=alpha*lambda*t^(alpha-1)*exp(-lambda*t^alpha)

Lognormal distribution: f(t)=(1/t)*(1/sqrt(2*pi*sigma^2))*exp[-(1/2)*(log(t)-mu)/sigma^2]

Normal distribution: f(t)=(1/sqrt(2*pi*sigma^2))*exp[-(1/2)*(t-mu)/sigma^2]

Value

pval	p-value for parameter instability test
type	1, if event times are more heterogeneous; 2, if censoring times are more hetergeneous.

Author(s)

Madan Gopal Kundu [e-mail]

References

Kundu, M. G., and Ghosh, S. (2021). Survival trees based on heterogeneity in time-to-event and censoring distributions using parameter instability test. Statistical Analysis and Data Mining: The ASA Data Science Journal, 14(5), 466-483.

See Also

StabCont.surv, SurvCART, plot, text

Examples

   
#--- time-to-event distribution: exponential, censoring distribution: None    
out1<- StabCont.surv(data=lung, timevar="time", censorvar="status", splitvar="age", event.ind=2) 
out1$pval

#--- time-to-event distribution: weibull, censoring distribution: None  
StabCont.surv(data=lung, timevar="time", censorvar="status", splitvar="age", 
              time.dist="weibull", event.ind=2) 

#--- time-to-event distribution: weibull, censoring distribution: exponential
StabCont.surv(data=lung, timevar="time", censorvar="status", splitvar="age", 
              time.dist="weibull", cens.dist="exponential", event.ind=2) 

LongCART documentation built on May 18, 2022, 1:06 a.m.