View source: R/StabCont_surv.R
StabCont.surv | R Documentation |
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.
StabCont.surv(data, timevar, censorvar, splitvar, time.dist="exponential", cens.dist="NA", event.ind=1, print=FALSE)
data |
name of the dataset. It must contain variable specified for |
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: |
cens.dist |
name of censoring distribution. It can be one of the following distributions: |
event.ind |
value of the censoring variable indicating event. |
splitvar |
the continuous partitioning variable of interest. |
print |
if |
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]
pval |
p-value for parameter instability test |
type |
1, if event times are more heterogeneous; 2, if censoring times are more hetergeneous. |
Madan Gopal Kundu madan_g.kundu@yahoo.com
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.
StabCont.surv
, SurvCART
, plot
, text
#--- 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)
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