European Bone Marrow Transplantation data obtained from mstate r package

Description

A multi state dataset

Usage

data(EBMTdata)

Format

a tibble of 13 columns and 2204 observations,

id

id value for subjects

prtime

Time in days from transplantation to platelet recovery or last follow-up

prstate

Platelet recovery status; 1 = platelet recovery, 0 = censored

rfstime

Time in days from transplantation to relapse or death or last follow-up (relapse-free survival time)

rfsstate

Relapse-free survival status; 1 = relapsed or dead, 0 = censored

dissub

Disease subclassification; factor with levels "AML", "ALL", "CML"

age

Patient age at transplant; factor with levels "<=20", "20-40", ">40"

drmatch

Donor-recipient gender match; factor with levels "No gender mismatch", "Gender mismatch"

tcd

T-cell depletion; factor with levels "No TCD", "TCD"

x1,x2,x3,x4

simulated covariate information used for SPSM

Source

We acknowledge that this data set is obtained from the r package mstate. We have included four continuous covariates in the dataset to demonstrate SPSM method in multistate survival model.

References

de Wreede, L. C., Fiocco, M., & Putter, H. (2011). mstate: an R package for the analysis of competing risks and multi-state models. Journal of statistical software, 38, 1-30.

Vishwakarma, G. K., Bhattacherjee, A., Rajbongshi, B. K., & Tripathy, A. (2024). Censored imputation of time to event outcome through survival proximity score method. Journal of Computational and Applied Mathematics, 116103;

Bhattacharjee, A., Vishwakarma, G. K., Tripathy, A., & Rajbongshi, B. K. (2024). Competing risk multistate censored data modeling by propensity score matching method. Scientific Reports, 14(1), 4368.

European Bone Marrow Transplantation data obtained from mstate r package. This is the updated data obtained after applying SPSM.

Description

A multi state dataset

Usage

data(EBMTupdate)

Format

a tibble of 13 columns and 2204 observations,

id

id value for subjects

prtime

Time in days from transplantation to platelet recovery or last follow-up

prstate

Platelet recovery status; 1 = platelet recovery, 0 = censored

rfstime

Time in days from transplantation to relapse or death or last follow-up (relapse-free survival time)

rfsstate

Relapse-free survival status; 1 = relapsed or dead, 0 = censored

dissub

Disease subclassification; factor with levels "AML", "ALL", "CML"

age

Patient age at transplant; factor with levels "<=20", "20-40", ">40"

drmatch

Donor-recipient gender match; factor with levels "No gender mismatch", "Gender mismatch"

tcd

T-cell depletion; factor with levels "No TCD", "TCD"

x1,x2,x3,x4

simulated covariate information used for SPSM

Source

We acknowledge that this data set is obtained from the r package mstate. We have included four continuous covariates in the dataset to demonstrate SPSM method in multistate survival model.

References

de Wreede, L. C., Fiocco, M., & Putter, H. (2011). mstate: an R package for the analysis of competing risks and multi-state models. Journal of statistical software, 38, 1-30.

Vishwakarma, G. K., Bhattacherjee, A., Rajbongshi, B. K., & Tripathy, A. (2024). Censored imputation of time to event outcome through survival proximity score method. Journal of Computational and Applied Mathematics, 116103;

Bhattacharjee, A., Vishwakarma, G. K., Tripathy, A., & Rajbongshi, B. K. (2024). Competing risk multistate censored data modeling by propensity score matching method. Scientific Reports, 14(1), 4368.

CoxPH model with parametric baseline and frailty terms

Description

Function for estimating the parameters of coxPH model with frailty terms

Usage

cphGM(
  formula,
  fterm,
  Time,
  status,
  id,
  data,
  bhdist,
  method = "L-BFGS-B",
  maxit = 200
)

Arguments

Details

The hazard model is as follows:

h_i(t)=z_ih_0(t)exp(\textbf{x}_i\beta)\;;i=1,2,3,...,n

where baseline survival distribution could be Weibull distribution and the hazard function is:

h_0(t)=\rho \lambda t^{\rho-1}

. Similarly we can have Expoenetial, log logistic distribution. The following are the formula for hazard and cummulative hazard function For exponential: h_0(t)=\lambda and H_0(t)=\lambda t\;\lambda>0 Gompertz: h_0(t)=\lambda exp(\gamma t) and H_0(t)=\frac{\lambda}{\gamma}(exp(\gamma t)-1);\lambda,\gamma>0 The frailty term z_i follows Gamma distribution with parameter \theta. The parameter estimates are obtained by maximising the log likelihood

\prod_{i=1}^{n}l_i(\beta,\theta,\lambda,\rho)

The method argument allows the user to select suitable optimisation method available in optim function.

Value

Estimates obtained from coxph model with the frailty terms.

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra K. Vishwakarma

References

Vishwakarma, G. K., Bhattacherjee, A., Rajbongshi, B. K., & Tripathy, A. (2024). Censored imputation of time to event outcome through survival proximity score method. Journal of Computational and Applied Mathematics, 116103;

Bhattacharjee, A., Vishwakarma, G. K., Tripathy, A., & Rajbongshi, B. K. (2024). Competing risk multistate censored data modeling by propensity score matching method. Scientific Reports, 14(1), 4368.

See Also

dscore,simfdata

Examples

##
X1<-matrix(rnorm(1000*2),1000,2)
simulated_data<-simfdata(n=1000,beta=c(0.5,0.5),fvar=0.5,
X=X1)
model1<-cphGM(formula=Surv(time,status)~X1+X2,
fterm<-c('gamma','id'),Time="time",status="status",
id="id",data=simulated_data,bhdist='weibull')
model1
##

Survival Proximity Score matching for MSM

Description

function for survival proximity score matching in multistate model with three state.

Usage

dscore(status, data, prob, m, n, method = "euclidean")

Arguments

Value

list with newdataset updated using dscore

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra K. Vishwakarma

References

Vishwakarma, G. K., Bhattacherjee, A., Rajbongshi, B. K., & Tripathy, A. (2024). Censored imputation of time to event outcome through survival proximity score method. Journal of Computational and Applied Mathematics, 116103;

Bhattacharjee, A., Vishwakarma, G. K., Tripathy, A., & Rajbongshi, B. K. (2024). Competing risk multistate censored data modeling by propensity score matching method. Scientific Reports, 14(1), 4368.

See Also

cphGM,simfdata

Examples

##s
data(simulated_data)
udata<-dscore(status="status",data=simulated_data,prob=0.65,m=4,n=7)
##

Exponential baseline hazard

Description

Exponential baseline hazard

Usage

expbh(t, shape = 2)

Arguments

Value

hazard function value under Exponential distibution

Reciever Operating Curve

Description

this function provides roc plot for coxph model fitted before and after survival proximity score matching.

Usage

ggplot_roc(
  trns,
  model1,
  model2,
  data1,
  data2,
  folder_path = NULL,
  times = NULL
)

Arguments

Value

returns roc plot for model1 and model2

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma

References

Vishwakarma, G. K., Bhattacherjee, A., Rajbongshi, B. K., & Tripathy, A. (2024). Censored imputation of time to event outcome through survival proximity score method. Journal of Computational and Applied Mathematics, 116103;

Bhattacharjee, A., Vishwakarma, G. K., Tripathy, A., & Rajbongshi, B. K. (2024). Competing risk multistate censored data modeling by propensity score matching method. Scientific Reports, 14(1), 4368.

See Also

dscore, simfdata, cphGM

Examples

##
library(mstate)
data(EBMTdata)
data(EBMTupdate)
tmat<-transMat(x=list(c(2,3),c(3),c()),
               names=c("Tx","Rec","Death"))
covs<-c("dissub","age","drmatch","tcd","prtime","x1","x2","x3","x4")
msbmt<-msprep(time=c(NA,"prtime","rfstime"),
              status=c(NA,"prstat","rfsstat"),
              data=EBMTdata,trans=tmat,keep=covs)
msbmt1<-msprep(time=c(NA,"prtime","rfstime"),
               status=c(NA,"prstat","rfsstat"),
               data=EBMTupdate,trans=tmat,keep=covs)
msph3<-coxph(Surv(time,status)~dissub+age+drmatch+tcd+
frailty(id,distribution='gamma'),data=msbmt[msbmt$trans==3,])
msph33<-coxph(Surv(Tstart,Tstop,status)~dissub+age +drmatch+ tcd+
frailty(id,distribution='gamma'),data=msbmt1[msbmt1$trans==3,])
ggplot_roc(trns=3,model1=msph3,model2=msph33,
           data1=msbmt,data2=msbmt1)
##

Survival probability plot

Description

it gives plot with fitted survival curve obtained from two different coxPH model fitted before and after SPSM

Usage

ggplot_surv(model1, model2, data1, data2, n_trans, id)

Arguments

Value

plot for survival curve of a particular id obtained from both the model

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma

See Also

dscore, simfdata, cphGM

Examples

##
library(mstate)
data(EBMTdata)
data(EBMTupdate)
tmat<-transMat(x=list(c(2,3),c(3),c()),names=c("Tx","Rec","Death"))
covs<-c("dissub","age","drmatch","tcd","prtime","x1","x2","x3","x4")
msbmt<-msprep(time=c(NA,"prtime","rfstime"),status=c(NA,"prstat","rfsstat"),
             data=EBMTdata,trans=tmat,keep=covs)
msbmt1<-msprep(time=c(NA,"prtime","rfstime"),status=c(NA,"prstat","rfsstat"),
              data=EBMTupdate,trans=tmat,keep=covs)
msph3<-coxph(Surv(time,status)~dissub+age+drmatch+tcd+
             frailty(id,distribution='gamma'),data=msbmt[msbmt$trans==3,])
msph33<-coxph(Surv(Tstart,Tstop,status)~dissub+age +drmatch+ tcd+
              frailty(id,distribution='gamma'),data=msbmt1[msbmt1$trans==3,])
ggplot_surv(model1=msph3,model2=msph33,data1=msbmt,
           data2=msbmt1,n_trans=3,id=1)
#####
# plot1<-ggplot_surv(model1=msph3,model2=msph33,data1=msbmt,data2=msbmt1,
# ggsave("plot1.jpg",path="C:/Users/.....")
#####
##

Gompartz baseline hazard

Description

Gompartz baseline hazard

Usage

gompbh(t, shape = 2, scale = 1)

Arguments

Value

hazard function value under Gompartz distibution

print function for cphGM

Description

S3 print method for class 'cphGM'

Usage

## S3 method for class 'cphGM'
print(x, ...)

Arguments

Value

prints table containing various parameter estimates, SE, P-value.

Examples

##
n1<-1000
p1<-2
X1<-matrix(rnorm(n1*p1),n1,p1)
simulated_data<-simfdata(n=1000,beta=c(0.5,0.5),fvar=0.5,X=X1)
model1<-cphGM(formula=Surv(time,status)~X1+X2,
fterm=c('gamma','id'),Time="time",status="status",
id="id",data=simulated_data,bhdist='weibull')
print(model1)
##

simulation of survival data

Description

function for simulation of survival data assuming the data comes from a parametric coxph model with gamma frailty distribution

Usage

simfdata(n, beta, fvar, bhdist = "weibull", X, fdist = "gamma", ...)

Arguments

Details

The process for simulation of multistate survival data is described in our manuscript. As the process includes transition through different states and it involves simulating survival time in different transition. So we have demonstrated the code for simulation of simple survival model. Suppose we want to simulate a survival data with parametric baseline hazard and parametric frailty model. The hazard model is as follows:

h_i(t)=z_ih_0(t)exp(\textbf{x}_i\beta)\;;i=1,2,3,...,n

where the baseline survival time follow Weibull distribution and the hazard is

h_0(t)=\rho \lambda t^{\rho-1}

. Similarly we can have Gompertz, log logistic distribution. The following are the formula for hazard and cummulative hazard function For exponential: h_0(t)=\lambda and H_0(t)=\lambda t\;\lambda>0 Gompertz: h_0(t)=\lambda exp(\gamma t) and H_0(t)=\frac{\lambda}{\gamma}(exp(\gamma t)-1);\lambda,\gamma>0

Value

simulated survival data for a single transition

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra K. Vishwakarma

References

Vishwakarma, G. K., Bhattacherjee, A., Rajbongshi, B. K., & Tripathy, A. (2024). Censored imputation of time to event outcome through survival proximity score method. Journal of Computational and Applied Mathematics, 116103;

Bhattacharjee, A., Vishwakarma, G. K., Tripathy, A., & Rajbongshi, B. K. (2024). Competing risk multistate censored data modeling by propensity score matching method. Scientific Reports, 14(1), 4368.

See Also

cphGM

Examples

##
n1<-1000
p1<-2
X1<-matrix(rnorm(n1*p1),n1,p1)
simulated_data<-simfdata(n=1000,beta=c(0.5,0.5),fvar=0.5,
X=X1)
##

Simulated multistate data

Description

A simulated multi state dataset used for demonstration purpose.

Usage

data(simulated_data)

Format

a tibble of 13 columns and 2204 observations,

id

id value for subjects

status

survival status

time

survival time

x1

Numeric covariate

x2

Numeric covariate

x3

Numeric covariate

x4

Numeric covariate

References

Vishwakarma, G. K., Bhattacherjee, A., Rajbongshi, B. K., & Tripathy, A. (2024). Censored imputation of time to event outcome through survival proximity score method. Journal of Computational and Applied Mathematics, 116103;

Bhattacharjee, A., Vishwakarma, G. K., Tripathy, A., & Rajbongshi, B. K. (2024). Competing risk multistate censored data modeling by propensity score matching method. Scientific Reports, 14(1), 4368.

Weibull baseline hazard

Description

Weibull baseline hazard

Usage

weibulbh(t, shape = 2, scale = 1)

Arguments

Value

hazard function value under Weibull distibution