mstatejson: mstatejson

In nskourlis/MSMplus: Prepare MSMplus input files and locally deploys the MSMplus app

mstatejson

Description

Function mstatejson can receive models from mstate package. It then uses functions from mstate package internally to estimate multi-state model measures such as transition probabilities, length of stay, and confidence intervals of the estimations. Function mstatejson then take these results and reshapes them so that they can be fed to MSMplus properly as a json file.

Usage

mstatejson(
  x,
  qmat,
  process = "Markov",
  totlos = FALSE,
  ci.json = FALSE,
  cl.json = 0.95,
  B.json = 10,
  variance = FALSE,
  vartype = "aalen",
  covariates_list = list(),
  Mjson = 50,
  jsonpath = "",
  name = "predictions.json"
)

Arguments

x	The hazard model (or list of hazard models)
qmat	he user has to supply the transition matrix
process	"Markov" for clock forward approach, "semiMarkov" for clock reset approach, Default: 'Markov'
totlos	Estimate total length of stay spent in each state "TRUE", "FALSE", Default: "FALSE"
ci.json	Estimate confidence intervals, "TRUE", "FALSE", Default: "FALSE"
cl.json	Specify confidence level, Default: 0.95
B.json	Number of simulations from the normal asymptotic distribution used to calculate variances. Decrease for greater speed at the expense of accuracy, Default: 100
variance	A logical value indicating whether the (co-)variances of the subject-specific transition hazards should be computed. Default is FALSE
vartype	A character string specifying the type of variances to be computed (so only needed if variance=TRUE). Possible values are "aalen" or "greenwood"
covariates_list	The user can specify different covariate patterns for which predictions will be made, Default: list()
Mjson	Number of individuals to simulate in order to approximate the transition probabilities. Users should adjust this to obtain the required precision. Default: 100
jsonpath	specify the path of the folder that the json file should be saved, Default: "" saves the json file to the current working directory
name	Specify the name of the output json file, Default: 'predictions.json'

Details

DETAILS

Value

OUTPUT_DESCRIPTION

See Also

stri_sort

Examples

## Not run: 

 #EXAMPLE
 
library("MSMplus")
library("survival")
library("mstate")
library("dplyr")



head(ebmt)
### Let's first define the transition matrix

tmat <- transMat(x = list(c(2, 3),c(3), c() ), names = c("Transplant", "Platelet Recovery", "Relapse/Death" ) )

### We will now create dummy variables for the age categories

ebmt$age2=  recode(ebmt$age, ">40" =0, "20-40"=1,"<=20" =0 )
ebmt$age3=  recode(ebmt$age, ">40" =1, "20-40"=0,"<=20" =0 )

#Data preparation- From one row per participant to multiple rows per participant, one for each allowed transition.
msebmt <- msprep(data = ebmt, trans = tmat, 
                time = c(NA, "prtime", "rfstime"), status = c(NA, "prstat", "rfsstat"), keep=c("age2","age3"))

### Semi parametric analysis

   #### Markov
### Run the hazard models: Clock forward approach 

cfcox <- coxph(Surv(Tstart, Tstop, status) ~age2+age3+strata(trans), data = msebmt)


### Prediction for different covariate patterns (the 3 age categories)

wh1 <- which(msebmt$age2 == 0 & msebmt$age3 == 0)
pat1 <- msebmt[rep(wh1[1], 3), 9:10]
pat1$trans <- 1:3
attr(pat1, "trans") <- tmat
pat1$strata <- pat1$trans


wh2 <- which(msebmt$age2 == 1 & msebmt$age3 == 0)
pat2 <- msebmt[rep(wh2[1], 3), 9:10]
pat2$trans <- 1:3
attr(pat2, "trans") <- tmat
pat2$strata <- pat2$trans

wh3 <- which(msebmt$age2 == 0 & msebmt$age3 == 1)
pat3 <- msebmt[rep(wh3[1], 3), 9:10]
pat3$trans <- 1:3
attr(pat3, "trans") <- tmat
pat3$strata <- pat3$trans


##We now run the mstatejson function to perform the multi-state model analysis using the function 
##from package mstate and the pack the predictions in a json file.

results_semipar <- MSMplus::mstatejson(x=cfcox,  qmat=tmat, process="Markov", 
                                    totlos=TRUE, ci.json=TRUE, cl.json=0.95, B.json=10,
                                    variance=FALSE, vartype="greenwood",
                                    covariates_list=list(pat1 ,pat2, pat3 ) , Mjson=50,
                                    jsonpath="",
                                    name="predictions_EBMT_mstate_fw.json")


results_semipar
 

## End(Not run)

nskourlis/MSMplus documentation built on Dec. 7, 2023, 4:53 a.m.