msmjson: msmjson

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

msmjson

Description

Function msmjson can receive models from msm package. It then uses functions from msm package internally to estimate multi-state model measures such as transition probabilities, transition intensity rates, length of stay, probability of visiting each state probability each state is next, sojoutn times and confidence intervals of the estimations. Function flexjson then take these results and reshapes them so that they can be fed to MSMplus properly as a json file.

Usage

msmjson(
  msm.model,
  vartime = seq(1, 1, 1),
  mat.init,
  totlos = FALSE,
  visit = FALSE,
  sojourn = FALSE,
  pnext = FALSE,
  efpt = FALSE,
  envisits = FALSE,
  ci.json = "normal",
  cl.json = 0.95,
  B.json = 50,
  cores.json = NULL,
  piecewise.times.json = NULL,
  piecewise.covariates.json = NULL,
  num.integ.json = FALSE,
  covariates_list = list(),
  jsonpath = "",
  name = "predictions_R.json"
)

Arguments

msm.model	A fitted multi-state model, as returned by msm.
vartime	The time points to estimate the transition probabilities for, by default one unit.
mat.init	A transition intensity matrix. Default: NULL
totlos	Estimate the total length of stay in each state for the time points specified by vartime argument, Default: "FALSE"
visit	Estimate the probability of visiting each state for the time points specified by vartime argument, Default: "FALSE"
sojourn	Estimate the mean sojourn time in each state, Default: "FALSE"
pnext	Estimate the probability that each state is next, Default: "FALSE"
efpt	Estimate the expected first passage time by each state, Default: "FALSE"
envisits	Estimate the number of visits in each state , Default: "FALSE"
ci.json	Estimate confidence intervals, Default: 'normal'
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: 50
cores.json	Number of cores to use for bootstrapping using parallel processing. See boot.msm for more details., Default: NULL
piecewise.times.json	Times at which piecewise-constant intensities change. See pmatrix.piecewise.msm for how to specify this. This is only required for time-inhomogeneous models specified using explicit time-dependent covariates, and should not be used for models specified using "pci"., Default: NULL
piecewise.covariates.json	Covariates on which the piecewise-constant intensities depend. See pmatrix.piecewise.msm for how to specify this., Default: NULL
num.integ.json	Use numerical integration instead of analytic solution (see below).
covariates_list	The user can specify different covariate patterns for which predictions will be made, Default: list()
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

returns a list of objects: the time variable the number of covariate patterns, the names of covariate patterns, the transition matrix, the number of transitions, the transition probabilities, transition intensity rates, length of stay, probability of visiting each state probability each state is next, sojoutn times and confidence intervals of the estimations(if estimated).

See Also

stri_sort

Examples

## Not run: 

#Example

# Multi-state model analysis: Using msmjson function together with msm package

library("MSMplus")
library("msm")
library("dplyr")

options(scipen = 999,"digits"=10)

head(cav)

### Renaming variable PTNUM to id

cav$id=cav$PTNUM

### Defining the transition matrix

tmat=matrix(NA,nrow=4,ncol=4)
tmat[1,2]=1; tmat[1,4]=2; tmat[2,1]=3; tmat[2,3]=4
tmat[2,4]=5; tmat[3,2]=6; tmat[3,4]=7

### We can now call function msboxes_R  

results3_days=MSMplus::msboxes_R(data=cav,id= cav$id, yb=c(0.3,0.5,0.6,0.75), msm=TRUE,
                              xb=c(0.5,0.2,0.7,0.3),boxwidth=0.1,boxheight=0.1,
                              tmatrix= tmat, vartime=seq(0,10,by=1),scale=1,
                              jsonpath="", name="msboxes_cav_R.json" ) 


### Defining the transition matrix with initial values under an initial assumption

#0 for transitions not allowed, initial values for rest of transitions under a rationale ##

Q<- rbind(c(0,0.25,0,0.25),c(0.166,0,0.166,0.166),c(0,0.25,0,0.25),c(0,0,0,0))


### Getting initial Q matrix in a default way- Feed the hand made matrix 

q.crude<- crudeinits.msm(state~years, id,data=cav, qmatrix=Q)


### Apply the msm model

cavsex.msm<- msm(state~years, covariates=~sex, id,data=cav,qmatrix=q.crude, deathexact = 4, control=list(trace=1,REPORT=1)) 
summary(cavsex.msm)


### Prediction for different covariate patterns (males and females)

results <- MSMplus::msmjson(msm.model=cavsex.msm, vartime=seq(1,3,1), mat.init=q.crude,
                         totlos=TRUE, visit=TRUE, sojourn=TRUE, pnext=TRUE, efpt=TRUE, envisits=TRUE,
                         ci.json="normal", cl.json=0.95, B.json=100,
                         cores.json=NULL,piecewise.times.json=NULL, piecewise.covariates.json=NULL,num.integ.json=FALSE,
                         covariates_list=list(list(sex = 1),list(sex = 0)), 
                         jsonpath="",
                         name="predictions_cav_R.json" ) 
                         

## End(Not run)

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