Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes

Utilities to estimate parameters of the models with survival functions induced by stochastic covariates. Miscellaneous functions for data preparation and simulation are also provided. For more information, see: (i)"Stochastic model for analysis of longitudinal data on aging and mortality" by Yashin A. et al. (2007), Mathematical Biosciences, 208(2), 538-551, ; (ii) "Health decline, aging and mortality: how are they related?" by Yashin A. et al. (2007), Biogerontology 8(3), 291(302), .


Features

Data simulation

  • Continuous (one- and multiple-dimensions)
  • Discrete (one- and multiple-dimensions)

Optimisation

  • Continuous (one- and multiple-dimensions)
  • Discrete (one- and multiple-dimensions)
  • Time-dependant coefficients (one-dimensional optimisation)

Data imputation (censored time-to-event data imputation)

How to install

Note: for Windows, please install Rtools: https://cran.r-project.org/bin/windows/Rtools/ Note: compilation under Windows may fail if you run Windows on a virtual machine! Then:

install.packages("devtools")
library(devtools)
install_github("izhbannikov/spm")

Examples

Test discrete simulation

library(stpm)
## Test discrete
data <- simdata_discr(N=1000)
pars <- spm_discrete(data)
pars

Test projection

library(stpm)
# Setting up the model
model.par <- list()
model.par$a <- matrix(c(-0.05, 1e-3, 2e-3, -0.05), nrow=2, ncol=2, byrow=TRUE)
model.par$f1 <- matrix(c(90, 35), nrow=1, ncol=2)
model.par$Q <- matrix(c(1e-8, 1e-9, 1e-9, 1e-8), nrow=2, ncol=2, byrow=TRUE)
model.par$f <- matrix(c(80, 27), nrow=1, ncol=2)
model.par$b <- matrix(c(6, 2), nrow=2, ncol=2)
model.par$mu0 <- 1e-5
model.par$theta <- 0.11
# Projection
# Discrete-time model
data.proj.discrete <- spm_projection(model.par, N=100, ystart=c(80, 27), tstart=c(30, 60))
plot(data.proj.discrete$stat$srv.prob, xlim = c(30, 115))
# Continuous-time model
data.proj.continuous <- spm_projection(model.par, N=100, ystart=c(80, 27), model="continuous", gomp=TRUE)
plot(data.proj.continuous$stat$srv.prob, xlim = c(30, 115))

Time-dependent model

library(stpm)
model.par <- list(at = "-0.05", f1t = "80", Qt = "2e-8", ft= "80", bt = "5", mu0t = "1e-5*exp(0.11*t)")
data.proj.time_dependent <- spm_projection(model.par, N=100, ystart=80, model="time-dependent")
plot(data.proj.time_dependent$stat$srv.prob, xlim = c(30,105))

Test prepare_data()

library(stpm)
data <- prepare_data(x=system.file("data","longdat.csv",package="stpm"))
head(data[[1]])
head(data[[2]])

Test sim_pobs()

library(stpm)
dat <- sim_pobs(N=500)
head(dat)

Test spm_pobs()

library(stpm)
#Reading the data:
data <- sim_pobs(N=100)
head(data)
#Parameters estimation:
pars <- spm_pobs(x=data)
pars

Test simdata_cont()

library(stpm)
dat <- simdata_cont(N=50)
head(dat)

Test simdata_discr()

library(stpm)
data <- simdata_discr(N=100)
head(data)

Test simdata_time-dep()

library(stpm)
dat <- simdata_time_dep(N=100)
head(dat)

Test spm_continuous()

library(stpm)
data <- simdata_cont(N=50)
pars <- spm_continuous(dat=data,a=-0.05, f1=80, Q=2e-8, f=80, b=5, mu0=2e-5)
pars

Test spm_discrete()

library(stpm)
data <- simdata_discr(N=10)
pars <- spm_discrete(data)
pars

Test spm()

library(stpm)
data.continuous <- simdata_cont(N=1000)
data.discrete <- simdata_discr(N=1000)
data <- list(data.continuous, data.discrete)
p.discr.model <- spm(data)
p.discr.model
p.cont.model <- spm(data, model="continuous")
p.cont.model
p.td.model <- spm(data, model="time-dependent",formulas=list(at="aa*t+bb", f1t="f1", Qt="Q", ft="f", bt="b", mu0t="mu0"), start=list(a=-0.001, bb=0.05, f1=80, Q=2e-8, f=80, b=5, mu0=1e-3))
p.td.model

Multiple imputation with spm.impute(...)

The SPM offers longitudinal data imputation with results that are better than from other imputation tools since it preserves data structure, i.e. relation between Y(t) and mu(Y(t),t). Below there are two examples of multiple data imputation with function spm.impute(...).

library(stpm)

#######################################################
############## One dimensional case ###################
#######################################################

# Data preparation (short format)#
data <- simdata_discr(N=1000, dt = 2, format="short")

miss.id <- sample(x=dim(data)[1], size=round(dim(data)[1]/4)) # ~25% missing data
incomplete.data <- data
incomplete.data[miss.id,4] <- NA
# End of data preparation #

##### Multiple imputation with SPM #####
imp.data <- spm.impute(x=incomplete.data, id=1, case="xi", t1=3, covariates="y1", minp=1, theta_range=seq(0.075, 0.09, by=0.001))$imputed

##### Look at the incomplete data with missings #####
head(incomplete.data)

##### Look at the imputed data #####
head(imp.data)


#########################################################
################ Two-dimensional case ###################
#########################################################

# Parameters for data simulation #
a <- matrix(c(-0.05, 0.01, 0.01, -0.05), nrow=2)
f1 <- matrix(c(90, 30), nrow=1, byrow=FALSE)
Q <- matrix(c(1e-7, 1e-8, 1e-8, 1e-7), nrow=2)
f0 <- matrix(c(80, 25), nrow=1, byrow=FALSE)
b <- matrix(c(5, 3), nrow=2, byrow=TRUE)
mu0 <- 1e-04
theta <- 0.07
ystart <- matrix(c(80, 25), nrow=2, byrow=TRUE)

# Data preparation #
data <- simdata_discr(N=1000, a=a, f1=f1, Q=Q, f=f0, b=b, ystart=ystart, mu0 = mu0, theta=theta, dt=2, format="short")

# Delete some observations in order to have approx. 25% missing data
incomplete.data <- data
miss.id <- sample(x=dim(data)[1], size=round(dim(data)[1]/4)) 
incomplete.data <- data
incomplete.data[miss.id,4] <- NA
miss.id <- sample(x=dim(data)[1], size=round(dim(data)[1]/4)) 
incomplete.data[miss.id,5] <- NA
# End of data preparation #

##### Multiple imputation with SPM #####
imp.data <- spm.impute(x=incomplete.data, id=1, case="xi", t1=3, covariates=c("y1", "y2"), minp=1, theta_range=seq(0.060, 0.07, by=0.001))$imputed

##### Look at the incomplete data with missings #####
head(incomplete.data)

##### Look at the imputed data #####
head(imp.data)


News

New for version 1.7.7

  • Version released
  • spm_con_1d_g: genetic test for parameter f1 added.

Reference manual

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install.packages("stpm")

1.7.7 by Ilya Y. Zhbannikov, 5 months ago


Browse source code at https://github.com/cran/stpm


Authors: I. Y. Zhbannikov , Liang He , K. G. Arbeev , A. I. Yashin.


Documentation:   PDF Manual  


GPL license


Imports sas7bdat, stats, nloptr, survival, tools, knitcitations, MASS

Depends on Rcpp

Suggests knitr

Linking to Rcpp, RcppArmadillo


See at CRAN