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

Description

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: "Stochastic model for analysis of longitudinal data on aging and mortality" by Yashin A. et al, 2007, Mathematical Biosciences, 208(2), 538-551 <DOI:10.1016/j.mbs.2006.11.006>.

Author(s)

I. Y. Zhbannikov, I. V. Akushevich, K. G. Arbeev, A. I. Yashin.

References

Yashin, A. et al (2007), Stochastic model for analysis of longitudinal data on aging and mortality. Mathematical Biosciences, 208(2), 538-551.

Akushevich I., Kulminski A. and Manton K. (2005). Life tables with covariates: Dynamic model for Nonlinear Analysis of Longitudinal Data. Mathematical Popu-lation Studies, 12(2), pp.: 51-80. <DOI: 10.1080/08898480590932296>.

Yashin, A. et al (2007), Health decline, aging and mortality: how are they related? Biogerontology, 8(3), 291-302.<DOI:10.1007/s10522-006-9073-3>.

Examples

## Not run:  
library(stpm)
#Prepare data for optimization
data <- prepare_data(x=system.file("data","longdat.csv",package="stpm"), 
				   y=system.file("data","vitstat.csv",package="stpm"))
#Parameters estimation (default model: discrete-time):
p.discr.model <- spm(data)
p.discr.model
# Continuous-time model:
p.cont.model <- spm(data, model="continuous")
p.cont.model
#Model with time-dependent coefficients:
data <- prepare_data(x=system.file("data","longdat.csv",package="stpm"), 
				   y=system.file("data","vitstat.csv",package="stpm"), 
				   covariates="BMI")
p.td.model <- spm(data, model="time-dependent")
p.td.model

## End(Not run)

Results