Maintainer: Ehsan Karim: http://www.ehsankarim.com
- This package simulates survival data suitable for fitting Marginal Structural Model.
library(devtools)
install_github("ehsanx/simMSM")require(simMSM)?simmsmsetwd("C:/data") # change working dirsimmsm(subjects = 2500, tpoints = 10, psi = 0.3, n = 1000)
# This code generates 1000 datasets (takes time!)
# 2500 subjects in each datasets
# Each subject followed upto 10 time-points (say, months)
# Causal effect (log-odds) is 0.3| Parameter | Description |
|---|---|
| subjects | Number of Subjects in each simulated dataset |
| tpoints | Maximum number of time-points each subjects are followed |
| psi | Causal effect parameter for Marginal Structural Model |
| n | Number of simulated datasets an user wants to generate |
- Ehsan Karim
(only R porting from the SAS code). I wrote them in R basically to understand the mechanism, but the SAS / SAS IML / Stata codes (I have them as well, available upon request) are faster than this. Feel free to report any errors / update suggestions.
- Young J.G., Hernan M.A., Picciotto S., and Robins J.M. Relation between three classes of structural models for the effect of a time-varying exposure on survival. Lifetime Data Analysis, 16(1):71-84, 2010.
- Young, Jessica G., et al. Simulation from structural survival models under complex time-varying data structures. JSM proceedings, section on statistics in epidemiology. American Statistical Association, Denver, CO (2008)
- Ali R.A., Ali M.A., and Wei Z. On computing standard errors for marginal structural cox models. Lifetime data analysis, pages 1–26, 2013. doi: 10.1007/s10985-013-9255-7.
- Xiao Y., Abrahamowicz M., and Moodie E.E.M. Accuracy of conventional and marginal structural Cox model estimators: A simulation study. The International Journal of Biostatistics, 6(2):1–28, 2010.
- Karim, M. E.; Petkau, J.; Gustafson, P.; Platt, R.; Tremlett, H. and BeAMS study group. Comparison of Statistical Approaches Dealing with Time-dependent Confounding in Drug Effectiveness Studies. Statistical Methods in Medical Research. First published online: September 21. doi: 10.1177/0962280216668554
- Karim, M. E.; Petkau, J.; Gustafson, P.; Tremlett, H. and BeAMS study group. On the application of statistical learning approaches to construct inverse probability weights in marginal structural Cox models: hedging against weight-model misspecification. Communications in Statistics - Simulation and Computation (In Press).