Simulated dataset. Baseline data of 386 HIV positive individuals, including time of first active tuberculosis, time of death, individual end time. Time varying CD4 measurements of these patients are included in dataset timedat.
Usage
data(basdat)Format
A data frame with 386 observations on the following 4 variables:
- id
Patient ID.
- Ttb
Time of first active tuberculosis, measured in days since HIV seroconversion.
- Tdeath
Time of death, measured in days since HIV seroconversion.
- Tend
Individual end time (either death or censoring), measured in days since HIV seroconversion.
Details
These simulated data are used together with data in timedat in a detailed causal modelling example using inverse probability weighting (IPW). See ipwtm for the example. Data were simulated using the algorithm described in Van der Wal e.a. (2009).#'
[Image of a multistate model for HIV, TB, and death]
References
Van der Wal W.M. & Geskus R.B. (2011). ipw: An R Package for Inverse Probability Weighting. Journal of Statistical Software, 43(13), 1-23. doi:10.18637/jss.v043.i13 .
Van der Wal W.M., Prins M., Lumbreras B. & Geskus R.B. (2009). A simple G-computation algorithm to quantify the causal effect of a secondary illness on the progression of a chronic disease. Statistics in Medicine, 28(18), 2325-2337.
Author
Willem M. van der Wal willem@vanderwalresearch.com, Ronald B. Geskus rgeskus@oucru.org