A simulated dataset containing time-varying CD4 measurements for 386
HIV-positive individuals. Corresponding baseline data, including timing
of tuberculosis and death, are available in basdat.
Usage
data(timedat)Format
A data frame with 6291 observations on the following 3 variables:
- id
Patient ID.
- fuptime
Follow-up time (days since HIV seroconversion).
- cd4count
CD4 count measured at
fuptime.
Details
These simulated data are used together with basdat in a
detailed causal modeling example using inverse probability weighting (IPW).
See ipwtm for the full example. Data were simulated using
the algorithm described in Van der Wal et al. (2009).
References
Cole, S.R. & Hernán, M.A. (2008). Constructing inverse probability weights for marginal structural models. American Journal of Epidemiology, 168(6), 656-664.
Robins, J.M., Hernán, M.A. & Brumback, B.A. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550-560.
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