ipw (version 1.3.0)

timedat: HIV: TB and Survival (Longitudinal Measurements)

Description

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.

Author

Willem M. van der Wal , Ronald B. Geskus

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. tools:::Rd_expr_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.

See Also

basdat, haartdat, ipwplot, ipwpoint, ipwtm, tstartfun

Examples

# For an example of how to use these longitudinal measurements with basdat, see:
# ?ipwtm