Simulated dataset. Time varying CD4 measurements of 386 HIV positive individuals. Time of first active tuberculosis, time of death and individual end time of the patients are included in dataset `basdat`

.

1 |

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.

These simulated data are used together with data in `basdat`

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).

Willem M. van der Wal w.m.vanderwal@amc.uva.nl

Cole, S.R. & Hern<e1>n, M.A. (2008). Constructing inverse probability weights for marginal structural models. *American Journal of Epidemiology*, **168**(6), 656-664.

Robins, J.M., Hern<e1>n, 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. http://www.jstatsoft.org/v43/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.

`basdat`

, `haartdat`

, `ipwplot`

, `ipwpoint`

, `ipwtm`

, `timedat`

, `tstartfun`

.

1 | ```
#See ?ipwtm for example
``` |

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