R: Data from the Amsterdam Cohort Studies on HIV infection and...

aidssi {mstate} | R Documentation

## Data from the Amsterdam Cohort Studies on HIV infection and AIDS

### Description

These data sets give the times (in years) from HIV infection to AIDS, SI switch and death in 329 men who have sex with men (MSM). Data are from the period until combination anti-retroviral therapy became available (1996). For more background information on the cohort, ccr5 and SI, see Geskus et al. (2000, 2003)

### Format

aidssi

patnr: | Patient identification number
time: | Time from HIV infection to first of SI appearance and AIDS, or last follow-up
status: | Event indicator; 0 = censored, 1 = AIDS, 2 = SI appearance
cause: | Failure cause; factor with levels "event-free", "AIDS", "SI"
ccr5: | CCR5 genotype; factor with levels "WW" (wild type allele on both chromosomes),
| "WM" (mutant allele on one chromosome)

aidssi2

patnr: | Patient identification number
entry.time: | Time from HIV infection to cohort entry. Value is zero if HIV infection occurred while in follow-up.
aids.time: | Time from HIV infection to AIDS, or last follow-up if AIDS was not observed
aids.stat: | Event indicator with respect to AIDS, with values 0 (censored) and 1 (AIDS)
si.time: | Time from HIV infection to SI switch, or last follow-up if SI switch was not observed
si.stat: | Event indicator with respect to SI switch, with values 0 (no switch) and 1 (switch)
death.time: | Time from HIV infection to death, or last follow-up if death was not observed
death.stat: | Event indicator with respect to death; 0 = alive, 1 = dead
age.inf: | Age at HIV infection
ccr5: | CCR5 genotype; factor with levels "WW" (wild type allele on both chromosomes),
| "WM" (mutant allele on one chromosome)

### Details

`aidssi` contains follow-up data until the first of AIDS and SI switch. It was used as example for the competing risks analyses in Putter, Fiocco, Geskus (2007) and in Geskus (2016).

`aidssi2` extends the `aidssi` data set in three ways. First, it considers events after the initial one. Second, it includes the entry times of the individuals that entered the study after HIV infection. Third, age at HIV infection has been added as extra covariable. Numbers differ slightly from the `aidssi` data set. Some individuals were diagnosed with AIDS only when they died and others had their last follow-up at AIDS diagnosis. In order to prevent two transitions to happen at the same time, their time to AIDS was shortened by 0.25 years. This data set was used as example for the multi-state analyses in Geskus (2016).

### Source

Geskus RB (2000). On the inclusion of prevalent cases in HIV/AIDS natural history studies through a marker-based estimate of time since seroconversion. Statistics in Medicine 19, 1753–1769.

Geskus RB, Miedema FA, Goudsmit J, Reiss P, Schuitemaker H, Coutinho RA (2003). Prediction of residual time to AIDS and death based on markers and cofactors. Journal of AIDS 32, 514–521.

### References

Geskus, Ronald B. (2016). Data Analysis with Competing Risks and Intermediate States. CRC Press, Boca Raton.

Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine 26, 2389–2430.

[Package mstate version 0.3.1 Index]