npde (version 3.5)

virload: Simulated HIV viral loads in HIV patients

Description

This is simulated data, based on real data obtained in a phase II clinical trial supported by the French Agency for AIDS Research, the COPHAR 3-ANRS 134 trial (Goujard et al., 2010). The original study included 35 patients, who received a once daily dose containing atazanavir (300 mg), ritonavir (100 mg), tenofovir disoproxil (245 mg) and emtricitabine (200 mg) during 24 weeks. Viral loads were measured 6 times over a treatment period of 24 weeks (day 0, 28, 56, 84, 112, 168).

Usage

virload

Format

This data frame contains the following columns:

ID

an ordered factor with levels 1, ..., 50 identifying the subject on whom the observation was made. The ordering is by Time at which the observation was made.

Time

time since the beginning of the study (days)

Log_VL

logarithm (base 10) of the viral load (copies/L)

cens

indicator variable (cens=1 for censored data, cens=0 for observed data)

ipred

individual predictions

Details

The datasets were generated in a simulation study designed to evaluate the new method proposed to handle BQL data (Nguyen et al., 2011). Data was simulated using a simple bi-exponential HIV dynamic model describing the two-phase decline of viral load during anti-retroviral treatment.

The virload data frame has 300 rows and 4 columns of data. The dataset was then censored at two different LOQ levels (LOQ=20 or 50~copies/mL) to generate two datasets containing different proportions of BQL data, creating the data frames virload20 andvirload50 respectively

The file simvirload contains 500 simulations under the same model. A full version of the simulated data with 1000 simulations can be downloaded from the github for npde3.0: https://github.com/ecomets/npde30/blob/main/keep/data/simvirload.tab

References

PDF documentation for npde 3.0: https://github.com/ecomets/npde30/blob/main/userguide_npde_3.0.pdf

Examples

# \donttest{
  data(virload)
  str(virload)
  data(virload50)
  # Plotting the data
  plot(Log_VL~Time,data=virload,xlab="Time (d)",ylab="Viral loads, base 10 log-scale (cp/mL)")
  plot(Log_VL~Time,data=virload50,xlab="Time (d)",ylab="Viral loads, base 10 log-scale (cp/mL)")
# }