Virological decay after commencing HAART: proposal for analyses of UK CHIC data
(Proposal from Rachel Hughes at the University of Bristol based on an idea originally put forward by
John Walsh at one of our SC meetings. Circulated to SC 24/04/2013 by CS)
Background
A significant proportion of individuals commences HAART with a high viral load and may take
months to fully suppress their viraemia. It has been shown that viral load decreases rapidly
within the first week of starting treatment1,2,3. However, after the first week, and within the first
months, the rate of decline is much slower, due to the release of HIV viruses by macrophages
and other long-lived cells of the lymph nodes3,4. After 2-3 months some individuals may develop
HIV drug resistance such that their viral load increases sharply, possibly reaching pre-treatment
levels. For those individuals who have not yet developed HIV drug resistance, their viral loads
are expected to continue declining slowly, eventually becoming undetectable.
During the slow decline phase clinicians may be tempted to increase monitoring or switch drug
therapy, even when this slow decline is predictable and the individual is expected to achieve viral
suppression.
Aim
The aim is to develop a model that can predict time to suppression (undetectable viral load) after
commencing treatment based on repeated post-treatment measurements of viral load.
Population
All patients in UK-CHIC that:
1. were antiretroviral naive at baseline,
2. started HAART including an NNRTI, boosted PI or integrase inhibitor (if numbers
permit);
3. have a CD4 measurement and viral load measurement 0-3 months before starting
HAART;
4. have two or more post-HAART viral load measurements within the first year.
End point
 HIV viral load below assay limit of quantification;
o Complication: Data will contain viral load measurements with differing assay
limits of quantification. Use the upper limit of quantification (200 copies per ml);
o Complication: Sensitivity analyses will assess time to confirmed suppression (two
consecutive measurements below 200 copies per ml).
Censor data
 Switch of drug regime
 Lost to follow up
Data analysis
The proposed model must account for:
1. the longitudinal structure of the data
2. non-linear trends of the viral load trajectories
3. censoring due to dropout or switch of drug-regimen
Longitudinal structure
The repeated measurements of viral load will be modelled in a mixed-effects framework, which
accounts for variability between individuals and within individuals.
Non-linear trajectories
To model the viral load trajectories I will consider the following:
1. Fractional polynomials (within a linear mixed framework);
2. Linear piece-wise model with either:
a. fixed number of knots (change-points) and locations for all individuals;
or
b. number of knots and locations can vary among individuals5;
3. Non-linear mixed effects model.
Censoring
The mixed-effects model will include all individuals’ viral load measurements up to time of
censoring or end-point. Two approaches to account for censoring will be considered:
1. Mixed-effects model of viral load measurements only, with inverse-probability weighting
to account for informative censoring;
2. Joint model of viral load and time to dropout or switch of drug regime
Without inverse probability weighting, Option 1 above assumes that censoring (or missing data)
due to dropout or switching regime is only dependent upon past observed viral load
measurements. For example, a clinician switches a drug-regimen based on the slow decline in
viral load measurements.
If the reasons for censoring are dependent upon other factors (such as drug toxicity or based on
CD4 measurements) then the censoring is referred to as “informative”. In the presence of
informative censoring Option 1 may lead to biased parameter estimates. Informative censoring
can be accounted for using inverse probability weighting (Option 1) or joint modelling of the
longitudinal marker and the censoring mechanism6 (Option 2).
Outcomes
 Generate tables of risk of virological failure for key time points (in line with BHIVA
monitoring guidelines );
 Generate an online risk prediction tool;
 Consult with experts (such as John Walsh) to further develop tools based on this work
that would be useful for clinicians.
Utility
 Reduce patient anxiety about slow VL decline;
 Reduce unnecessary repeat VL and genotypic testing (and perhaps even treatment
switches) among patients with slow decline rates or high baseline viral loads.
References
1. Ho, D. D., Neumann, A. U., Perelson, A. S., Chen, W., Leonard, J. M., and Makowitz, M. (1995).
Rapid turnover of plasma virus and CD4 lymphocytes in HIV-1 infection, Nature, 373, 123–126.
2. Wei, X., Ghosh, S. K., Taylor, M. E., Johnson, V. A., Emini, E. A., Deutsch, P., Lifson, J. D.,
Bonhoeffer, S., Nowak, M. A., Hahn, B. H., Saag, M. S., and Shaw, G. M. (1995). Viral
dynamics in human immunodeficiency virus type 1 infection, Nature, 373: 117-122.
3. Tan, W. Y. (2000). Stochastic Modeling of AIDS Epidemiology and HIV Pathogenesis, World
Scientific, River Edge, New Jersey.
4.
Perelson, A. S., Essunger, O., Cao, Y. Z., Vesanen, M., Hurley, A., Saksela, K.,
Markowitz, M., and Ho, D. D. (1997). Decay characteristics of HIV infected
compartments during combination therapy, Nature, 387, 188–191.
5. Huang, Y. (2012) Segmental modelling of viral load changes for HIV longitudinal data
with skewness and detection limits. Statistics in Medicine, DOI: 10.1002/sim.5527
6. Philipson, P.M., Ho, W.K. and Henderson, R. (2008) Comparative review of methods for
handling drop-out in longitudinal studies. Statistics in Medicine, 27: 6276-6298.