30BMA AN INDIRECT COMPARISON OF TWO NEW HIV TREATMENTS FOR TREATMENT EXPERIENCED PATIENTS

Sunday, October 19, 2008
Columbus A-C (Hyatt Regency Penns Landing)
Neil Hawkins, PhD, University of York, York, United Kingdom and Andrew Davis, MSc, Oxford Outcomes, Oxford, United Kingdom

Background: Two new agents, Etravarine (ETR, TMC 125) and Raltegravir (RAL), have recently been compared to placebo in the presence of optimized background regimen (OBR), including darunavir/ritonavir (DRV/r), in four trials: DUET 1 & 2 (ETR) and BENCHMRK 1 & 2 (RAL). No head-to-head trials comparing ETR and RAL are available, so an indirect comparison of the probability of attaining the HIV RNA <50 copies/mL endpoint at week 24 was made between the two treatments.

The trials share similiar inclusion criteria and studied similar treatment experienced HIV-1 infected patients, but they differed in the composition of the OBR, especially the use of DRV/r.  All patients received background DRV/r in the DUET trials, whilst less than half received background DRV/r in the BENCHMRK trials The indirect comparison was adjusted for differences in background DRV/r between trials to obtain estimates of treatment effect assuming that all patients received background DRV/r.

Methods: The indirect comparison was made assuming that odds ratios were comparable between trials and that treatment effect modification due to background DRV/r was constant on the odds ratio scale. An analysis of subgroup data from the BENCHMRK trials provided a prior estimate of the treatment effect modification due DRV/r; this was used in the final Bayesian analysis.

Results: Adjusting for background DRV/r use alters the ranking of the mean treatment effect (odds-ratios) for the two treatments although the confidence intervals do overlap (see figure).

Conclusions: The two new agents demonstrate a similar treatment effect when differences in background therapy are taken into account. DRV/r appears to act as a treatment effect modifier and it is important to adjust for its use when comparing treatment effects across studies. The analysis also illustrates the use of individual patient level data to adjust an indirect comparison for potential confounding effects.

See more of: Poster Session I

See more of: 30th Annual Meeting of the Society for Medical Decision Making (October 19-22, 2008)