Purpose: To analyse data from the OPTIMA trial to estimate input parameters in a decision model assessing cost-effectiveness in patients with advanced HIV for whom standard therapies had failed.� The input parameters required to estimate such models often differ from the summary statistics on treatment efficacy available from trials. �However, the resulting information is valuable to a range of potential analyses.
Methods: We conducted survival analysis to model lifetime disease progression.� Events of interest were time to death and time to first AIDS defining event (ADE).� By considering events both individually and jointly, we were able to calculate appropriate conditional probabilities.� Assumptions were required to (i) link continuous time events to laboratory measures taken at discrete intervals; (ii) address missing data; and (iii) determine appropriate parametric distributions for survival time.� We determined CD4 and viral load levels at time of ADE or death by last value carried forward. �Examination of Kaplan-Meier curves, information criterion and the requirements of the model informed the selection of distribution.�
Results: Table 1 reports hazard ratios for time to first ADE and time to death.� Baseline covariates describe prognosis for different sub-groups.� Time-varying covariates illustrate how risk of events change as a patient's disease progresses. �Risk of ADE or death decreases as CD4 increases, and increases with viral load.�
Table 1 | Baseline covariates | Time-varying covariates | ||
Covariate | ADE | death | ADE | death |
Absolute CD4 (per 100 cell change) | 0.53** | 0.49** | 0.43** | 0.51** |
Log viral load | 1.16* | 1.17* | 1.21** | 1.08 |
AIDS at baseline | 1.32 | 1.47* | - | - |
1 SAE | - | - | 1.03 | 1.22 |
2+ SAEs | - | - | 1.03 | 3.6* |
Conclusions: Utilising exponential distributions where appropriate produced output more suited for use in Markov models.� The significance of cumulative number of SAEs signified the need for additional health states to more accurately model survival.� Inclusion of baseline covariates allowed disease progression to be modelled for different sub-groups.� These survival analyses were complemented by further analyses of the OPTIMA data regarding costs, quality of life, and duration of treatment response. The resulting decision model can evaluate new anti-retroviral therapies in an advanced HIV population less often recruited to clinical trials. � �