• Conference Proceeding

Patient preferences and HIV drugs: What about uncertainty?

Citation

Broekhuizen, H., Ijzerman, M. J., Hauber, A. B., & Groothuis-Oudshoorn, C. G. (2014). Patient preferences and HIV drugs: What about uncertainty?. In [17], p. A565. .

Abstract

OBJECTIVES: Quantitative patient preferences are increasingly considered for healthcare policy decisions. The objective of this study is to develop a methodology to combine patient preferences with clinical evidence in a multi-criteria framework that takes into account uncertainty in both preferences and clinical evidence. The methodology will be illustrated with a case on antiretroviral treatments.

METHODS: Treatments under consideration are eight highly active antiretroviral therapies (HAART) recommended for treatment-naïve patients by the National Institute of Health. The treatments are compared on the probabilities of virologic failure, hypersensitivity reaction, bone damage, and kidney damage; and on the treatability of bone/kidney damage. Preferences from 147 patients were elicited with a discrete choice method in an earlier study. Preferences were assumed to be distributed with a multivariate normal distribution. Treatment performances as identified from clinical trials were assumed to be distributed with beta distributions. The probability distributions around preferences and clinical performances were combined with a Monte Carlo simulation method to estimate the joint probability distribution around each treatment-s patient-weighted utility.

RESULTS: The three treatments with the highest mean patient-weighted utility were dolutegravir+abacavir/lamivudine (-0.4, 95% CI: -1.3 to 0.5), raltegravir+tenofovir/emtricitabine (-0.5, 95% CI: -1.6 to 0.7) and darunavir/ritonavir+tenofovir/emtricitabine (-0.6, 95% CI: -2.0 to 0.8). There was considerable overlap between the probability distributions of patient-weighed utilities (probability of first rank reversal: 49%; probability of any rank reversal: >99%). When ignoring uncertainty around patient preferences, the probability of a first rank reversal dropped to 12%, and that of any rank reversal dropped to 88%.

CONCLUSIONS: A probabilistic multi-criteria methodology was developed that explicitly combines patient preferences and clinical evidence. The individual or joint impact of uncertainty in these on the treatments' patient-weighted utilities is assessed. Although limited by the small number of attributes, the illustrative case suggests the choice of HAART is highly sensitive to patient preferences.