title: Prioritizing HIV comparative effectiveness trials based on value of information: generic versus brand-name ART in the US.
abstract: BACKGROUND
  Value of Information (VOI) analysis examines whether to acquire information before making a decision. We introduced VOI to the HIV audience, using the example of generic antiretroviral therapy (ART) in the US.
  
  
  RESULTS
  We used a mathematical model and probabilistic sensitivity analysis (PSA) to generate probability distributions of survival (in quality-adjusted life years, QALYs) and cost for three potential first-line ART regimens: three-pill generic, two-pill generic, and single-pill branded. These served as input for a comparison of two hypothetical two-arm trials: three-pill generic versus single-pill branded; and two-pill generic versus single-pill branded. We modeled pre-trial uncertainty by defining probability distributions around key inputs, including 24-week HIV-RNA suppression and subsequent ART failure. We assumed that, without a trial, patients received the single-pill branded strategy. Post-trial, we assumed that patients received the most cost-effective strategy. For both trials, we quantified the probability of changing to a generic-based regimen upon trial completion and the expected VOI in terms of improved health outcomes and costs. Assuming a willingness to pay (WTP) threshold of $100 000/QALY, the three-pill trial led to more treatment changes (84%) than the two-pill trial (78%). Estimated VOI was $48 000 (three-pill trial) and $35 700 (two-pill trial) per future patient initiating ART.
  
  
  CONCLUSIONS
  A three-pill trial of generic ART is more likely to lead to post-trial treatment changes and to provide more value than a two-pill trial if policy decisions are based on cost-effectiveness. Value of Information analysis can identify trials likely to confer the greatest impact and value for HIV care.
authors.affiliation: Medical Practice Evaluation Center, Massachusetts General Hospital , Boston, USA.
authors.forename: Pamela P
authors.initials: PP
authors.lastname: Pei
authors.forename: Milton C
authors.initials: MC
authors.lastname: Weinstein
authors.forename: X Cynthia
authors.initials: XC
authors.lastname: Li
authors.forename: Michael D
authors.initials: MD
authors.lastname: Hughes
authors.forename: A David
authors.initials: AD
authors.lastname: Paltiel
authors.forename: Taige
authors.initials: T
authors.lastname: Hou
authors.forename: Robert A
authors.initials: RA
authors.lastname: Parker
authors.forename: Melanie R
authors.initials: MR
authors.lastname: Gaynes
authors.forename: Paul E
authors.initials: PE
authors.lastname: Sax
authors.forename: Kenneth A
authors.initials: KA
authors.lastname: Freedberg
authors.forename: Bruce R
authors.initials: BR
authors.lastname: Schackman
authors.forename: Rochelle P
authors.initials: RP
authors.lastname: Walensky
mesh_terms: D019380:Anti-HIV Agents / Q000191:economics* / Q000627:therapeutic use*; D002986:Clinical Trials as Topic; D003362:Cost-Benefit Analysis; D016527:Drug Costs; D016568:Drugs, Generic / Q000191:economics*; D015658:HIV Infections / Q000188:drug therapy* / Q000453:epidemiology*; D006801:Humans; D018803:Models, Economic; D014481:United States / Q000453:epidemiology
chemical_list: D019380:Anti-HIV Agents; D016568:Drugs, Generic
keywords: Cost-effectiveness analysis; Decision modeling; Generic drugs; HIV; Value of information