HIV/AIDS, Utilities, and Cost-Effectiveness Analysis: Stepping toward the Future

T article in this issue by Tengs and Lin reports findings that may hasten movement toward the more active use of patient preferences in HIV/AIDS care decision making, an approach that has been underemployed. The authors used metaregression to derive adjusted, pooled estimates of utilities for different stages ofHIV infection, usingdata from1956 studyparticipants published in 25 articles between 1988 and 1999. Their findings herald a future of better utility estimates available for individuals with HIV/AIDS, estimates that are derived by pooling data from individual studies, the results of whichmay not have been definitive alone, and after which are adjusted for a number of important, potentially confounding variables. Such estimates, alongside a substantive listing of recently published single-study preference weights for HIV/AIDS and its related conditions, replace reliance on summary quality-of-life scores that have served as proxy utilities even though they have been known to differ systematically. This methodological leap forward is welcome. It remains tempered, however, by a number of concerns that lead the authors to state that their results should be viewed as “suggestive rather than definitive. ” I agree with themand enumerate additional reasonswhy their cautionary tone is prudent. Identifying these problematic areas does not detract from the authors’ contribution; rather, it highlights some areas where future work might be more attentive and thus more definitive. The authors report that 3 researchers read each article and resolved coding differences through consensus but supplyno information about them.The expertise of the researchers that would aid or hinder their ability to code information in an unbiased, accurate manner remains unknown. So, too, the degree of agreement between researchers remains unknown. Reporting how many coding differences required resolution and how theywere resolved, for example,would havemade this inscrutable task of coding more transparent. Manymeta-analysespresent tables that list all publications included, each study’s methodological quality, thenumber of studyparticipants, and soon.This—particularly the assessment of study quality—provides a means by which the independent interpretation of findings by the reader is possible. If all studies that included a particular elicitation method were less good in quality, for example, and tended to use patients of a similar disease severity, the reader’s ability to interpret findings about thenoted elicitationmethodandpatient subgroup would have been better served. The assessment of studyquality also couldhave allowed its inclusion as a potentially confounding variable in the metaregression. The subgroup classified as having asymptomatic disease was the one for which there were the fewest utility assessments available (n= 12), and yet this is the stage for which there is the greatest number of individuals living with HIV/AIDS today. The authors provide no means by which to assess the stability or precision of this pooled estimate—in fact, measures of stability and precision are absent for all pooled estimates, not just for the asymptomatic subgroup. (The lack of study quality also is relevant here. If the majority of these studies were of low quality methodologically, the accuracy of the utility estimates reported for this subgroup would be suspect.) Data from subgroups with indeterminate stages of HIV disease severity were excluded from the metaregression.This is problematicwhenconsidering those