ON THE OPPORTUNITY COST OF NON-RIGOROUS OR IRRELEVANT RESEARCH: IMPLICATIONS FOR ECONOMIC EVALUATION

Purpose: Parameter estimates used in cost-effectiveness analysis (CEA) are informed by empirical studies. However, existing studies might not be rigorous or directly relevant to the CEA's setting. We quantify the opportunity cost of biased research using a CEA that informed UK antenatal care policies.

Methods: We used a CEA of antenatal prophylaxis strategies in rhesus-negative pregnant women: anti-D prophylaxis for all; for primigravidae only; or for none. Existing anti-D prophylaxis studies are neither historically controlled, nor rigorously designed and conducted, and not directly relevant to the modern era. We incorporated corrections for bias and lack of relevance in parameters used in the CEA. We obtained estimates of crude and bias-corrected anti-D prophylaxis effectiveness from a previously published quantitative bias analysis, which used elicited opinion and constructed prior distributions to represent the biases of each study. We conducted separate CEAs, with and without such bias corrections. We estimated the opportunity cost of lack of rigor and relevance of research with a value of information analysis (VOI).

Results: Bias corrections changed the point estimates of anti-D prophylaxis effects little but inflated their variances substantially. Without bias-corrections, treating all rhesus negative pregnant women becomes the optimal strategy at a willingness-to-pay (WTP) of £13,000 per quality-adjusted life year (QALY). On the bias-corrected CEA, the optimality threshold increases to £16,000/QALY. In the VOI, effectiveness of anti-D prophylaxis was the most valuable parameter over the examined range of WTP thresholds, reaching a population expected value of partial perfect information (EVPPI) of £700,000 at a WTP of £16,000/QALY. The bias-correction on the treatment's effectiveness was the second most valuable parameter in terms of EVPPI, peaking at £620,000 at the same WTP. Other parameters of the CEA combined, without considering the risk of sensitization, account for just over half of the EVPPI of the bias parameter (Figure). Results were analogous in sensitivity analyses that used alternative opinion-elicited bias corrections.

Conclusions: Uncertainty associated with parameters used in CEAs should reflect not only sampling variance of empirical studies, but also the uncertainty stemming from less than perfect design, execution or analysis of such studies. The latter is rarely accounted for in economic evaluations, but can tramp the former. Research performed sub optimally can be associated with a large opportunity cost that is routinely left unclear.