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There are different stated preference (SP) approaches, including discrete choice experiments (DCEs). DCEs are a popular SP approach, but in some settings, alternative ways of framing survey questions may be more appropriate. The Health Economic Modelling Of Alternative Blood Donation Strategies (HEMO) study required choice tasks to be framed so that the study could estimate the effect of attribute levels on the frequency of a behavior—in this case, blood donation. SP questions were formulated to require ordered categorical responses from a single profile of attribute levels. However, it is unknown whether this way of framing SP questions leads to estimates of marginal rates of substitution (MRS) that are different from traditional DCE choices between 2 alternative profiles. The aim of this article is to compare estimates of relative preferences from SP questions requiring ordered categorical versus discrete choice responses. We compared relative preferences elicited from the 2 approaches for a common set of attributes and levels, formulated as choice tasks for 8,933 whole-blood donors. We found that the 2 forms of survey questions provided similar MRSs estimates. For example, respondents were willing to trade off only a small increase in travel time to receive a health report, irrespective of whether the choice given was binary (DCE response; approximately 3 min) or from an ordered category (about 8 min). The finding that any differences in the estimated MRSs are not of substantive importance offers some reassurance for policy makers in that estimates of relative preference may be robust to alternative ways of framing the survey questions. These findings can encourage future studies to frame choice tasks that align with the study’s objective. Highlights This article compares the relative preferences from stated preference (SP) questions requiring ordered categorical versus discrete choice responses. The approaches were contrasted for blood donation service characteristics that offer opportunities to donate blood. The estimates of relative preferences for alternative blood donation service characteristics were similar between the 2 forms of SP approach. This study illustrates how SP survey questions can be formulated to provide responses on an ordered categorical scale and to estimate marginal rates of substitution between different attributes, which can be compared with those derived from discrete choice experiment (DCE) choices. The article highlights the potential value of considering alternative choice framings rather than relying solely on DCEs.

In many countries, reimbursement decisions are informed by estimates of the relative cost-effectiveness of alternative technologies, with costs and health outcomes projected over the lifetime of patients. Most cost-effectiveness analyses (CEAs) use decision models populated with evidence from randomized controlled trials (RCTs) that do not follow all patients until death. In the presence of such censored data, analyses of clinical effectiveness tend to use Cox proportional hazards regression models and avoid specifying a baseline hazard. This approach is insufficient for CEA, which must predict expected survival over time, a process that often requires extrapolation beyond the RCT follow-up data. There are many options for conducting such extrapolations and great uncertainty as to the best choice of approach; consequently, gaming by manufacturers is a potential concern. A standard approach is to apply parametric survival functions to the individual patient data (IPD) from RCTs to predict mean survival by treatment arm, over the lifetime. Davies and others illustrate just how important and uncertain model selection for extrapolation can be, even in the simple setting where IPD are available to estimate relative effectiveness for the 2 comparators of interest. Davies and others updated a CEA of 2 alternative prostheses for total hip replacement using 16 years of follow-up data, rather than the 8 years that were available at the time the previous study was conducted, and found that the original conclusion was overturned. How critical should we be of the previous CEAs, which may have provided the best recommendation given the evidence available at that time? Who is to say that the current results, which still do not follow all patients for their lifetime, represent the truth? Rational decision making requires extrapolation approaches that are grounded in objective criteria and make efficient use of all evidence available at the time of decision. Recent reviews of technology appraisals for the National Institute for Health and Care Excellence (NICE) have shown that studies used extrapolation approaches that were inadequate or inadequately described. Latimer’s paper, which summarizes a longer report, proposes an algorithm to help future analysts improve their approach to extrapolation. The algorithm is limited to CEAs that compare 2 alternatives in which IPD are available from the pivotal RCT and there is no treatment switching. Within this context, there are 3 main suggestions: first, select the range of candidate models from a description of the occurrence of events over time, specifically by plotting the log of the cumulative hazards against the log of time; second, fit the chosen range of models to the entire RCT dataset and assess their relative fit to the observed data; and third, consider the relative plausibility of the predictions in the unobserved period. The development of the algorithm is to be welcomed, given the current state of both applied and methodological work in this area. We believe that the algorithm can improve the accuracy of extrapolations that are undertaken, not least by encouraging analysts to report a broader range of approaches than is current practice (in effect, report a structural sensitivity analysis). That said, we think further consideration From Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (RG, MP); and Oxford Outcomes, Oxford, UK (NH).

Decision makers require cost-effectiveness estimates for patient subgroups. In nonrandomized studies, propensity score (PS) matching and inverse probability of treatment weighting (IPTW) can address overt selection bias, but only if they balance observed covariates between treatment groups. Genetic matching (GM) matches on the PS and individual covariates using an automated search algorithm to directly balance baseline covariates. This article compares these methods for estimating subgroup effects in cost-effectiveness analyses (CEA). The motivating case study is a CEA of a pharmaceutical intervention, drotrecogin alfa (DrotAA), for patient subgroups with severe sepsis ( n = 2726). Here, GM reported better covariate balance than PS matching and IPTW. For the subgroup at a high level of baseline risk, the probability that DrotAA was cost-effective ranged from 30% (IPTW) to 90% (PS matching and GM), at a threshold of £20 000 per quality-adjusted life-year. We then compared the methods in a simulation study, in which initially the PS was correctly specified and then misspecified, for example, by ignoring the subgroup-specific treatment assignment. Relative performance was assessed as bias and root mean squared error (RMSE) in the estimated incremental net benefits. When the PS was correctly specified and inverse probability weights were stable, each method performed well; IPTW reported the lowest RMSE. When the subgroup-specific treatment assignment was ignored, PS matching and IPTW reported covariate imbalance and bias; GM reported better balance, less bias, and more precise estimates. We conclude that if the PS is correctly specified and the weights for IPTW are stable, each method can provide unbiased cost-effectiveness estimates. However, unlike IPTW and PS matching, GM is relatively robust to PS misspecification.

Cost-effectiveness analyses (CEA) may be undertaken alongside cluster randomized trials (CRTs) where randomization is at the level of the cluster (for example, the hospital or primary care provider) rather than the individual. Costs (and outcomes) within clusters may be correlated so that the assumption made by standard bivariate regression models, that observations are independent, is incorrect. This study develops a flexible modeling framework to acknowledge the clustering in CEA that use CRTs. The authors extend previous Bayesian bivariate models for CEA of multicenter trials to recognize the specific form of clustering in CRTs. They develop new Bayesian hierarchical models (BHMs) that allow mean costs and outcomes, and also variances, to differ across clusters. They illustrate how each model can be applied using data from a large (1732 cases, 70 primary care providers) CRT evaluating alternative interventions for reducing postnatal depression. The analyses compare cost-effectiveness estimates from BHMs with standard bivariate regression models that ignore the data hierarchy. The BHMs show high levels of cost heterogeneity across clusters (intracluster correlation coefficient, 0.17). Compared with standard regression models, the BHMs yield substantially increased uncertainty surrounding the cost-effectiveness estimates, and altered point estimates. The authors conclude that ignoring clustering can lead to incorrect inferences. The BHMs that they present offer a flexible modeling framework that can be applied more generally to CEA that use CRTs.