Integrating PP in HTA

Table 1 summarizes PP use cases—the alternate ways that have been proposed to integrate PP into HTA. References are provided for illustrative purposes.

Table 1. Use cases describing the integration of patient preference (PP) into health technology assessment (HTA)

Typologizing PP Use Cases

Table 2 summarizes the application of a two-dimensional framework to describe the ways that the PP use cases support HTA. The first dimension in the framework is the type of evidence that PP can provide in HTA. The framework distinguishes five types of HTA evidence: endpoint selection, clinical benefit rating, predicting uptake, input into economic evaluation, and a means to weight all the criteria relevant to HTA. Within “input into economic evaluation,” the framework distinguishes several roles for PP: the estimation of the within-therapy area efficiency of medical products and the estimation of the across-therapy area efficiency of medical products, through supporting either cost–benefit analysis or cost–utility analysis.

Table 2. Typology of roles and insights generated by PP for HTA

Abbreviations: PP, patient preference; HTA, health technology assessment; MCDA, multicriteria decision analysis; QALY, quality-adjusted life-year; WTP, willingness to pay.

The second dimension of the framework is the insight provided by the PP use cases. Five types of insight are distinguished: understanding what matters to patients and how much this matters; predicting patient choices; estimating the utility generated by treatments benefits; estimating the willingness to pay for, or the opportunity cost associated with, treatment benefits; and informing distributional considerations, such as how the value of a QALY gained varies between patient groups.

Understanding What Matters to Patients and How Much

Trade-off assessment, including endpoint selection and clinical benefit rating, can help decision makers understand what matters to patients and how much (Reference Bouvy, Cowie, Lovett, Morrison, Livingstone and Crabb7;Reference Patalano, Gutzwiller, Shah, Kumari and Cook10). This evidence is supportive, ensuring that clinical studies collect data on endpoints that matter to patients, or that data on the value proposition for patients are considered alongside other evidence.

This is the more feasible of the PP use cases and easily acceptable to HTA agencies, as it does not attempt to analytically incorporate PP into an economic evaluation of an overall assessment of the value of a medical product (see below for more discussion). This is supported by a recent publication from staff at the NICE on how PP might support their decision making (Reference Bouvy, Cowie, Lovett, Morrison, Livingstone and Crabb7), by interviews with HTA agency staff from Germany, Canada, and Belgium (Reference van Overbeeke, Forrester, Simoens and Huys6), and by case studies of the use of PP by the Ontario HTA agency (11).

However, this acceptability is accompanied by concerns that the impact of this PP use case on reimbursement decisions will be limited without a more formal integration with evidence already considered by the agencies, such as economic analysis (Reference van Overbeeke, Forrester, Simoens and Huys6). This concern may, however, be mitigated by generating insights earlier in the product life cycle in order to inform the endpoints that will be included in the pivotal clinical trial (Reference Cook, Cave and Holtorf18). This way, the PP study can help the sponsor and HTA agency align on what constitutes patient value, ensuring that this is measured and considered during HTA.

There is also a lack of guidance on when and how quantitative data based on what matters to patients could support HTA. Bouvy et al. (Reference Bouvy, Cowie, Lovett, Morrison, Livingstone and Crabb7) illustrate how this PP use case could support NICE decision makers with an example of cancer treatments, where chemotherapy, radiation therapy, and immunotherapy differ in modes and ease of administration, effectiveness, and the risk of serious side effects. In such cases, a PP study could provide a committee with important insights into how patients with cancer would make trade-offs between treatment options and the probability that patients would prefer one treatment over another (Reference Bouvy, Cowie, Lovett, Morrison, Livingstone and Crabb7). However, no generalized guidance on this PP use case is available.

Predicting Patient Choice

The likelihood that patients will take and adhere to treatment is a critical component of HTA, not least as an input into cost-effectiveness and budget impact models (Reference Terris-Prestholt, Quaife and Vickerman13). This is especially the case for technologies that require upfront costs, but from which only those patients who follow and adhere to the treatment would likely benefit, for example, screening, inpatient/physician-led procedures followed by prescription and monitoring, or chronic prophylactic medications such as statins.

At the point at which HTA is undertaken, uptake and adherence are usually uncertain. PP studies can be used to estimate the probability that patients will choose one treatment over others and their willingness to tolerate the inconvenience of taking medication or safety events. There is precedence for such studies being used in submissions to the Pharmaceutical Benefits Advisory Committee (PBAC) to successfully support the argument that reduced injection frequency will result in improved adherence (Reference Fifer, Rose, Hamrosi and Swain19). It has also been argued that these insights can be used to parameterize economic analyses (Reference Terris-Prestholt, Quaife and Vickerman13). However, we are not aware of any precedence of such an approach being used to support a reimbursement decision.

There are concerns about the external or theoretical validity of the choices predicted by PP studies. Thus, where possible, observations of patients’ actual choices should still be preferred to their stated preferences. In the absence of such data, where PP data are used to help understand uptake or adherence, it is important to follow best practice to minimize hypothetical bias and ensure valid choice prediction (Reference de Bekker-Grob, Swait, Kassahun, Bliemer, Jonker and Veldwijk20).

Estimating Utility Generated by Technologies

Three of the PP use cases are concerned with valuing treatment outcomes and integrating these into economic evaluation. Three broad approaches are adopted: estimating utility in the form of patient willingness to pay (WTP) (4), estimating overall benefit within a therapy area-specific value framework (Reference Muhlbacher and Sadler16), and supporting the estimation of QALY gains within a cost–utility analysis (Reference Burr, Kilonzo, Vale and Ryan14). The first of these is considered in the next subsection. This section deals with the latter two approaches.

By defining the evaluation problem as the assessment of within-therapy area efficiency of technologies for a given patient group, the use of PP to estimate overall benefit for efficiency frontiers avoids many of the challenges associated with comparing the preferences of different patient groups. Having conducted two pilot studies, the Institute for Quality and Efficiency in Healthcare (IQWiG) concluded that such methods for eliciting PP were “suitable and manageable” and delivered “useful results” (21). Although they identified some methodological questions raised by this use of PP, such as ensuring the use of representative samples of patients, they have adopted the efficiency frontier approach as the basis for their economic evaluation (Reference Marsh, van Til, Molsen-David, Juhnke, Hawken and Oehrlein1). However, it is unclear how many IQWiG assessments have been informed by such efficiency frontiers and to what extent PP evidence fed into these assessments. Furthermore, the IQWiG is unique in its adoption of the within-therapy area efficiency frontier assessments.

Another PP use case involves PP in the estimation of QALYs. Two methods have been proposed to support this use case—using PP to re-estimate utility tariffs and to estimate QALY equivalents. Both involve estimating utility gains based on the amount of survival that patients would be willing to give up for a treatment benefit. Although both approaches could be used to estimate utility gains for a range of treatment benefits—improvements in health states, avoiding adverse events, or improvements in convenience—they differ in their emphasis. Estimating utility tariffs would conventionally focus on valuing health states, whereas estimating QALY equivalents may be more appropriate for estimating the utility associated with treatment benefits missed by conventional methods—acute events, such as pain or adverse events, or improvements in the mode of administration.

Both methods raise several methodological and normative questions. Consequently, the use of these methods in practice is relatively limited. We are not aware of any instances of HTA agencies using PP to estimate QALY gains in a cost–utility analysis (CUA). There is precedent for the supplementation of QALY estimation by using preference studies to estimate the QALY-equivalence of treatment benefits not captured by conventional QALY estimation methods, specifically process utilities (22). However, this was based on general population, rather than PP.

Most HTA agencies use public preferences to estimate utility gains (Reference Marsh, van Til, Molsen-David, Juhnke, Hawken and Oehrlein1) and are reluctant to estimate utility gains based on patients’ preferences (Reference van Overbeeke, Forrester, Simoens and Huys6). The predominance of public preferences rests on two normative principles—the public is the recipient and often the funder of health care and patient's adaptation to their disease, for instance lowering their expectations of “full health,” would disadvantage them if their preferences were the basis for utility estimation (Reference Versteegh and Brouwer23). However, various authors have raised challenges to this position, including that the public is unable to conceptualize what it is like to be in a certain health state and that the patient perspective, thus, provides important insight for decision makers, especially in the context of “laudable” adaptation where patients might be expected to put greater weight on their health gains than the public (Reference Menzel, Dolan, Richardson and Olsen24). Reflecting the idea that both the public and patients hold information that is relevant to estimating utility gains, various authors have put forward methods for integrating both sets of preferences into economic evaluation, including conducting CUA using both patient and public preferences, generating two efficiency estimates (Reference Versteegh and Brouwer23), or valuing health states using PP, and then addressing distributional concerns regarding health states using public preferences (Reference Ubel, Richardson and Menzel25).

Estimating Willingness to Pay for Treatments

Using patients’ WTP is accepted in specific circumstances by two HTA agencies. The PBAC recommend its use where nonhealth benefits are generated for the patient, such as a more convenient mode of administration (4). It is also recommended that WTP estimates be used in a cost–benefit analysis (CBA) where the health benefits of treatments are comparable. The Dental and Pharmaceutical Benefits Agency in Sweden recommend the use of patient WTP to capture either health gains that are not captured by the QALY, such as acute pain, or nonhealth gain (Reference Marsh, van Til, Molsen-David, Juhnke, Hawken and Oehrlein1). However, neither agency has provided data on the number of submissions supported by these data or how the PP data have impacted decisions.

There are several important objections to using PP to estimate WTP. First, it is argued that asking patients will result in unrealistic inflated WTP as they are self-interested in the outcome of a reimbursement decision while not having to cover the full cost of treatment themselves. Second, the efficiency of existing healthcare interventions should be the basis for opportunity cost rather than stakeholder's stated preferences (Reference Marsh, Sculpher, Caro and Tervonen26). Third, WTP may be a function of income, potentially undermining agencies’ objectives to ensure equality of access. Finally, questions about the external validity of preference studies mean that estimates of patients’ WTP may not translate into WTP in practice (Reference de Bekker-Grob, Swait, Kassahun, Bliemer, Jonker and Veldwijk20). Although these objections apply to the use of PP to estimate WTP in a publicly funded healthcare system, they would also point to the potential use of PP in privately funded health care.

Distributional Considerations

One of the use cases—MCDA weights—involves patients weighing treatment outcomes, cost, and other factors, such as disease severity, to formally score these attributes in making a decision about the adoption of a technology (Reference Muhlbacher and Johnson17). In this way, MCDA is an attempt to bring together many decision criteria, which may already include preference-based measures such as QALYs gained, into one composite criterion. If the MCDA includes a cost criterion, the approach also uses PP to determine WTP for treatment outcomes and how this should vary with, for instance, disease severity. Although there is precedent for the use of MCDA to support HTA, we are not aware of any instances where patients were exclusively the source of MCDA weights. Rather, patients tend to be one of a number of stakeholder groups who contribute weights (Reference Marsh, Lanitis, Neasham, Orfanos and Caro27).

The use of MCDA for HTA faces many challenges unrelated to the use of PP to generate weight inputs, which would nevertheless be a cause for concern, including the correct specification of value models and the selection of appropriate weighting methods (Reference Marsh, Sculpher, Caro and Tervonen26). Furthermore, interviews with agency staff in Belgium, Canada, and Germany identified concerns about the practicality of such an approach, questioning the feasibility of eliciting PP for large numbers of criteria or in a manner timely with the HTA process (Reference van Overbeeke, Forrester, Simoens and Huys6). More fundamentally, using PP instead of public preferences as the basis for resolving distributional considerations is challenged by patients’ vested interest in such decisions.