This paper outlines an approach to develop a standard, quantitative estimate of unmet need that considers both disease burden and differential access and outcomes elements of the definition of unmet need; it also looks to the patient to determine what is meaningful or dissatisfactory about unmet need. Specifically, it is proposed that a thresholding method could be used to estimate the LOLE. Table 4 describes the LOLE approach using the six questions identified by Dolan [28] as being required to define a method for measuring health outcomes.

The benefits of and questions raised by LOLE are summarized in Table 5. While LOLE can provide unmet need input into many health decisions, just as with measures of AS currently adopted by HTA agencies, LOLE expresses unmet need in units of disutility generated by a disease. LOLE would mimic the strengths of the QALY-based AS metrics adopted by several HTA agencies, consisting of a common, comparable, disease agnostic, quantitative measure of unmet need; however, LOLE additionally helps to address some of the limitations of QALY-based metrics. First, QALYs only capture a narrow set of impacts of a disease on health-related quality of life (HRQL). LOLEs could be used to capture a much broader set of disease impacts, as relevant to an agency’s normative objectives. LOLE would only be limited by whether the concept was relevant to patients, allowing for the inclusion of a broader conception of HRQL (e.g., capturing the tolerability of treatments) and even a perspective wider than HRQL (capturing treatment burdens such as mode of administration).

Second, LOLE is more sensitive to the impact of a disease on patients. While regulatory agencies acknowledge the role of PED in understanding unmet need, HTA agencies use standard HRQL metrics and general population preferences. The attributes of a PP study could include the HRQL domains that matter to patients. This could be either a condition-specific HRQL instrument or standard measures of the concepts that matter to patients, such as those from PROMIS [29]. The use of the latter would further enhance the cross-patient comparability of LOLE. Further, the use of PP, rather than the general population preference set used in the utility tariffs on which QALYs are calculated, ensures that LOLE reflects the patients’ experience of the disease. In addition, the characteristics of the patient’s experience in the threshold exercise could be provided to the patient and capture the mean or expected characteristics of living with the disease. It could also be elicited from the patient such that the patient is expressly comparing their own experience with the likely experience for an age- and sex-matched healthy person.

The proposal to use LOLE rather than an QALY-based estimate of AS evokes the 1990s debate over the relative merits of the QALY and Healthy Years Equivalent (HYE). While the QALY approach involves the valuation and aggregation of discrete health states, like LOLE, HYE involves the valuation of an entire health profile [30]. Arguments for HYE included that it better represented preferences for health states, including capturing time preferences and avoiding restrictive assumptions, such as additive separability—permitting the rate of trade-off between life years and quality of life to depend on the life span—and incorporating attitudes toward risk [30,31,32,33]. Rejection of the claim that HYE incorporates attitudes toward risk and the intractability of separately valuing all health state sequences [34,35,36] caused health economists to lean toward the greater generalizability of the QALY approach. Recent efforts to breathe new life into HYE have focused on justifying the greater investment required to implement HYE by substantiating its ability to generate better preference data [37]. However, this tradeoff between quality of preference data and the effort of separately valuing health states is less relevant to the consideration of LOLE for measuring unmet need. As the focus is on patients’ valuations of their unmet need, LOLE would necessitate a bespoke analysis of each health state with the relevant patient group, and the desire for greater generalizability becomes redundant.

The acceptability of LOLE as a measure of unmet need raises normative questions that HTA and regulatory agencies will need to consider. First, who should determine whether unmet need is meaningful? LOLE turns to the patient for this insight, which is consistent with efforts to implement PFDD; however, HTA agencies have conventionally looked to the public to support value judgements. This rests on the principle that the public are both potential recipients and funders of healthcare [38] and that patients adapt to their condition [39] which would result in an underestimation of unmet need. Arguments for instead leveraging a patient’s perspective point to the public being unable to conceptualize some health states [38,39,40,41] and, contrary to concerns that patients downplay their needs, there is evidence that in some situations, patients put greater weight on their needs than the public [42].

Second, is length of life an appropriate numeraire? Length of life was selected as the common metric as this aligns with the approach adopted by many HTA agencies in their application of TTO. However, this will result in patients’ valuation of losses in quality of life being a function of their life expectancy. The shorter a patient’s life expectancy, the more value they will put on a unit increase in their life expectancy [43]. Thus, any given reduction in symptom burden would generate fewer LOLE for a patient with a shorter life expectancy than a patient with a longer life expectancy—as they attach more importance to improving life expectancy, they are less willing to give up life expectancy for reducing symptom burden. It would be important for agencies to consider the normative acceptability of this feature of the LOLE approach.

Third, LOLE can capture a comprehensive set of patient-relevant disease impacts, but it cannot capture some of the more societal-level disease impacts included in some frameworks, such as disease prevalence or environmental impacts [1].

The LOLE approach also raises practical questions, the answer to which will depend on each agency’s goals for estimating unmet need. First, which unmet need profiles are amenable to estimating LOLE with thresholding? The illustrations of unmet need used in this paper will be simpler than those experienced by most patients, assuming that patients’ quality of life decrement is constant over time. Further work could usefully review the experience of vignette-based preference elicitation, including that associated with HYE, to understand patients’ cognitive limitations when conducting such exercises. Further work should also assess patients’ own understanding of their unmet need profile and test the extent to which patients require supporting their disease profile with detailed descriptions.

Second, to ensure LOLE is genuinely patient centric, we recommend that patients inform the dimensions of unmet need that are included in the disease profile. Leveraging existing good practice guidance [10, 14], further guidance could usefully clarify good practice in eliciting patients’ input into disease profile generation.

Third, is thresholding the appropriate method to use to estimate LOLE? HTA agencies conventionally turn to TTO or SG methods to elicit similar trade-offs. Several features of LOLE could be replicated with the TTO, including the estimation of X in Fig. 1, the use of patients’ preferences, the incorporation of non-health benefits, and the personalization of the health states been assessed. However, there are differences, including that LOLE uses a choice method while TTO is a matching method, and that the tariff generated by TTO requires constraining between 0 and 1. Further work on the relative merits of these methods should be undertaken.

Fourth, would patients be willing to accept tradeoffs of life expectancy for improvements in quality of life? What should be done with patients who are unwilling to make this tradeoff? Would it be appropriate to assess their unmet need as being zero, or would this phenomenon necessitate the selection of an alternative numeraire?

Fifth, operationalizing LOLE will require agencies to select the LOLE thresholds that define different levels of unmet need. Further, where unmet need estimates are used to determine cost-effectiveness thresholds, agencies will need to define the relationship between LOLE and willingness to pay for health gain.

Finally, operationalizing LOLE for each decision will require resources, most likely those of sponsors. While LOLE would replace a variety of other efforts to estimate unmet need, further work could usefully consider whether it is possible to implement LOLE more efficiently by standardizing the use of LOLE across decision makers or developing more generalizable estimates of LOLE.

Among other uses, LOLE is an alternative way to estimate AS used by HTA agencies. However, agency use of AS to weight QALYs gained has been criticized for reducing the weighted population health [44] by inflating the cost-effectiveness thresholds further over supply-side thresholds. Solving this problem would require agencies to either adopt a net-benefit approach in which weights are applied to both health gains and expected health forgone, or that the policy threshold be adjusted such that the difference between it and the supply-side threshold is constrained by the ratio of weights for health gained and health forgone. If agencies would be willing to update their decision-making framework accordingly, the implication for LOLE would be that it would need to be estimated for both the health gain and the health forgone, with the practical obstacle of needing to survey the patients in receipt of the technologies that would be disinvested. However, supporting the use of unmet need as a cost-effectiveness threshold weight is only one of the applications for a standardized patient-centric estimate of unmet need. As there are many other uses of unmet need across healthcare decisions, such as supporting the incorporation of unmet need into regulatory decisions, so there are many applications of LOLE.