Health economic evaluation of gene replacement therapies: methodological issues and recommendations

Figures & data

Table

Main challenges	Recommendations	Sources
Assessment of clinical effectiveness based on small clinical trials, often single-arm	The results from clinical trials conducted on small samples and/or performed without appropriate comparators can in some instances be compared with those obtained from previous studies conducted on cohorts similar to the population of interest. Historical controls may include prior patients with the same disorder from an observational study (prospective natural history study, medical chart data from clinical care), or from a control group from a prior randomised investigational study.	Literature review^a
	Factors to be considered to ensure historical cohorts acceptability by HTA bodies: The rationale behind not doing a comparative trial is provided. Preliminary data suggest that the magnitude of treatment effect size versus historical cohort is dramatic. The primary endpoint is objective, durable and reproducible. The impact of study heterogeneity in the patient population and the impact on the outcome is studied. Confounding factors affecting the outcome are relatively well known, and a statistically sound adjustment method is used to control for confounding. The generalisability and transferability of the clinical data toward the historical cohort are proactively assessed.	Augustine 2013[28], Rémuzat 2019[33]
Estimation of costs	Costs should be evaluated from a health care perspective and a societal perspective	Sanders 2016[16]
	Caregiver time can be valued either based on the income generated by the caregiver if (s)he had been doing paid work instead of caring for a relative, or based on the cost of hiring a professional caregiver for providing the same service.	Oliva-Moreno 2017[23], Hjortsberg 2010[24]
	Including future unrelated medical costs, along with associated health benefits (which may be considered implicitly), would be appropriate.	Van Baal 2019[25]
Valuation of health outcomes	→ Valuing quality of life in very young children The Saved Young Life Equivalents (SAVE) approach, which has attracted less interest than QALYs in the health economic literature, may be worth reconsidering. First, the SAVE approach would avoid assumptions of the QALY model such as independence between health state value and duration, which is not sustainable. Second, SAVEs would be elicited using a Person Trade-off (PTO) approach, from a societal perspective, thus avoiding the difficulties to elicit utilities for very young children from self-perspective. A cost-benefit analysis would avoid the problems related to the elicitation of utilities for very young children, but the valuation of quality of life in monetary terms is challenging for clinicians and many healthcare decision-makers.	Nord 1992[21], panel^aa
	→ Valuing the HRQoL of families and caregivers The burden of caregivers, involved by both emotional distresses facing suffering from a disease of a close relative as well as by the burden of caring, will be substantial considering the severity of diseases treated by GRT. When there is evidence of an impact of the disease on the HRQoL of families and caregivers, this should be accounted for in the evaluation of GRTs, irrespective of whether the analysis is performed from a healthcare payer’s or societal perspective.	Review^a, NICE[61], HAS[62]
	→ Incorporating broader elements of value Broader elements of value could be taken into consideration in the cost/QALY evaluation framework through some modifiers, such as the application of a factor to inflate the QALYs or a higher cost-effectiveness threshold. Drummond et al[15,37]. Highlighted the fact that in practice, HTA agencies consider those elements of value through a deliberative process, and it would be important to identify all relevant elements when presenting evaluations of GRT.	NICE 2019[^61], Medic 2017[^71] Drummond 2013[^37], Drummond 2019[^15]
Time horizon and extrapolation, as there is substantial uncertainty around long-term effects, positive or negative	→ Selection of an appropriate time horizon While a lifetime horizon may seem desirable for GRT, it may be misleading for decision-makers if we have no way to know whether the net benefits of treatment will be positive or negative in distant years. One solution to palliate long-term uncertainty would be to assess scenario analyses with different time horizons pertaining to different knowledge about treatment benefit. However, when different scenarios produce a wide range of ICERs, some expert guidance via the use of Delphi panels could be useful for decision-makers to weigh the different results presented to them.	Panel**
	→ Eliciting expert opinion Experts acknowledged the importance of expert opinion due to limited data available in the context of GRT. Several elicitation procedures are available to obtain information from experts and make a probabilistic representation of their knowledge. Two different approaches of structured elicitation are recommended for cost-effectiveness analyses: 1) the fixed interval method, in which the expert reports his/her probability of the uncertain quantity of interest θ, for example the recurrence rate, the mortality rate or the time to death, lying in specified intervals, and 2) the variable interval method, in which (s)he makes quantile judgements.	Panel**, Grigore 2016[79], Soares 2018[80]
	→ Extrapolation methods Standard modelling techniques used in economic evaluation, such as Markov models and Discrete Event Simulation, will likely be appropriate for GRT. The challenge will be to find appropriate data, such as transition probabilities, to populate these models. Information from historical patients might be used to generate the input data. When there is uncertainty around the proportion of cured patients, then mixture cure models may be helpful to determine the probability of reaching key development endpoints.	Panel**, Hettle 2017[7] Literature reviewa Yu 2008[78]
Discount rate	There are strong reasons to argue that discount rates recommended by many HTA agencies are too high, in particular for health outcomes. This is an important consideration for the evaluation of GRTs, as higher discount rates may lead to substantially higher ICERs.	Klock 2005[95], Brouwer 2005[96]

^aRecommendations based on the review of published evaluations of GRT; **Recommendations based on expert panel meeting

Augustine EF, Adams HR, Mink JW. Clinical trials in rare disease: challenges and opportunities. J Child Neurol. 2013;28(9):1142–1150. [published Online First: 2013/09/10].

 PubMed Web of Science ®Google Scholar

Rémuzat C, Thokagevistk K, Millier A, et al. Factors to be considered to ensure acceptability of historically controlled studies by HTA bodies. ISPOR EU. Copenhagen; 2019.

 Google Scholar

Sanders GD, Neumann PJ, Basu A, et al. Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: second panel on cost-effectiveness in health and medicine. JAMA. 2016;316(10):1093–1103.

 PubMed Web of Science ®Google Scholar

Oliva-Moreno J, Trapero-Bertran M, Pena-Longobardo LM, et al. The valuation of informal care in cost-of-illness studies: a systematic review. PharmacoEconomics. 2017;35(3):331–345. [published Online First: 2016/11/17].

 PubMed Web of Science ®Google Scholar

Hjortsberg C, Persson U. The value of informal caregiver time for psychotic illness. J Ment Health Policy Econ. 2010;13(3):127–133. [published Online First: 2010/11/06].

 Web of Science ®Google Scholar

van Baal P, Morton A, Meltzer D, et al. Future unrelated medical costs need to be considered in cost effectiveness analysis. Eur J Health Econ. 2019;20(1):1–5. [published Online First: 2018/04/20].

 PubMed Web of Science ®Google Scholar

Nord E. An alternative to QALYs: the saved young life equivalent (SAVE). BMJ. 1992;305(6858):875–877.

 Google Scholar

NICE. Guide to the methods of technology appraisal. Process and methods [PMG9]; 2013.

 Google Scholar

HAS. Choices in methods for economic evaluation. A methodological guide; 2012. [cited 2019 Jun 6]. Available from: https://www.has-sante.fr/portail/upload/docs/application/pdf/2012-10/choices_in_methods_for_economic_evaluation.pdf

 Google Scholar

Drummond MF, Neumann PJ, Sullivan SD, et al. Analytic considerations in applying a general economic evaluation reference case to gene therapy. Value Health. 2019;22(6):661–668. [published Online First: 2019/06/15].

 PubMed Web of Science ®Google Scholar

Drummond M, Tarricone R, Torbica A. Assessing the added value of health technologies: reconciling different perspectives. Value Health. 2013;16(1Suppl):S7–13. [published Online First: 2013/01/18].

 Google Scholar

Medic G, Korchagina D, Young KE, et al. Do payers value rarity? An analysis of the relationship between disease rarity and orphan drug prices in Europe. J Mark Access Health Policy. 2017;5(1):1299665. [published Online First: 2017/05/06].

 PubMedGoogle Scholar

Grigore B, Peters J, Hyde C, et al. A comparison of two methods for expert elicitation in health technology assessments. BMC Med Res Methodol. 2016;16(1):85.

 Google Scholar

Soares MO, Sharples L, Morton A, et al. Experiences of structured elicitation for model-based cost-effectiveness analyses. Value Health. 2018;21(6):715–723. [published Online First: 2018/06/19].

 Web of Science ®Google Scholar

Hettle R, Corbett M, Hinde S, et al. The assessment and appraisal of regenerative medicines and cell therapy products: an exploration of methods for review, economic evaluation and appraisal. Health Technol Assess. 2017;21(7):1–204. [published Online First: 2017/03/01].

 PubMed Web of Science ®Google Scholar

Yu B, Peng Y. Mixture cure models for multivariate survival data. Comput Stat Data Anal. 2008;52(3):1524–1532.

 Web of Science ®Google Scholar

Klock RM, Brouwer WB, Annemans LJ, et al. Towards a healthier discount procedure. Expert Rev Pharmacoecon Outcomes Res. 2005;5(1):59–63. [published Online First: 2005/02/01].

 PubMedGoogle Scholar

Brouwer WB, Niessen LW, Postma MJ, et al. Need for differential discounting of costs and health effects in cost effectiveness analyses. BMJ. 2005;331(7514):446–448. [published Online First: 2005/08/20].

 PubMedGoogle Scholar