April 2019


A Review of Clinical Trials With an Adaptive Design and Health Economic Analysis 

Flight L, Arshad F, Barnsley R, Patel K, Julious S, Brennan A, Todd S.
Value in Health.  2019;22(4):391-398.

OBJECTIVE
An adaptive design uses data collected as a clinical trial progresses to inform modifications to the trial. Hence, adaptive designs and health economics aim to facilitate efficient and accurate decision making. Nevertheless, it is unclear whether the methods are considered together in the design, analysis, and reporting of trials. This review aims to establish how health economic outcomes are used in the design, analysis, and reporting of adaptive designs.

METHODS
Registered and published trials up to August 2016 with an adaptive design and health economic analysis were identified. The use of health economics in the design, analysis, and reporting was assessed. Summary statistics are presented and recommendations formed based on the research team's experiences and a practical interpretation of the results.

RESULTS
Thirty-seven trials with an adaptive design and health economic analysis were identified. It was not clear whether the health economic analysis accounted for the adaptive design in 17/37 trials where this was thought necessary, nor whether health economic outcomes were used at the interim analysis for 18/19 of trials with results. The reporting of health economic results was suboptimal for the (17/19) trials with published results.

CONCLUSION
Appropriate consideration is rarely given to the health economic analysis of adaptive designs. Opportunities to use health economic outcomes in the design and analysis of adaptive trials are being missed. Further work is needed to establish whether adaptive designs and health economic analyses can be used together to increase the efficiency of health technology assessments without compromising accuracy.

Estimating Joint Health Condition Utility Values

Thompson AJ, Sutton M, Payne K.
Value in Health. 2019;22(4):82-490.

OBJECTIVE
To predict health state utility values (HSUVs) for individuals with up to 4 conditions simultaneously.

METHODS
Person-level data were taken from the General Practice Patient Survey, a national survey of adult patients registered with general practices in England. Individuals reported whether they had any 1 of 16 chronic conditions and completed the 3-level EuroQol 5-dimensional questionnaire. Four nonparametric methods (additive, multiplicative, minimum, and the adjusted decrement estimator) and 1 parametric estimator (the linear index) were used to predict HSUVs for individuals with a joint health condition (JHC). Predicted and actual utility scores were compared for precision using root mean square error and mean absolute error. Bias was assessed using mean error.

RESULTS
The analysis included 929,565 individuals, of which 30.5% had at least 2 conditions. Of the nonparametric estimators, the multiplicative approach produced estimates with the lowest bias and most precision for 2 JHCs. For populations with a long-term mental health condition within the JHC, the multiplicative approach overestimated utility scores. All nonparametric methods produced biased results when estimating HSUVs for 3 or 4 JHCs. The linear index generally produced unbiased results with the highest precision.

CONCLUSION
The multiplicative approach was the best nonparametric estimator when estimating HSUVs for 2 JHCs. None of the nonparametric approaches for estimating HSUVs can be recommended with more than 2 JHCs. The linear index was found to have good predictive properties but needs external validation before being recommended for routine use.

Understanding Patients' Preferences: A Systematic Review of Psychological Instruments Used in Patients' Preference and Decision Studies

Russo S, Jongerius C, Faccio F, Pizzoli SFM, Pinto CA, Veldwijk J, Janssens R, Simons G, Falahee M, de Bekker-Grob E, Huys I, Postmus D, Kihlbom U, Pravettoni G.
Value in Health. 2019;22(4):439-445.

BACKGROUND
Research has been mainly focused on how to elicit patient preferences, with less attention on why patients form certain preferences.

OBJECTIVE
To assess which psychological instruments are currently used and which psychological constructs are known to have an impact on patients' preferences and health-related decisions including the formation of preferences and preference heterogeneity.

METHODS
A systematic database search was undertaken to identify relevant studies. From the selected studies, the following information was extracted: study objectives, study population, design, psychological dimensions investigated, and instruments used to measure psychological variables.

RESULTS
Thirty-three studies were identified that described the association between a psychological construct, measured using a validated instrument, and patients' preferences or health-related decisions. We identified 33 psychological instruments and 18 constructs, and categorized the instruments into 5 groups, namely, motivational factors, cognitive factors, individual differences, emotion and mood, and health beliefs.

CONCLUSION
This review provides an overview of the psychological factors and related instruments in the context of patients' preferences and decisions in healthcare settings. Our results indicate that measures of health literacy, numeracy, and locus of control have an impact on health-related preferences and decisions. Within the category of constructs that could explain preference and decision heterogeneity, health locus of control is a strong predictor of decisions in several healthcare contexts and is useful to consider when designing a patient preference study. Future research should continue to explore the association of psychological constructs with preference formation and heterogeneity to build on these initial recommendations.


May 2019



June 2019


Are Payers Ready, Willing, and Able to Provide Access to New Durable Gene Therapies?

Barlow JF, Yang M, Teagarden JR.
Value in Health. 2019;22(6):642-647.

OBJECTIVE
To explore payer feedback regarding awareness of new gene therapies, sustainability of current financing mechanisms, unique challenges by payer segment, and need and preference for new financial models.

STUDY DESIGN
Qualitative interview with standardized interview guide.

METHODS
Sixty-minute telephone interviews were conducted with financial decision makers from 15 US payers between August and September 2017.

RESULTS
One-third of payers interviewed (n = 5) were newly aware and learning about new gene therapies, 40% (n = 6) described watchful waiting, whereas 26.7% (n = 4) were engaged in active management. New payment models—specifically, performance-based agreements and risk-pooling—were supported by 47% (n = 7) of payers, whereas the current payment model was supported by 53% (n = 8). Major challenges included uncertainty related to utilization, cost, and duration of cure. Payers cited regulation, plan turnover, and ability to track long-term outcomes as barriers to implementation of new models.

CONCLUSIONS
Access to new gene therapies may be impacted by payer ability to absorb the cost of coverage. Variation exists in awareness of new gene therapies and level of incorporation of new costs into future plan coverage. The sustainability of current financing mechanisms varies by payer segment, profitability, and size; smaller plans and Medicaid are likely to be impacted first. Government reinsurance, commercial reinsurance, and stop-loss insurance backstop current reimbursement models, dampening the need for urgent action. The tipping point for action may be severe premium inflation in stop loss and reinsurance. Payers are open to innovative financing models that improve financial predictability and reward clinical performance.

New Cost-Effectiveness Methods to Determine Value-Based Prices for Potential Cures: What Are the Options?

Pearson SD, Ollendorf DA, Chapman RH.
Value in Health. 2019;22(6):665-660.

ABSTRACT
Evaluating different approaches to assessing the clinical effectiveness and value of potential cures will be essential to arm the policymaker, payer, and manufacturer communities with a platform that can reward innovation while supporting a sustainable health insurance system. Potential cures will accentuate concerns about substantial uncertainty in long-term outcomes. They will also focus attention on whether broader elements of value need to be incorporated and whether specific social values have a special place in evaluations of potential cures. In addition, the large magnitudes of potential health gain and cost offsets may require new methods before translation into value-based price recommendations. This article analyzes the challenges and presents several options to modify the conduct and presentation of cost-effectiveness analyses to ensure they provide policy-relevant assessments of the value of potential cures.

 

Analytic Considerations in Applying a General Economic Evaluation Reference Case to Gene Therapy

Drummond MF, Neumann PJ, Sullivan SD, Fricke FU, Tunis S, Dabbous O, Toumi M.
Value in Health. 2019;22(6):661-668.

ABSTRACT
The concept of a reference case, first proposed by the US Panel on Cost-Effectiveness in Health and Medicine, has been used to specify the required methodological features of economic evaluations of healthcare interventions. In the case of gene therapy, there is a difference of opinion on whether a specific methodological reference case is required. The aim of this article was to provide a more detailed analysis of the characteristics of gene therapy and the extent to which these characteristics warrant modifications to the methods suggested in general reference cases for economic evaluation. We argue that a completely new reference case is not required, but propose a tailored checklist that can be used by analysts and decision makers to determine which aspects of economic evaluation should be considered further, given the unique nature of gene therapy.

 

 

Comparing the Noncomparable: The Need for Equivalence Measures That Make Sense in Health-Economic Evaluations

Johnson FR, Scott FI, Reed SD, Lewis JD, Bewtra M.
Value in Health. 2019;22(6):684-692.

BACKGROUND
The popularity of quality-adjusted life years (QALYs) has been resistant to concerns about validity and reliability. Utility-theoretic outcome equivalents are widely used in other areas of applied economics. Equivalence values can be derived for time, money, risk, and other metrics. These equivalence measures preserve all available information about individual preferences and are valid measures of individual welfare changes.

OBJECTIVE
The objective of this study was to derive alternative generalized equivalence measures from first principles and illustrate their application in an empirical comparative-effectiveness example.

METHODS
We specify a general-equilibrium model incorporating neoclassical utility functions, health production function, severity-duration preferences, and labor-market tradeoff function. The empirical implementation takes advantage of discrete-choice experiment methods that are widely accepted in other areas of applied economics and increasingly in health economics. We illustrate the practical significance of restrictive QALY assumptions using comparative-effectiveness results based on both QALYs and estimates of welfare-theoretic time-equivalent values for anti-tumor necrosis factor and prolonged corticosteroid treatments for Crohn’s disease in three distinct preference classes.

RESULTS
The QALY difference between the two treatments is 0.2 months, while time-equivalent values range between 0.5 and 1.3 months for aggregate and class-specific differences. Thus, the QALY-based analysis understates welfare-theoretic values by 60%-85%.

CONCLUSIONS
These results suggest that using disease-specific equivalence values offer a meaningful alternative to QALYs to compare global outcomes across treatments. The equivalence values approach is consistent with principles of welfare economics and offers several features not represented in QALYs, including accounting for preference nonlinearities in disease severity and duration, inclusion of preference-relevant nonclinical healthcare factors, representing preferences of clinically-relevant patient subpopulations, and including utility losses related to risk aversion.

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