Achieving dynamic efficiency in pharmaceutical innovation: Identifying the optimal share of value and payments required.

It has been argued that cost-effectiveness analysis of branded pharmaceuticals only considers static efficiency, neglects dynamic effects and undermines incentives for socially valuable innovation. We present a framework for designing pharmaceutical pricing policy to achieve dynamic efficiency. We develop a coherent framework that identifies the long-term static and dynamic benefits and costs of offering manufacturers different levels of reward. The share of value that would maximise long-term population health depends on how the quantity and quality of innovation responds to payment. Using evidence of the response of innovation to payment, the optimal share of value of new pharmaceuticals to offer to manufacturers is roughly 20% (range: 6%-51%). Reanalysis of a sample of NICE technology appraisals suggests that, in most cases, the share of value offered to manufacturers and the price premium paid by the English NHS were too high. In the UK, application of optimal shares would offer considerable benefits under both a public health objective and a broader view of social welfare. We illustrate how an optimal share of value can be delivered through a range of payment mechanisms including indirect price regulation via the use of different approval norms by an HTA body.

Antimicrobial Resistance: Is Health Technology Assessment Part of the Solution or Part of the Problem?

Antimicrobial resistance is a serious challenge to the success and sustainability of our healthcare systems. There has been increasing policy attention given to antimicrobial resistance in the last few years, and increased amounts of funding have been channeled into funding for research and development of antimicrobial agents. Nevertheless, manufacturers doubt whether there will be a market for new antimicrobial technologies sufficient to enable them to recoup their investment. Health technology assessment (HTA) has a critical role in creating confidence that if valuable technologies can be developed they will be reimbursed at a level that captures their true value. We identify 3 deficiencies of current HTA processes for appraising antimicrobial agents: a methods-centric approach rather than problem-centric approach for dealing with new challenges, a lack of tools for thinking about changing patterns of infection, and the absence of an approach to epidemiological risks. We argue that, to play their role more effectively, HTA agencies need to broaden their methodological tool kit, design and communicate their analysis to a wider set of users, and incorporate long-term policy goals, such as containing resistance, as part of their evaluation criteria alongside immediate health gains.

Classifying information-sharing methods.

BACKGROUND: Sparse relative effectiveness evidence is a frequent problem in Health Technology Assessment (HTA). Where evidence directly pertaining to the decision problem is sparse, it may be feasible to expand the evidence-base to include studies that relate to the decision problem only indirectly: for instance, when there is no evidence on a comparator, evidence on other treatments of the same molecular class could be used; similarly, a decision on children may borrow-strength from evidence on adults. Usually, in HTA, such indirect evidence is either included by ignoring any differences ('lumping') or not included at all ('splitting'). However, a range of more sophisticated methods exists, primarily in the biostatistics literature. The objective of this study is to identify and classify the breadth of the available information-sharing methods. METHODS: Forwards and backwards citation-mining techniques were used on a set of seminal papers on the topic of information-sharing. Papers were included if they specified (network) meta-analytic methods for combining information from distinct populations, interventions, outcomes or study-designs. RESULTS: Overall, 89 papers were included. A plethora of evidence synthesis methods have been used for information-sharing. Most papers (n=79) described methods that shared information on relative treatment effects. Amongst these, there was a strong emphasis on methods for information-sharing across multiple outcomes (n=42) and treatments (n=25), with fewer papers focusing on study-designs (n=23) or populations (n=8). We categorise and discuss the methods under four 'core' relationships of information-sharing: functional, exchangeability-based, prior-based and multivariate relationships, and explain the assumptions made within each of these core approaches. CONCLUSIONS: This study highlights the range of information-sharing methods available. These methods often impose more moderate assumptions than lumping or splitting. Hence, the degree of information-sharing that they impose could potentially be considered more appropriate. Our identification of four 'core' methods of information-sharing allows for an improved understanding of the assumptions underpinning the different methods. Further research is required to understand how the methods differ in terms of the strength of sharing they impose and the implications of this for health care decisions.

Estimating the shares of the value of branded pharmaceuticals accruing to manufacturers and to patients served by health systems.

Previous studies have estimated that patients served by health systems accrue 59-98% of the value generated by new pharmaceuticals. This has led to questions about whether sufficient returns accrue to manufacturers to incentivize socially optimal levels of R&D. These studies have not, however, fully reflected the health opportunity costs imposed by payments for branded pharmaceuticals. We present a framework for estimating how the value generated by new branded pharmaceuticals is shared. We quantify value in net health effects and account for benefits and health opportunity costs in the patent period and post-patent period when generic/biosimilar products become available. We apply the framework to 12 National Institute for Health and Care Excellence appraisals and show that realized net health effects range from losses of 160%, to gains of 94%, of the potential net health benefits available. In many cases, even in the long run, the benefits of new medicines are not sufficient to offset the opportunity costs of payments to manufacturers, and approval is expected to reduce population health. This cannot be dynamically efficient as it incentivizes future innovation at prices which will also reduce population health. Further work should consider how to reflect these findings in reimbursement policies.

Partitioned Survival and State Transition Models for Healthcare Decision Making in Oncology: Where Are We Now?

OBJECTIVES: Partitioned survival models (PSMs) are routinely used to inform reimbursement decisions for oncology drugs. We discuss the appropriateness of PSMs compared to the most common alternative, state transition models (STMs). METHODS: In 2017, we published a National Institute for Health and Care Excellence (NICE) Technical Support Document (TSD 19) describing and critically reviewing PSMs. This article summarizes findings from TSD 19, reviews new evidence comparing PSMs and STMs, and reviews recent NICE appraisals to understand current practice. RESULTS: PSMs evaluate state membership differently from STMs and do not include a structural link between intermediate clinical endpoints (eg, disease progression) and survival. PSMs directly consider clinical trial endpoints and can be developed without access to individual patient data, but limit the scope for sensitivity analyses to explore clinical uncertainties in the extrapolation period. STMs facilitate these sensitivity analyses but require development of robust survival models for individual health-state transitions. Recent work has shown PSMs and STMs can produce substantively different survival extrapolations and that extrapolations from STMs are heavily influenced by specification of the underlying survival models. Recent NICE appraisals have not generally included both model types, reviewed individual clinical event data, or scrutinized life-years accrued in individual health states. CONCLUSIONS: The credibility of survival predictions from PSMs and STMs, including life-years accrued in individual health states, should be assessed using trial data on individual clinical events, external data, and expert opinion. STMs should be used alongside PSMs to support assessment of clinical uncertainties in the extrapolation period, such as uncertainty in post-progression survival.

APOE-related risk of mild cognitive impairment and dementia for prevention trials: An analysis of four cohorts.

BACKGROUND: With the onset of prevention trials for individuals at high risk for Alzheimer disease, there is increasing need for accurate risk prediction to inform study design and enrollment, but available risk estimates are limited. We developed risk estimates for the incidence of mild cognitive impairment (MCI) or dementia among cognitively unimpaired individuals by APOE-e4 dose for the genetic disclosure process of the Alzheimer's Prevention Initiative Generation Study, a prevention trial in cognitively unimpaired APOE-e4/e4 homozygote individuals. METHODS AND FINDINGS: We included cognitively unimpaired individuals aged 60-75 y, consistent with Generation Study eligibility criteria, from the National Alzheimer's Coordinating Center (NACC) (n = 5,073, 158 APOE-e4/e4), the Rotterdam Study (n = 6,399, 156 APOE-e4/e4), the Framingham Heart Study (n = 4,078, 67 APOE-e4/e4), and the Sacramento Area Latino Study on Aging (SALSA) (n = 1,294, 11 APOE-e4/e4). We computed stratified cumulative incidence curves by age (60-64, 65-69, 70-75 y) and APOE-e4 dose, adjusting for the competing risk of mortality, and determined risk of MCI and/or dementia by genotype and baseline age. We also used subdistribution hazard regression to model relative hazard based on age, APOE genotype, sex, education, family history of dementia, vascular risk, subjective memory concerns, and baseline cognitive performance. The four cohorts varied considerably in age, education, ethnicity/race, and APOE-e4 allele frequency. Overall, cumulative incidence was uniformly higher in NACC than in the population-based cohorts. Among APOE-e4/e4 individuals, 5-y cumulative incidence was as follows: in the 60-64-y age stratum, it ranged from 0% to 5.88% in the three population-based cohorts versus 23.06% in NACC; in the 65-69-y age stratum, from 9.42% to 10.39% versus 34.62%; and in the 70-75-y age stratum, from 18.64% to 33.33% versus 38.34%. Five-year incidence of dementia was negligible except for APOE-e4/e4 individuals and those over 70 y. Lifetime incidence (to age 80-85 y) of MCI or dementia for the APOE-e4/e4 individuals in the long-term Framingham and Rotterdam cohorts was 34.69%-38.45% at age 60-64 y, 30.76%-40.26% at 65-69 y, and 33.3%-35.17% at 70-75 y. Confidence limits for these estimates are often wide, particularly for APOE-e4/e4 individuals and for the dementia outcome at 5 y. In regression models, APOE-e4 dose and age both consistently increased risk, as did lower education, subjective memory concerns, poorer baseline cognitive performance, and family history of dementia. We discuss several limitations of the study, including the small numbers of APOE-e4/e4 individuals, missing data and differential dropout, limited ethnic and racial diversity, and differences in definitions of exposure and outcome variables. CONCLUSIONS: Estimates of the absolute risk of MCI or dementia, particularly over short time intervals, are sensitive to sampling and a variety of methodological factors. Nonetheless, such estimates were fairly consistent across the population-based cohorts, and lower than those from a convenience cohort and those estimated in prior studies-with implications for informed consent and design for clinical trials targeting high-risk individuals.

Country-Level Cost-Effectiveness Thresholds: Initial Estimates and the Need for Further Research.

BACKGROUND: Cost-effectiveness analysis can guide policymakers in resource allocation decisions. It assesses whether the health gains offered by an intervention are large enough relative to any additional costs to warrant adoption. When there are constraints on the health care system's budget or ability to increase expenditures, additional costs imposed by interventions have an "opportunity cost" in terms of the health foregone because other interventions cannot be provided. Cost-effectiveness thresholds (CETs) are typically used to assess whether an intervention is worthwhile and should reflect health opportunity cost. Nevertheless, CETs used by some decision makers-such as the World Health Organization that suggested CETs of 1 to 3 times the gross domestic product (GDP) per capita-do not. OBJECTIVES: To estimate CETs based on opportunity cost for a wide range of countries. METHODS: We estimated CETs based on recent empirical estimates of opportunity cost (from the English National Health Service), estimates of the relationship between country GDP per capita and the value of a statistical life, and a series of explicit assumptions. RESULTS: CETs for Malawi (the country with the lowest income in the world), Cambodia (with borderline low/low-middle income), El Salvador (with borderline low-middle/upper-middle income), and Kazakhstan (with borderline high-middle/high income) were estimated to be $3 to $116 (1%-51% GDP per capita), $44 to $518 (4%-51%), $422 to $1967 (11%-51%), and $4485 to $8018 (32%-59%), respectively. CONCLUSIONS: To date, opportunity-cost-based CETs for low-/middle-income countries have not been available. Although uncertainty exists in the underlying assumptions, these estimates can provide a useful input to inform resource allocation decisions and suggest that routinely used CETs have been too high.

Methods for network meta-analysis of continuous outcomes using individual patient data: a case study in acupuncture for chronic pain.

BACKGROUND: Network meta-analysis methods, which are an extension of the standard pair-wise synthesis framework, allow for the simultaneous comparison of multiple interventions and consideration of the entire body of evidence in a single statistical model. There are well-established advantages to using individual patient data to perform network meta-analysis and methods for network meta-analysis of individual patient data have already been developed for dichotomous and time-to-event data. This paper describes appropriate methods for the network meta-analysis of individual patient data on continuous outcomes. METHODS: This paper introduces and describes network meta-analysis of individual patient data models for continuous outcomes using the analysis of covariance framework. Comparisons are made between this approach and change score and final score only approaches, which are frequently used and have been proposed in the methodological literature. A motivating example on the effectiveness of acupuncture for chronic pain is used to demonstrate the methods. Individual patient data on 28 randomised controlled trials were synthesised. Consistency of endpoints across the evidence base was obtained through standardisation and mapping exercises. RESULTS: Individual patient data availability avoided the use of non-baseline-adjusted models, allowing instead for analysis of covariance models to be applied and thus improving the precision of treatment effect estimates while adjusting for baseline imbalance. CONCLUSIONS: The network meta-analysis of individual patient data using the analysis of covariance approach is advocated to be the most appropriate modelling approach for network meta-analysis of continuous outcomes, particularly in the presence of baseline imbalance. Further methods developments are required to address the challenge of analysing aggregate level data in the presence of baseline imbalance.

Antimicrobial treatment imprecision: an outcome-based model to close the data-to-action loop.

Health-care systems, food supply chains, and society in general are threatened by the inexorable rise of antimicrobial resistance. This threat is driven by many factors, one of which is inappropriate antimicrobial treatment. The ability of policy makers and leaders in health care, public health, regulatory agencies, and research and development to deliver frameworks for appropriate, sustainable antimicrobial treatment is hampered by a scarcity of tangible outcome-based measures of the damage it causes. In this Personal View, a mathematically grounded, outcome-based measure of antimicrobial treatment appropriateness, called imprecision, is proposed. We outline a framework for policy makers and health-care leaders to use this metric to deliver more effective antimicrobial stewardship interventions to future patient pathways. This will be achieved using learning antimicrobial systems built on public and practitioner engagement; solid implementation science; advances in artificial intelligence; and changes to regulation, research, and development. The outcomes of this framework would be more ecologically and organisationally sustainable patterns of antimicrobial development, regulation, and prescribing. We discuss practical, ethical, and regulatory considerations involved in the delivery of novel antimicrobial drug development, and policy and patient pathways built on artificial intelligence-augmented measures of antimicrobial treatment imprecision.

Cefiderocol for treating severe aerobic Gram-negative bacterial infections: technology evaluation to inform a novel subscription-style payment model.

Background: To limit the use of antimicrobials without disincentivising the development of novel antimicrobials, there is interest in establishing innovative models that fund antimicrobials based on an evaluation of their value as opposed to the volumes used. The aim of this project was to evaluate the population-level health benefit of cefiderocol in the NHS in England, for the treatment of severe aerobic Gram-negative bacterial infections when used within its licensed indications. The results were used to inform the National Institute for Health and Care Excellence guidance in support of commercial discussions regarding contract value between the manufacturer and NHS England. Methods: The health benefit of cefiderocol was first derived for a series of high-value clinical scenarios. These represented uses that were expected to have a significant impact on patients' mortality risks and health-related quality of life. The clinical effectiveness of cefiderocol relative to its comparators was estimated by synthesising evidence on susceptibility of the pathogens of interest to the antimicrobials in a network meta-analysis. Patient-level costs and health outcomes of cefiderocol under various usage scenarios compared with alternative management strategies were quantified using decision modelling. Results were reported as incremental net health effects expressed in quality-adjusted life-years, which were scaled to 20-year population values using infection number forecasts based on data from Public Health England. The outcomes estimated for the high-value clinical scenarios were extrapolated to other expected uses for cefiderocol. Results: Among Enterobacterales isolates with the metallo-beta-lactamase resistance mechanism, the base-case network meta-analysis found that cefiderocol was associated with a lower susceptibility relative to colistin (odds ratio 0.32, 95% credible intervals 0.04 to 2.47), but the result was not statistically significant. The other treatments were also associated with lower susceptibility than colistin, but the results were not statistically significant. In the metallo-beta-lactamase Pseudomonas aeruginosa base-case network meta-analysis, cefiderocol was associated with a lower susceptibility relative to colistin (odds ratio 0.44, 95% credible intervals 0.03 to 3.94), but the result was not statistically significant. The other treatments were associated with no susceptibility. In the base case, patient-level benefit of cefiderocol was between 0.02 and 0.15 quality-adjusted life-years, depending on the site of infection, the pathogen and the usage scenario. There was a high degree of uncertainty surrounding the benefits of cefiderocol across all subgroups. There was substantial uncertainty in the number of infections that are suitable for treatment with cefiderocol, so population-level results are presented for a range of scenarios for the current infection numbers, the expected increases in infections over time and rates of emergence of resistance. The population-level benefits varied substantially across the base-case scenarios, from 896 to 3559 quality-adjusted life-years over 20 years. Conclusion: This work has provided quantitative estimates of the value of cefiderocol within its areas of expected usage within the NHS. Limitations: Given existing evidence, the estimates of the value of cefiderocol are highly uncertain. Future work: Future evaluations of antimicrobials would benefit from improvements to NHS data linkages; research to support appropriate synthesis of susceptibility studies; and application of routine data and decision modelling to assess enablement value. Study registration: No registration of this study was undertaken. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment Policy Research Programme (NIHR award ref: NIHR135591), conducted through the Policy Research Unit in Economic Methods of Evaluation in Health and Social Care Interventions, PR-PRU-1217-20401, and is published in full in Health Technology Assessment; Vol. 28, No. 28. See the NIHR Funding and Awards website for further award information.

This project tested new methods for estimating the value to the NHS of an antimicrobial, cefiderocol, so its manufacturer could be paid fairly even if very little drug is used in order to reduce the risk of bacteria becoming resistant to the product. Clinicians said that the greatest benefit of cefiderocol is when used for complicated urinary tract infections and pneumonia acquired within hospitals caused by two types of bacteria (called Enterobacterales and Pseudomonas aeruginosa), with a resistance mechanism called metallo-beta-lactamase. Because there were no relevant clinical trial data, we estimated how effective cefiderocol and alternative treatments were by doing a systematic literature review of studies that grew bacteria from infections in the laboratory and tested the drugs on them. We linked this to data estimating the long-term health and survival of patients. Some evidence was obtained by asking clinicians detailed questions about what they thought the effects would be based on their experience and the available evidence. We included the side effects of the alternative treatments, some of which can cause kidney damage. We estimated how many infections there would be in the UK, whether they would increase over time and how resistance to treatments may change over time. Clinicians told us that they would also use cefiderocol to treat intra-abdominal and bloodstream infections, and some infections caused by another bacteria called Stenotrophomonas. We estimated how many of these infections there would be, and assumed the same health benefits as for other types of infections. The total value to the NHS was calculated using these estimates. We also considered whether we had missed any additional elements of value. We estimated that the value to the NHS was £18­71 million over 20 years. This reflects the maximum the NHS could pay for use of cefiderocol if the health lost as a result of making these payments rather than funding other NHS services is not to exceed the health benefits of using this antimicrobial. However, these estimates are uncertain due to limitations with the evidence used to produce them and assumptions that had to be made.

Using economic analysis to inform health resource allocation: lessons from Malawi.

Despite making remarkable strides in improving health outcomes, Malawi faces concerns about sustaining the progress achieved due to limited fiscal space and donor dependency. The imperative for efficient health spending becomes evident, necessitating strategic allocation of resources to areas with the greatest impact on mortality and morbidity. Health benefits packages hold promise in supporting efficient resource allocation. However, despite defining these packages over the last two decades, their development and implementation have posed significant challenges for Malawi. In response, the Malawian government, in collaboration with the Thanzi la Onse Programme, has developed a set of tools and frameworks, primarily based on cost-effectiveness analysis, to guide the design of health benefits packages likely to achieve national health objectives. This review provides an overview of these tools and frameworks, accompanied by other related analyses, aiming to better align health financing with health benefits package prioritization. The paper is organized around five key policy questions facing decision-makers: (i) What interventions should the health system deliver? (ii) How should resources be allocated geographically? (iii) How should investments in health system inputs be prioritized? (iv) How should equity considerations be incorporated into resource allocation decisions? and (v) How should evidence generation be prioritized to support resource allocation decisions (guiding research)? The tools and frameworks presented here are intended to be compatible for use in diverse and often complex healthcare systems across Africa, supporting the health resource allocation process as countries pursue Universal Health Coverage.

How does cholesterol burden change the case for investing in familial hypercholesterolaemia? A cost-effectiveness analysis.

BACKGROUND AND AIMS: This study aimed to ascertain how the long-term benefits and costs of diagnosis and treatment of familial hypercholesterolaemia (FH) vary by prognostic factors and 'cholesterol burden', which is the effect of long-term exposure to low-density lipoprotein cholesterol (LDL-C) on cardiovascular disease (CVD) risk. METHODS: A new cost-effectiveness model was developed from the perspective of the UK National Health Service (NHS), informed by routine data from individuals with FH. The primary outcome was net health gain (i.e., health benefits net of the losses due to costs), expressed in quality-adjusted life years (QALYs) at the £15,000/QALY threshold. Prognostic factors included pre-treatment LDL-C, age, gender, and CVD history. RESULTS: If cholesterol burden is considered, diagnosis resulted in positive net health gain (i.e., it is cost-effective) in all individuals with pre-treatment LDL-C ≥ 4 mmol/L, and in those with pre-treatment LDL-C ≥ 2 mmol/L aged ≥50 years or who have CVD history. If cholesterol burden is not considered, diagnosis resulted in lower net health gain, but still positive in children aged 10 years with pre-treatment LDL-C ≥ 6 mmol/L and adults aged 30 years with pre-treatment LDL-C ≥ 4 mmol/L. CONCLUSIONS: Diagnosis and treatment of most people with FH results in large net health gains, particularly in those with higher pre-treatment LDL-C. Economic evaluations of FH interventions should consider the sensitivity of the study conclusions to cholesterol burden, particularly where interventions target younger patients, and explicitly consider prognostic factors such as pre-treatment LDL-C, age, and CVD history.

Bendamustine versus chlorambucil for the first-line treatment of chronic lymphocytic leukemia in England and Wales: a cost-utility analysis.

OBJECTIVES: To evaluate the cost-effectiveness of bendamustine compared with chlorambucil as first-line treatment for patients with chronic lymphocytic leukemia who would be considered unsuitable for treatment with fludarabine combination chemotherapy regimens. METHODS: A semi-Markov approach was used to estimate time in each health state. The model was parameterized primarily by using data from a phase III randomized, open-label trial comparing bendamustine with chlorambucil. It captured the increased progression-free survival and improved response rates with bendamustine, and the cost and quality of life impacts of postprogression treatments. The analysis was conducted from the perspective of the National Health Service in England and Wales. A lifetime (35-year) time horizon was used. Deterministic sensitivity analyses, probabilistic sensitivity analyses, and subgroup analyses in older patients and patients with poor performance status were carried out. RESULTS: The estimated incremental cost-effectiveness ratio was £ 11,960 per quality-adjusted life-year. None of the deterministic sensitivity analyses increased the incremental cost-effectiveness ratio by more than £ 2000. Subgroup analyses showed that bendamustine remained cost-effective across different patient groups. Probabilistic sensitivity analysis showed that at the £ 20,000 threshold, bendamustine has a 90% probability of being cost-effective. CONCLUSIONS: Bendamustine represents good value for first-line treatment of patients with chronic lymphocytic leukemia who are unsuitable for treatment with fludarabine combination chemotherapy. The incremental cost-effectiveness ratio is below the thresholds commonly applied in England and Wales (£ 20,000-£ 30,000 per quality-adjusted life-year).

Prioritizing pharmacogenetic research: a value of information analysis of CYP2D6 testing to guide breast cancer treatment.

OBJECTIVES: To demonstrate how value of information (VOI) analysis can be used to establish research priorities regarding the use of pharmacogenetic tests using CYP2D6 testing to select adjuvant hormonal therapy in early stage breast cancer as a case study. METHODS: The following four treatment pathways are compared in a Markov model: tamoxifen treatment; CYP2D6 test and treat homozygous and heterozygous wild type patients (wt/wt; wt/*4) with tamoxifen and *4/*4 patients with anastrozole (HetTam); CYP2D6 test and treat homozygous wild type patients with tamoxifen and others with anastrozole (HomTam); and anastrozole treatment. Pharmacogenetic testing efficacy is estimated by synthesizing randomized controlled trial data comparing tamoxifen to anastrozole with observational data linking CYP2D6 genotype to tamoxifen outcomes. RESULTS: In order of increasing effectiveness the comparators are tamoxifen, HetTam, HomTam, anastrozole. Health outcomes for test and treatment strategies are highly uncertain. Differences in comparator costs depend on assumptions made regarding anastrozole patent expiry. The expected value of a decision taken with perfect information is £69 to £106 million (pound sterling) for the United Kingdom depending on patent expiry assumptions and the acceptable cost-effectiveness threshold. The most valuable research (VOI £53-£82 million) elucidates the relationship between CYP2D6 genotype and tamoxifen effectiveness. It is uncertain whether values of other research designs would exceed their costs. CONCLUSIONS: Retrospective analysis of one of the large adjuvant aromatase inhibitor trials is warranted to better understand any association between CYP2D6 genotype and tamoxifen outcomes. VOI approaches may be helpful for prioritising evidence needs and structuring coverage with evidence development agreements for pharmacogenetics.

Concomitant health benefits package design and research prioritisation: development of a new approach and an application to Malawi.

Health benefits packages (HBPs) are increasingly used in many countries to guide spending priorities on the path towards universal health coverage. Their design is, however, informed by an uncertain evidence base but research funds available to address this are limited. This gives rise to the question of which piece of research relating to the cost-effectiveness of interventions would most contribute to improving resource allocation. We propose to incorporate research prioritisation as an integral part of HBP design. We have, therefore, developed a framework and a freely available companion stand-alone tool, to quantify in terms of net disability-adjusted life-years (DALYs) averted, the value of research for the interventions considered for inclusion in a package. Using the tool, the framework can be implemented using sensitivity analysis results typically reported in cost-effectiveness studies. To illustrate the framework, we applied the tool to the evidence base that informed the Malawi Health Sector Strategic Plan 2017-2022. Out of 21 interventions considered, 8 investment decisions were found to be uncertain and three showed strong potential for research to generate large health gains: 'male circumcision', 'community-management of acute malnutrition in children' and 'isoniazid preventive therapy in HIV +individuals', with a potential to avert up to 65 762, 36 438 and 20 132 net DALYs, respectively. Our work can help set research priorities in resource-constrained settings so that research funds are invested where they have the largest potential to impact on the population health generated via HBPs.

Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: a tutorial.

BACKGROUND: Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on a single statistic. In this case, studies which do not report the chosen statistic are excluded from the analysis which may introduce bias. METHODS: In this paper we present a tutorial illustrating how network meta-analyses of survival endpoints can combine count and hazard ratio statistics in a single analysis on the hazard ratio scale. We also describe methods for accounting for the correlations in relative treatment effects (such as hazard ratios) that arise in trials with more than two arms. Combination of count and hazard ratio data in a single analysis is achieved by estimating the cumulative hazard for each trial arm reporting count data. Correlation in relative treatment effects in multi-arm trials is preserved by converting the relative treatment effect estimates (the hazard ratios) to arm-specific outcomes (hazards). RESULTS: A worked example of an analysis of mortality data in chronic obstructive pulmonary disease (COPD) is used to illustrate the methods. The data set and WinBUGS code for fixed and random effects models are provided. CONCLUSIONS: By incorporating all data presentations in a single analysis, we avoid the potential selection bias associated with conducting an analysis for a single statistic and the potential difficulties of interpretation, misleading results and loss of available treatment comparisons associated with conducting separate analyses for different summary statistics.

Practical metrics for establishing the health benefits of research to support research prioritisation.

INTRODUCTION: We present practical metrics for estimating the expected health benefits of specific research proposals. These can be used by research funders, researchers and healthcare decision-makers within low-income and middle-income countries to support evidence-based research prioritisation. METHODS: The methods require three key assessments: (1) the current level of uncertainty around the endpoints the proposed study will measure; (2) how uncertainty impacts on the health benefits and costs of healthcare programmes and (3) the health opportunity costs imposed by programme costs. Research is valuable because it can improve health by informing the choice of which programmes should be implemented. We provide a Microsoft Excel tool to allow readers to generate estimates of the health benefits of research studies based on these three assessments. The tool can be populated using existing studies, existing cost-effectiveness models and expert opinion. Where such evidence is not available, the tool can quantify the value of research under different assumptions. Estimates of the health benefits of research can be considered alongside research costs, and the consequences of delaying implementation until research reports, to determine whether research is worthwhile. We illustrate the method using a case study of research on HIV self-testing programmes in Malawi. This analysis combines data from the literature with outputs from the HIV synthesis model. RESULTS: For this case study, we found a costing study that could be completed and inform decision making within 1 year offered the highest health benefits (67 000 disability-adjusted life years (DALYs) averted). Research on outcomes improved population health to a lesser extent (12 000 DALYs averted) and only if carried out alongside programme implementation. CONCLUSION: Our work provides a method for estimating the health benefits of research in a practical and timely fashion. This can be used to support accountable use of research funds.

No study left behind: a network meta-analysis in non-small-cell lung cancer demonstrating the importance of considering all relevant data.

OBJECTIVE: To demonstrate the importance of considering all relevant indirect data in a network meta-analysis of treatments for non-small-cell lung cancer (NSCLC). METHODS: A recent National Institute for Health and Clinical Excellence appraisal focussed on the indirect comparison of docetaxel with erlotinib in second-line treatment of NSCLC based on trials including a common comparator. We compared the results of this analysis to a network meta-analysis including other trials that formed a network of evidence. We also examined the importance of allowing for the correlations between the estimated treatment effects that can arise when analysing such networks. RESULTS: The analysis of the restricted network including only trials of docetaxel and erlotinib linked via the common placebo comparator produced an estimated mean hazard ratio (HR) for erlotinib compared with docetaxel of 1.55 (95% confidence interval [CI] 0.72-2.97). In contrast, the network meta-analysis produced an estimated HR for erlotinib compared with docetaxel of 0.83 (95% CI 0.65-1.06). Analyzing the wider network improved the precision of estimated treatment effects, altered their rankings and also allowed further treatments to be compared. Some of the estimated treatment effects from the wider network were highly correlated. CONCLUSIONS: This empirical example shows the importance of considering all potentially relevant data when comparing treatments. Care should therefore be taken to consider all relevant information, including correlations induced by the network of trial data, when comparing treatments.

How far do you go? Efficient searching for indirect evidence.

BACKGROUND: Indirect evidence is particularly valuable in health care decision making when direct trial evidence comparing relevant treatments is absent or limited. Current approaches using a predetermined set of comparators in the search query may fail to identify all relevant indirect evidence. PURPOSE: To present a framework for the efficient design of search strategies for identifying clinical trials providing indirect evidence for a treatment comparison. FINDINGS: The authors present 2 search strategies that differ from traditional search strategies in using a series of iterative searches to identify the set of relevant comparators. In both, the comparators included in each search are determined by the results of previous searches. For a given number of searches, the strategies presented will find all indirect comparisons that include a certain number of comparators linking the treatments of interest. Methods of estimating the value of indirect evidence via a given number of comparators linking the treatments of interest are presented, thus allowing the burden of additional searching to be traded off against the likely impact of finding more distant comparisons. A practical illustration of the search strategies in the context of informing a network meta-analysis of second-line treatments for non-small cell lung cancer is presented. CONCLUSIONS: The iterative strategies presented offer a means of identifying such evidence and allow the researcher to determine the optimal scope of the search by estimating the value of additional indirect evidence.

Appraising the value of evidence generation activities: an HIV modelling study.

INTRODUCTION: The generation of robust evidence has been emphasised as a priority for global health. Evidence generation spans a wide range of activities including clinical trials, surveillance programmes and health system performance measurement. As resources for healthcare and research are limited, the desirability of research expenditure should be assessed on the same basis as other healthcare resources, that is, the health gains from research must be expected to exceed the health opportunity costs imposed as funds are diverted to research rather than service provision. METHODS: We developed a transmission and costing model to examine the impact of generating additional evidence to reduce uncertainties on the evolution of a generalised HIV epidemic in Zambia. RESULTS: We demonstrate three important points. First, we can quantify the value of additional evidence in terms of the health gain it is expected to generate. Second, we can quantify the health opportunity cost imposed by research expenditure. Third, the value of evidence generation depends on the budgetary policies in place for managing HIV resources under uncertainty. Generating evidence to reduce uncertainty is particularly valuable when decision makers are required to strictly adhere to expenditure plans and when transfers of funds across geographies/programmes are restricted. CONCLUSION: Better evidence can lead to health improvements in the same way as direct delivery of healthcare. Quantitative appraisals of evidence generation activities are important and should reflect the impact of improved evidence on population health, evidence generation costs and budgetary policies in place.