Advances in Addressing Patient Heterogeneity in Economic Evaluation: A Review of the Methods Literature.

Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.

Generating EQ-5D-3L health utility scores from the Edinburgh Postnatal Depression Scale: a perinatal mapping study.

BACKGROUND: Perinatal depression (PND) describes depression experienced by parents during pregnancy or in the first year after a baby is born. The EQ-5D instrument (a generic measure of health status) is not often collected in perinatal research, however disease-specific measures, such as the Edinburgh Postnatal Depression Scale (EPDS) are widely used. Mapping can be used to estimate generic health utility index values from disease-specific measures like the EPDS. OBJECTIVE: To develop a mapping algorithm to estimate EQ-5D utility index values from the EPDS. METHODS: Patient-level data from the BaBY PaNDA study (English observational cohort study) provided 1068 observations with paired EPDS and EQ-5D (3-level version; EQ-5D-3L) responses. We compared the performance of six alternative regression model types, each with four specifications of covariates (EPDS score and age: base, squared, and cubed). Model performance (ability to predict utility values) was assessed by ranking mean error, mean absolute error, and root mean square error. Algorithm performance in 3 external datasets was also evaluated. RESULTS: There was moderate correlation between EPDS score and utility values (coefficient: - 0.42). The best performing model type was a two-part model, followed by ordinary least squared. Inclusion of squared and cubed covariates improved model performance. Based on graphs of observed and predicted utility values, the algorithm performed better when utility was above 0.6. CONCLUSIONS: This direct mapping algorithm allows the estimation of health utility values from EPDS scores. The algorithm has good external validity but is likely to perform better in samples with higher health status.

An economic evaluation of universal and targeted case-finding strategies for identifying antenatal depression: a model-based analysis comparing common case-finding instruments.

Half of women with depression in the perinatal period are not identified in routine care, and missed cases reflect inequalities in other areas of maternity care. Case finding (screening) for depression in pregnant women may be a cost-effective strategy to improve identification, and targeted case finding directs finite resources towards the greatest need. We compared the cost-effectiveness of three case-finding strategies: no case finding, universal (all pregnant women), and targeted (only pregnant women with risk factors for antenatal depression, i.e. history of anxiety/depression, age < 20 years, and adverse life events). A decision tree model was developed to represent case finding (at around 20 weeks gestation) and subsequent treatment for antenatal depression (up to 40 weeks gestation). Costs include case finding and treatment. Health benefits are measured as quality-adjusted life years (QALYs). The sensitivity and specificity of case-finding instruments and prevalence and severity of antenatal depression were estimated from a cohort study of pregnant women. Other model parameters were derived from published literature and expert consultation. The most cost-effective case-finding strategy was a two-stage strategy comprising the Whooley questions followed by the PHQ-9. The mean costs were £52 (universal), £61 (no case finding), and £62 (targeted case finding). Both case-finding strategies improve health compared with no case finding. Universal case finding is cost-saving. Costs associated with targeted case finding are similar to no case finding, with greater health gains, although targeted case finding is not cost-effective compared with universal case finding. Universal case finding for antenatal depression is cost-saving compared to no case finding and more cost-effective than targeted case finding.

A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment.

BACKGROUND: External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they compare. PURPOSE: This review aims to identify, describe, and categorize established methods to incorporate external evidence into survival extrapolation for HTA. DATA SOURCES: Embase, MEDLINE, EconLit, and Web of Science databases were searched to identify published methodological studies, supplemented by hand searching and citation tracking. STUDY SELECTION: Eligible studies were required to present a novel extrapolation approach incorporating external evidence (i.e., data or information) within survival model estimation. DATA EXTRACTION: Studies were classified according to how the external evidence was integrated as a part of model fitting. Information was extracted concerning the model-fitting process, key requirements, assumptions, software, application contexts, and presentation of comparisons with, or validation against, other methods. DATA SYNTHESIS: Across 18 methods identified from 22 studies, themes included use of informative prior(s) (n = 5), piecewise (n = 7), and general population adjustment (n = 9), plus a variety of "other" (n = 8) approaches. Most methods were applied in cancer populations (n = 13). No studies compared or validated their method against another method that also incorporated external evidence. LIMITATIONS: As only studies with a specific methodological objective were included, methods proposed as part of another study type (e.g., an economic evaluation) were excluded from this review. CONCLUSIONS: Several methods were identified in this review, with common themes based on typical data sources and analytical approaches. Of note, no evidence was found comparing the identified methods to one another, and so an assessment of different methods would be a useful area for further research.HighlightsThis review aims to identify methods that have been used to incorporate external evidence into survival extrapolations, focusing on those that may be used to inform health technology assessment.We found a range of different approaches, including piecewise methods, Bayesian methods using informative priors, and general population adjustment methods, as well as a variety of "other" approaches.No studies attempted to compare the performance of alternative methods for incorporating external evidence with respect to the accuracy of survival predictions. Further research investigating this would be valuable.

An economic evaluation of targeted case-finding strategies for identifying postnatal depression: A model-based analysis comparing common case-finding instruments.

BACKGROUND: Half of women with postnatal depression (PND) are not identified in routine care. We aimed to estimate the cost-effectiveness of PND case-finding in women with risk factors for PND. METHODS: A decision tree was developed to represent the one-year costs and health outcomes associated with case-finding and treatment for PND. The sensitivity and specificity of case-finding instruments, and prevalence and severity of PND, for women with ≥1 PND risk factor were estimated from a cohort of postnatal women. Risk factors were history of anxiety/depression, age < 20 years, and adverse life events. Other model parameters were derived from published literature and expert consultation. Case-finding for high-risk women only was compared with no case-finding and universal case-finding. RESULTS: More than half of the cohort had one or more PND risk factor (57.8 %; 95 % CI 52.7 %-62.7 %). The most cost-effective case-finding strategy was the Edinburgh Postnatal Depression Scale with a cut-off of ≥10 (EPDS-10). Among high-risk women, there is a high probability that EPDS-10 case-finding for PND is cost-effective compared to no case-finding (78.5 % at a threshold of £20,000/QALY), with an ICER of £8146/QALY gained. Universal case-finding is even more cost-effective at £2945/QALY gained (versus no case-finding). There is a greater health improvement with universal rather than targeted case-finding. LIMITATIONS: The model includes costs and health benefits for mothers in the first year postpartum, the broader (e.g. families, societal) and long-term impacts are also important. CONCLUSIONS: Universal PND case-finding is more cost-effective than targeted case-finding which itself is more cost-effective than not case-finding.

Cost-effectiveness of home-based cardiac rehabilitation: a systematic review.

OBJECTIVE: Centre-based cardiac rehabilitation (CR) is recognised as cost-effective for individuals following a cardiac event. However, home-based alternatives are becoming increasingly popular, especially since COVID-19, which necessitated alternative modes of care delivery. This review aimed to assess whether home-based CR interventions are cost-effective (vs centre-based CR). METHODS: Using the MEDLINE, Embase and PsycINFO databases, literature searches were conducted in October 2021 to identify full economic evaluations (synthesising costs and effects). Studies were included if they focused on home-based elements of a CR programme or full home-based programmes. Data extraction and critical appraisal were completed using the NHS EED handbook, Consolidated Health Economic Evaluation Reporting Standards and Drummond checklists and were summarised narratively. The protocol was registered on the PROSPERO database (CRD42021286252). RESULTS: Nine studies were included in the review. Interventions were heterogeneous in terms of delivery, components of care and duration. Most studies were economic evaluations within clinical trials (8/9). All studies reported quality-adjusted life years, with the EQ-5D as the most common measure of health status (6/9 studies). Most studies (7/9 studies) concluded that home-based CR (added to or replacing centre-based CR) was cost-effective compared with centre-based options. CONCLUSIONS: Evidence suggests that home-based CR options are cost-effective. The limited size of the evidence base and heterogeneity in methods limits external validity. There were further limitations to the evidence base (eg, limited sample sizes) that increase uncertainty. Future research is needed to cover a greater range of home-based designs, including home-based options for psychological care, with greater sample sizes and the potential to acknowledge patient heterogeneity.

An Investigation into the Relationship Between Choice of Model Structure and How to Adjust for Subsequent Therapies Using a Case Study in Oncology.

BACKGROUND: A common challenge in health technology assessments (HTAs) of cancer treatments is how subsequent therapy use within the trial follow-up may influence cost-effectiveness model outcomes. Although overall survival (OS) is often a key driver of model results, there are no guidelines to advise how to adjust for this potential confounding, with different approaches available dependent on the model structure. OBJECTIVE: We compared a partitioned survival analysis (PartSA) with a semi-Markov multi-state model (MSM) structure, with and without attempts to adjust for the impact of subsequent therapies on OS using a case study describing outcomes for people with relapsed/refractory multiple myeloma. METHODS: Both model structures included three health states: pre-progression, progressed disease and death. Three traditional crossover methods were considered within the context of the PartSA, whereas for the MSM, the probability of post-progression death was pooled across arms. Impacts on the model incremental cost-effectiveness ratio (ICER) were recorded. RESULTS: The unadjusted PartSA produced an ICER of £623,563, and after adjustment yielded an ICER range of £381,340-£386,907. The unadjusted MSM produced an ICER of £1,283,780. Adjusting OS in the MSM resulted in an ICER of £345,486. CONCLUSIONS: The simplicity of the PartSA is lost when the decision problem becomes more complex (for example, when OS data are confounded by subsequent therapies). In this setting, the MSM structure may be considered more flexible, with fewer and less restrictive assumptions required versus the PartSA. Researchers should consider important study design features that may influence the generalisability of data when undertaking model conceptualisation.

Discrete choice experiment to investigate preferences for psychological intervention in cardiac rehabilitation.

OBJECTIVE: Cardiac rehabilitation (CR) is offered to people who recently experienced a cardiac event, and often comprises of exercise, education and psychological care. This stated preference study aimed to investigate preferences for attributes of a psychological therapy intervention in CR. METHODS: A discrete choice experiment (DCE) was conducted and recruited a general population sample and a trial sample. DCE attributes included the modality (group or individual), healthcare professional providing care, information provided prior to therapy, location and the cost to the National Health Service (NHS). Participants were asked to choose between two hypothetical designs of therapy, with a separate opt-out included. A mixed logit model was used to analyse preferences. Cost to the NHS was used to estimate willingness to pay (WTP) for aspects of the intervention design. RESULTS: Three hundred and four participants completed the DCE (general public sample (n=262, mean age 47, 48% female) and trial sample (n=42, mean age 66, 45% female)). A preference for receiving psychological therapy was demonstrated by both samples (general population WTP £1081; 95% CI £957 to £1206). The general population appeared to favour individual therapy (WTP £213; 95% CI £160 to £266), delivered by a CR professional (WTP £48; 9% % CI £4 to £93) and with a lower cost (ß=-0.002; p<0.001). Participants preferred to avoid options where no information was received prior to starting therapy (WTP -£106; 95% CI -£153 to -£59). Results for the location attribute were variable and challenging to interpret. CONCLUSIONS: The study demonstrates a preference for psychological therapy as part of a programme of CR, as participants were more likely to opt-in to therapy. Results indicate that some aspects of the delivery which may be important to participants can be tailored to design a psychological therapy. Preference heterogeneity is an issue which may prevent a 'one-size-fits-all' approach to psychological therapy in CR.

Development of a cost of illness inventory questionnaire for children with autism spectrum disorder in South Asia.

BACKGROUND: The economic burden of autism is substantial and includes a range of costs, including healthcare, education, productivity losses, informal care and respite care, among others. In India, approximately, 2 million children aged 2-9 years have autism. Given the likely substantial burden of illness and the need to identify effective and cost-effective interventions, this research aimed to produce a comprehensive cost of illness inventory (COII) suitable for children with autism in South Asia (India) to support future research. METHODS: A structured and iterative design process was followed to create the COII, including literature reviews, interviews with caregivers, pilot testing and translation. Across the development of the COII, thirty-two families were involved in the design and piloting of the tool. The COII was forward translated (from English to Hindi) and back translated. Each stage of the process of development of the COII resulted in the further refinement of the tool. RESULTS: Domains covered in the final COII include education, childcare, relocation, healthcare contacts (outpatient, inpatient, medical emergencies, investigations and medication), religious retreats and rituals, specialist equipment, workshops and training, special diet, support and care, certification, occupational adjustments and government rebates/schemes. Administration and completion of the COII determined it to be feasible to complete in 35 minutes by qualified and trained researchers. The final COII is hosted by REDCap Cloud and is a bilingual instrument (Hindi and English). CONCLUSIONS: The COII was developed using experiences gathered from an iterative process in a metropolitan area within the context of one low- and middle-income country (LMIC) setting, India. Compared to COII tools used for children with autism in high-income country settings, additional domains were required, such as complimentary medication (e.g. religious retreats and homeopathy). The COII will allow future research to quantify the cost of illness of autism in India from a broad perspective and will support relevant economic evaluations. Understanding the process of developing the questionnaire will help researchers working in LMICs needing to adapt the current COII or developing similar questionnaires.

Evaluating Metacognitive Therapy to Improve Treatment of Anxiety and Depression in Cardiovascular Disease: The NIHR Funded PATHWAY Research Programme.

Background: Anxiety and depression contribute to poorer physical and mental health outcomes in cardiac patients. Psychological treatments are not routinely offered in cardiac care and have mixed and small effects. We conducted a series of studies under the PATHWAY research programme aimed at understanding and improving mental health outcomes for patients undergoing cardiac rehabilitation (CR) through provision of metacognitive therapy (MCT). Methods: PATHWAY was a series of feasibility trials, single-blind, multicenter, randomized controlled trials (RCTs), qualitative, stated preferences for therapy and health economics studies. Findings: Patients felt their psychological needs were not met in CR and their narratives of distress could be parsimoniously explained by the metacognitive model. Patients reported they would prefer therapy over no therapy as part of CR, which included delivery by a cardiac professional. Two feasibility studies demonstrated that RCTs of group-based and self-help MCT were acceptable, could be embedded in CR services, and that RCTs of these interventions were feasible. A definitive RCT of group-MCT within CR (n = 332) demonstrated significantly greater reductions in the severity of anxiety and depression, exceeding CR alone, with gains maintained at 12 month follow-up (SMD HADS total score = 0.52 at 4 months and 0.33 at 12 months). A definitive trial of self-help MCT is ongoing. Conclusion: There is a need to better meet the psychological needs of CR patients. Embedding MCT into CR demonstrated high acceptability and improved efficacy on psychological outcomes. Results support roll-out of MCT in CR with evaluation of national implementation. Registration: URL: NCT02420431; ISRCTN74643496; NCT03129282.

A review of economic evaluations of health care for people at risk of psychosis and for first-episode psychosis.

BACKGROUND: Preventing psychotic disorders and effective treatment in first-episode psychosis are key priorities for the National Institute for Health and Care Excellence. This review assessed the evidence base for the cost-effectiveness of health and social care interventions for people at risk of psychosis and for first-episode psychosis. METHODS: Electronic searches were conducted using the PsycINFO, MEDLINE and Embase databases to identify relevant published full economic evaluations published before August 2020. Full-text English-language studies reporting a full economic evaluation of a health or social care intervention aiming to reduce or prevent symptoms in people at risk of psychosis or experiencing first-episode psychosis were included. Screening, data extraction, and critical appraisal were performed using pre-specified criteria and forms based on the NHS Economic Evaluation Database (EED) handbook and Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist for economic evaluations. The protocol was registered on the PROSPERO database (CRD42018108226). Results were summarised qualitatively. RESULTS: Searching identified 1,628 citations (1,326 following the removal of duplications). After two stages of screening 14 studies met the inclusion criteria and were included in the review. Interventions were varied and included multidisciplinary care, antipsychotic medication, psychological therapy, and assertive outreach. Evidence was limited in the at-risk group with only four identified studies, though all interventions were found to be cost-effective with a high probability (> 80%). A more substantial evidence base was identified for first-episode psychosis (11 studies), with a focus on early intervention (7/11 studies) which again had positive conclusions though with greater uncertainty. CONCLUSIONS: Study findings generally concluded interventions were cost-effective. The evidence for the population who are at-risk of psychosis was limited, and though there were more studies for the population with first-episode psychosis, limitations of the evidence base (including generalisability and heterogeneity across the methods used) affect the certainty of conclusions.

Acknowledging Patient Heterogeneity in Economic Evaluations in Schizophrenia: A Systematic Review.

OBJECTIVES: Schizophrenia is a severe mental illness with heterogeneous etiology, range of symptoms, and course of illness. Cost-effectiveness analysis often applies averages from populations, which disregards patient heterogeneity even though there are a range of methods available to acknowledge patient heterogeneity. This review evaluates existing economic evaluations of interventions in schizophrenia to understand how patient heterogeneity is currently reflected in economic evaluation. METHODS: Electronic searches of MEDLINE, Embase, and PsycINFO via Ovid and the Health Technology Assessment database were run to identify full economic evaluations of interventions aiming to reduce the symptoms associated with schizophrenia. Two levels of screening were used, and explicit inclusion criteria were applied. Prespecified data extraction and critical appraisal were performed. RESULTS: Seventy-six relevant studies were identified. More than half (41 of 76) of the articles acknowledged patient heterogeneity in some way through discussion or methods. There was a range of patient characteristics considered, including demographics and socioeconomic factors (eg, age, educational level, ethnicity), clinical characteristics (eg, symptom severity, comorbidities), and preferences (eg, preferences related to outcomes or symptoms). Subgroup analyses were rarely reported (8 of 76). CONCLUSIONS: Patient heterogeneity was frequently mentioned in studies but was rarely thoroughly investigated in the identified economic evaluations. When investigated, included patient characteristics and methods were found to be heterogeneous. Understanding and acknowledging patient heterogeneity may alter the conclusions of cost-effectiveness evaluations; subsequently, we would encourage further research in this area.

Out of Date or Best Before? A Commentary on the Relevance of Economic Evaluations Over Time.

The impact of time on the applicability and relevance of historical economic evaluations can be considerable. Ignoring this may lead to the use of weak or invalid evidence to inform important research questions or resource allocation decisions, as historical economic evaluations may have reached different conclusions compared to if a similar study had been conducted more recently. There are multiple factors that contribute towards evidence becoming outdated including changes to the relevant decision problem (e.g. comparators), changes to parameters (such as costs, utilities and resource use) and methodological updates (e.g. recommendations on uncertainty analysis). Researchers reviewing economic evaluations need to consider whether changes over time would influence the study design and results if the evaluation were repeated, to the extent that it is no longer helpful or informative. In this paper, we summarise these key issues and make recommendations about how and whether researchers can future proof their economic evaluations.

Factors Limiting Subgroup Analysis in Cost-Effectiveness Analysis and a Call for Transparency.

The use of population averages in cost-effectiveness analysis may hide important differences across subgroups, potentially resulting in suboptimal resource allocation, reduced population health and/or increased health inequalities. We discuss the factors that limit subgroup analysis in cost-effectiveness analysis and propose more thorough and transparent reporting. There are many issues that may limit whether subgroup analysis can be robustly included in cost-effectiveness analysis, including challenges with prespecifying and justifying subgroup analysis, identifying subgroups that can be implemented (identified and targeted) in practice, resource and data requirements, and statistical and ethical concerns. These affect every stage of the design, development and reporting of cost-effectiveness analyses. It may not always be possible to include and report relevant subgroups in cost effectiveness, e.g. due to data limitations. Reasons for not conducting subgroup analysis may be heterogeneous, and the consequences of not acknowledging patient heterogeneity can be substantial. We recommend that when potentially relevant subgroups have not been included in a cost-effectiveness analysis, authors report this and discuss their rationale and the limitations of this. Greater transparency of subgroup reporting should provide a starting point to overcoming these challenges in future research.

Self-Reported Health in Patients on or at Risk of Sick Leave Due to Depression and Anxiety: Validity of the EQ-5D.

Objectives: The EQ-5D is a generic, self-report measure of health that is increasingly used in clinical settings, including mental health. The EQ-5D captures health using five dimensions: Mobility, Self-care, Usual activities, Pain/discomfort, and Anxiety/Depression. The validity of the EQ-5D is previously unexplored in patients on or at risk of sick leave due to depression and anxiety. The study's aim was to examine its validity in this group of patients. Methods: Baseline data were collected from self-report questionnaires in an observational study (N=890) at a Norwegian outpatient-clinic. Participants were adults on or at risk of sick leave due to depression and anxiety who were referred for treatment by general practitioners. The crosswalk methodology was applied to estimate the EQ-5D value. Validity was assessed by comparing responses on the EQ-5D with the Beck Depression Inventory-II (BDI-II), the Beck Anxiety Inventory (BAI), and Subjective Health Complaints (SHC). An ordinal regression model was used to assess known-groups validity. Convergent validity was assessed using Pearson's correlation coefficient, and a multivariate regression model that included sociodemographic characteristics. Results: The mean EQ-5D value was 0.631, indicating reduced health status compared to "full health" anchored at 1.0, and patients reported moderate levels of depression and anxiety. Ordinal regression indicated that the EQ-5D could discriminate between different levels of symptom severity for depression and anxiety. The EQ-5D value showed significant correlation with the clinical measures; r=-0.52 for the BDI-II, r=-0.49 for the BAI, and r=-0.44 for SHC. The multivariate regression showed that the clinical variables significantly predicted the EQ-5D value, explaining 40.1% of the variance. Depression and anxiety scores were the largest determinants of EQ-5D value, respectively, whilst sick leave, subjective health complaints, and gender made moderate contributions. Conclusion: The EQ-5D showed indication of validity in patients on or at risk of sick leave due to depression and anxiety in the present study. The EQ-5D value was sensitive to both symptom severity and functional impairment in the form of sick leave. The findings support the EQ-5D as a feasible and relevant measure of health status in these patients.

Utilising Patient and Public Involvement in Stated Preference Research in Health: Learning from the Existing Literature and a Case Study.

Publications reporting discrete choice experiments of healthcare interventions rarely discuss whether patient and public involvement (PPI) activities have been conducted. This paper presents examples from the existing literature and a detailed case study from the National Institute for Health Research-funded PATHWAY programme that comprehensively included PPI activities at multiple stages of preference research. Reflecting on these examples, as well as the wider PPI literature, we describe the different stages at which it is possible to effectively incorporate PPI across preference research, including the design, recruitment and dissemination of projects. Benefits of PPI activities include gaining practical insights from a wider perspective, which can positively impact experiment design as well as survey materials. Further benefits included advice around recruitment and reaching a greater audience with dissemination activities, amongst others. There are challenges associated with PPI activities; examples include time, cost and outlining expectations. Overall, although we acknowledge practical difficulties associated with PPI, this work highlights that it is possible for preference researchers to implement PPI across preference research. Further research systematically comparing methods related to PPI in preference research and their associated impact on the methods and results of studies would strengthen the literature.

Challenges in synthesising cost-effectiveness estimates.

Economic evaluations help decision-makers faced with tough decisions on how to allocate resources. Systematic reviews of economic evaluations are useful as they allow readers to assess whether interventions have been demonstrated to be cost effective, the uncertainty in the evidence base, and key limitations or gaps in the evidence base. The synthesis of systematic reviews of economic evaluations commonly takes a narrative approach whereas a meta-analysis is common step for reviews of clinical evidence (e.g. effectiveness or adverse event outcomes). As they are common objectives in other reviews, readers may query why a synthesis has not been attempted for economic outcomes. However, a meta-analysis of incremental cost-effectiveness ratios, costs, or health benefits (including quality-adjusted life years) is fraught with issues largely due to heterogeneity across study designs and methods and further practical challenges. Therefore, meta-analysis is rarely feasible or robust. This commentary outlines these issues, supported by examples from the literature, to support researchers and reviewers considering systematic review of economic evidence.

Protocol for the economic evaluation of metacognitive therapy for cardiac rehabilitation participants with symptoms of anxiety and/or depression.

INTRODUCTION: Cardiac rehabilitation (CR) is offered to reduce the risk of further cardiac events and to improve patients' health and quality of life following a cardiac event. Psychological care is a common component of CR as symptoms of depression and/or anxiety are more prevalent in this population, however evidence for the cost-effectiveness of current interventions is limited. Metacognitive therapy (MCT), is a recent treatment development that is effective in treating anxiety and depression in mental health settings and is being evaluated in CR patients. This protocol describes the planned approach to the economic evaluation of MCT for CR patients. METHODS AND ANALYSIS: The economic evaluation work will consist of a within-trial analysis and an economic model. The PATHWAY Group MCT study has been prospectively designed to collect comprehensive self-reported resource use and health outcome data, including the EQ-5D, within a randomised controlled trial study design (UK Clinical Trials Gateway). A within-trial economic evaluation and economic model will compare the cost-effectiveness of MCT plus usual care (UC) to UC, from a health and social care perspective in the UK. The within-trial analysis will use intention-to-treat and estimate total costs and quality-adjusted life-years (QALYs) for the trial follow-up. Single imputation will be used to impute missing baseline variables. Multiple imputation will be used to impute values missing at follow-up. Items of resource use will be multiplied by published national healthcare costs. Regression analysis will be used to estimate net costs and net QALYs and these estimates will be bootstrapped to generate 10 000 net pairs of costs and QALYs to inform the probability of cost-effectiveness. A decision analytical economic model will be developed to synthesise trial data with the published literature over a longer time frame. Sensitivity analysis will explore uncertainty. Guidance of the methods for economic models will be followed and dissemination will adhere to reporting guidelines. ETHICS AND DISSEMINATION: The economic evaluation includes a within-trial analysis. The trial which included the collection of this data was reviewed and approved by Ethics. Ethics approval was obtained by the Preston Research Ethics Committee (project ID 156862). The modelling analysis is not applicable for Ethics as it will use data from the trial (secondary analysis) and the published literature. Results of the main trial and economic evaluation will be published in the peer-reviewed National Institute for Health Research (NIHR) journals library (Programme Grants for Applied Research), submitted to a peer-reviewed journal and presented at appropriate conferences. TRIAL REGISTRATION NUMBER: ISRCTN74643496; Pre-results.

Real-world evidence use in assessments of cancer drugs by NICE.

OBJECTIVE: To establish how real-world evidence (RWE) has been used to inform single technology appraisals (STAs) of cancer drugs conducted by the National Institute for Health and Care Excellence (NICE). METHODS: STAs published by NICE from April 2011 to October 2018 that evaluated cancer treatments were reviewed. Information regarding the use of RWE to directly inform the company-submitted cost-effectiveness analysis was extracted and categorized by topic. Summary statistics were used to describe emergent themes, and a narrative summary was provided for key case studies. RESULTS: Materials for a total of 113 relevant STAs were identified and analyzed, of which nearly all (96 percent) included some form of RWE within the company-submitted cost-effectiveness analysis. The most common categories of RWE use concerned the health-related quality of life of patients (71 percent), costs (46 percent), and medical resource utilization (40 percent). While sources of RWE were routinely criticized as part of the appraisal process, we identified only two cases where the use of RWE was overtly rejected; hence, in the majority of cases, RWE was accepted in cancer drug submissions to NICE. DISCUSSION: RWE has been used extensively in cancer submissions to NICE. Key criticisms of RWE in submissions to NICE are seldom regarding the use of RWE in general; instead, these are typically concerned with specific data sources and the applicability of these to the decision problem. Within an appropriate context, RWE constitutes an extremely valuable source of information to inform decision making; yet the development of best practice guidelines may improve current reporting standards.

A comparison of partitioned survival analysis and state transition multi-state modelling approaches using a case study in oncology.

AIMS: To construct and compare a partitioned-survival analysis (PartSA) and a semi-Markov multi-state model (MSM) to investigate differences in estimated cost effectiveness of a novel cancer treatment from a UK perspective. MATERIALS AND METHODS: Data from a cohort of late-stage cancer patients (N > 700) enrolled within a randomized, controlled trial were used to populate both modelling approaches. The statistical software R was used to fit parametric survival models to overall survival (OS) and progression-free survival (PFS) data to inform the PartSA (package "flexsurv"). The package "mstate" was used to estimate the MSM transitions (permitted transitions: (T1) "progression-free" to "dead", (T2) "post-progression" to "death", and (T3) "pre-progression" to "post-progression"). Key costs included were treatment-related (initial, subsequent, and concomitant), adverse events, hospitalizations and monitoring. Utilities were stratified by progression. Outcomes were discounted at 3.5% per annum over a 15-year time horizon. RESULTS: The PartSA and MSM approaches estimated incremental cost-effectiveness ratios (ICERs) of £342,474 and £411,574, respectively. Scenario analyses exploring alternative parametric forms provided incremental discounted life-year estimates that ranged from +0.15 to +0.33 for the PartSA approach, compared with -0.13 to +0.23 for the MSM approach. This variation was reflected in the range of ICERs. The PartSA produced ICERs between £234,829 and £522,963, whereas MSM results were more variable and included instances where the intervention was dominated and ICERs above £7 million (caused by very small incremental QALYs). LIMITATIONS AND CONCLUSIONS: Structural uncertainty in economic modelling is rarely explored due to time and resource limitations. This comparison of structural approaches indicates that the choice of structure may have a profound impact on cost-effectiveness results. This highlights the importance of carefully considered model conceptualization, and the need for further research to ascertain when it may be most appropriate to use each approach.