A structured process for the validation of a decision-analytic model: application to a cost-effectiveness model for risk-stratified national breast screening.

BACKGROUND: Decision-makers require knowledge of the strengths and weaknesses of decision-analytic models used to evaluate healthcare interventions to be able to confidently use the results of such models to inform policy. A number of aspects of model validity have previously been described, but no systematic approach to assessing the validity of a model has been proposed. This study aimed to consolidate the different aspects of model validity into a step-by-step approach to assessing the strengths and weaknesses of a decision-analytic model. METHODS: A pre-defined set of steps were used to conduct the validation process of an exemplar early decision-analytic-model-based cost-effectiveness analysis of a risk-stratified national breast cancer screening programme [UK healthcare perspective; lifetime horizon; costs (£; 2021)]. Internal validation was assessed in terms of descriptive validity, technical validity and face validity. External validation was assessed in terms of operational validation, convergent validity (or corroboration) and predictive validity. RESULTS: The results outline the findings of each step of internal and external validation of the early decision-analytic-model and present the validated model (called 'MANC-RISK-SCREEN'). The positive aspects in terms of meeting internal validation requirements are shown together with the remaining limitations of MANC-RISK-SCREEN. CONCLUSION: Following a transparent and structured validation process, MANC-RISK-SCREEN has been shown to have satisfactory internal and external validity for use in informing resource allocation decision-making. We suggest that MANC-RISK-SCREEN can be used to assess the cost-effectiveness of exemplars of risk-stratified national breast cancer screening programmes (NBSP) from the UK perspective. IMPLICATIONS: A step-by-step process for conducting the validation of a decision-analytic model was developed for future use by health economists. Using this approach may help researchers to fully demonstrate the strengths and limitations of their model to decision-makers.

Trade-offs between overall survival and side effects in the treatment of metastatic breast cancer: eliciting preferences of patients with primary and metastatic breast cancer using a discrete choice experiment.

OBJECTIVES: There has been a recent proliferation in treatment options for patients with metastatic breast cancer. Such treatments often involve trade-offs between overall survival and side effects. Our study aims to estimate the trade-offs that could be used to inform decision-making at the individual and policy level. DESIGN: We designed a discrete choice experiment (DCE) to look at preferences for avoiding severity levels of side effects when choosing treatment for metastatic breast cancer. Treatment attributes were: fatigue, nausea, diarrhoea, other side effects (peripheral neuropathy, hand-foot syndrome and mucositis) and urgent hospital admission and overall survival. Responses were analysed using an error component logit model. We estimated the relative importance of attributes and minimum acceptable survival for improvements in side effects. SETTING: The DCE was completed online by UK residents with self-reported diagnoses of breast cancer. PARTICIPANTS: 105 respondents participated, of which 72 patients had metastatic breast cancer and 33 patients had primary breast cancer. RESULTS: Overall survival had the largest relative importance, followed by other side effects, diarrhoea, nausea and fatigue. The risk of urgent hospital admission was not significant. While overall survival was the most important attribute, respondents were willing to forgo some absolute probability of overall survival for reductions in all Grade 2 side effects (12.02% for hand-foot syndrome, 11.01% for mucositis, 10.42% for peripheral neuropathy, 6.33% for diarrhoea and 3.62% for nausea). Grade 1 side effects were not significant, suggesting respondents have a general tolerance for them. CONCLUSIONS: Patients are willing to forgo overall survival to avoid particular severity levels of side effects. Our results have implications for data collected in research studies and can help inform person-centred care and shared decision-making.

Global serum profiling: an opportunity for earlier cancer detection.

The advances in cancer research achieved in the last 50 years have been remarkable and have provided a deeper knowledge of this disease in many of its conceptual and biochemical aspects. From viewing a tumor as a 'simple' aggregate of mutant cells and focusing on detecting key cell changes leading to the tumorigenesis, the understanding of cancer has broadened to consider it as a complex organ interacting with its close and far surroundings through tumor and non-tumor cells, metabolic mechanisms, and immune processes. Metabolism and the immune system have been linked to tumorigenesis and malignancy progression along with cancer-specific genetic mutations. However, most technologies developed to overcome the barriers to earlier detection are focused solely on genetic information. The concept of cancer as a complex organ has led to research on other analytical techniques, with the quest of finding a more sensitive and cost-effective comprehensive approach. Furthermore, artificial intelligence has gained broader consensus in the oncology community as a powerful tool with the potential to revolutionize cancer diagnosis for physicians. We herein explore the relevance of the concept of cancer as a complex organ interacting with the bodily surroundings, and focus on promising emerging technologies seeking to diagnose cancer earlier, such as liquid biopsies. We highlight the importance of a comprehensive approach to encompass all the tumor and non-tumor derived information salient to earlier cancer detection.

Variation in hospital cost trajectories at the end of life by age, multimorbidity and cancer type.

Background: Approximately thirty thousand people in Scotland are diagnosed with cancer annually, of whom a third live less than one year. The timing, nature and value of hospital-based healthcare for patients with advanced cancer are not well understood. The study's aim was to describe the timing and nature of hospital-based healthcare use and associated costs in the last year of life for patients with a cancer diagnosis. Methods: We undertook a Scottish population-wide administrative data linkage study of hospital-based healthcare use for individuals with a cancer diagnosis, who died aged 60 and over between 2012 and 2017. Hospital admissions and length of stay (LOS), as well as the number and nature of outpatient and day case appointments were analysed. Generalised linear models were used to adjust costs for age, gender, socioeconomic deprivation status, rural-urban (RU) status and comorbidity. Results: The study included 85,732 decedents with a cancer diagnosis. For 64,553 (75.3%) of them, cancer was the primary cause of death. Mean age at death was 80.01 (SD 8.15) years. The mean number of inpatient stays in the last year of life was 5.88 (SD 5.68), with a mean LOS of 7 days. Admission rates rose sharply in the last month of life. One year adjusted and unadjusted costs decreased with increasing age. A higher comorbidity burden was associated with higher costs. Major cost differences were present between cancer types. Conclusions: People in Scotland in their last year of life with cancer are high users of secondary care. Hospitalisation accounts for a high proportion of costs, particularly in the last month of life. Further research is needed to examine triggers for hospitalisations and to identify influenceable reasons for unwarranted variation in hospital use among different cancer cohorts.

Clinical validation of a spectroscopic liquid biopsy for earlier detection of brain cancer.

Background: Diagnostic delays impact the quality of life and survival of patients with brain tumors. Earlier and expeditious diagnoses in these patients are crucial to reduce the morbidities and mortalities associated with brain tumors. A simple, rapid blood test that can be administered easily in a primary care setting to efficiently identify symptomatic patients who are most likely to have a brain tumor would enable quicker referral to brain imaging for those who need it most. Methods: Blood serum samples from 603 patients were prospectively collected and analyzed. Patients either had non-specific symptoms that could be indicative of a brain tumor on presentation to the Emergency Department, or a new brain tumor diagnosis and referral to the neurosurgical unit, NHS Lothian, Scotland. Patient blood serum samples were analyzed using the Dxcover® Brain Cancer liquid biopsy. This technology utilizes infrared spectroscopy combined with a diagnostic algorithm to predict the presence of intracranial disease. Results: Our liquid biopsy approach reported an area under the receiver operating characteristic curve of 0.8. The sensitivity-tuned model achieves a 96% sensitivity with 45% specificity (NPV 99.3%) and identified 100% of glioblastoma multiforme patients. When tuned for a higher specificity, the model yields a sensitivity of 47% with 90% specificity (PPV 28.4%). Conclusions: This simple, non-invasive blood test facilitates the triage and radiographic diagnosis of brain tumor patients while providing reassurance to healthy patients. Minimizing time to diagnosis would facilitate the identification of brain tumor patients at an earlier stage, enabling more effective, less morbid surgical and adjuvant care.

Feasibility study for interactive reporting of network meta-analysis: experiences from the development of the MetaInsight COVID-19 app for stakeholder exploration, re-analysis and sensitivity analysis from living systematic reviews.

BACKGROUND: Network meta-analysis (NMA) has been increasingly adopted worldwide by Cochrane reviews, guideline developers and decision-making bodies to identify optimal treatment choices. However, NMA results are often produced statically, not allowing stakeholders to 'dig deeper' and interrogate with their own judgement. Additionally, amid the COVID-19 pandemic, unnecessary or duplicated reviews have been proposed which analyse from the same pool of evidence. We developed the 'MetaInsight COVID-19' app as a prototype for an interactive platform to eliminate such duplicated efforts, by empowering users to freely analyse the data and improve scientific transparency. METHODS: MetaInsight COVID-19 ( https://crsu.shinyapps.io/metainsightcovid/ ) was developed to conduct NMA with the evolving evidence on treatments for COVID-19. It was updated weekly between 19th May - 19th Oct 2020, incorporating new evidence identified from a living systematic review. RESULTS: The app includes embedded functions to facilitate study selection based on study characteristics, and displays the synthesised results in real time. It allows both frequentist and Bayesian NMA to be conducted as well as consistency and heterogeneity assessments. A demonstration of the app is provided and experiences of building such a platform are discussed. CONCLUSIONS: MetaInsight COVID-19 allows users to take control of the evidence synthesis using the analytic approach they deem appropriate to ascertain how robust findings are to alternative analysis strategies and study inclusion criteria. It is hoped that this app will help avoid many of the duplicated efforts when reviewing and synthesising the COVID-19 evidence, and, in addition, establish the desirability of an open platform format such as this for interactive data interrogation, visualisation, and reporting for any traditional or 'living' NMA.

From Spreadsheets to Script: Experiences From Converting a Scottish Cardiovascular Disease Policy Model into R.

Given the advantages in transparency, reproducibility, adaptability and computational efficiency in R, there is a growing interest in converting existing spreadsheet-based models into an R script for model re-use and upskilling training among health economic modellers. The objective of this exercise was to convert the Scottish Cardiovascular Disease (CVD) Policy Model from Excel to R and discuss the lessons learnt throughout this process. The CVD model is a competing risk state transition cohort model. Four health economists, with varied experience of R, attempted to replicate an identical model structure in R based on the model in Excel and reproduce the intermediate and final results. Replications varied in their use of specialist health economics packages in addition to standard data management packages. Two versions of the CVD model were created in R along with a Shiny app. Version 1 was developed without health economics specialist packages and produced identical results to the Excel version. Version 2 used the heemod package and did not achieve the same results, possibly due to the non-standard elements of the model and limited time to adapt the functions. The R model requires less than half the computational time than the Excel model. Conversion of the spreadsheet models to script models is feasible for health economists. A step-by-step guide for the conversion process is provided and modellers' experience is discussed. Coding without specialist packages allows full flexibility, while specialist packages may add convenience if the model structure is suitable. Whichever approach is taken, transparency and replicability remain the key criteria in model programming. Model conversions must maintain standards in these areas regardless of the choice of software.

The impact of the Covid-19 pandemic on breast cancer early detection and screening.

The COVID-19 pandemic affects mortality and morbidity, with disruptions expected to continue for some time, with access to timely cancer-related services a concern. For breast cancer, early detection and treatment is key to improved survival and longer-term quality of life. Health services generally have been strained and in many settings with population breast mammography screening, efforts to diagnose and treat breast cancers earlier have been paused or have had reduced capacity. The resulting delays to diagnosis and treatment may lead to more intensive treatment requirements and, potentially, increased mortality. Modelled evaluations can support responses to the pandemic by estimating short- and long-term outcomes for various scenarios. Multiple calibrated and validated models exist for breast cancer screening, and some have been applied in 2020 to estimate the impact of breast screening disruptions and compare options for recovery, in a range of international settings. On behalf of the Covid and Cancer Modelling Consortium (CCGMC) Working Group 2 (Breast Cancer), we summarize and provide examples of such in a range of settings internationally, and propose priorities for future modelling exercises. International expert collaborations from the CCGMC Working Group 2 (Breast Cancer) will conduct analyses and modelling studies needed to inform key stakeholders recovery efforts in order to mitigate the impact of the pandemic on early diagnosis and treatment of breast cancer.

Content of Health Economics Analysis Plans (HEAPs) for Trial-Based Economic Evaluations: Expert Delphi Consensus Survey.

OBJECTIVES: Health economics analysis plans (HEAPs) currently lack consistency, with uncertainty surrounding appropriate content. We aimed to develop a list of essential items that should be included in HEAPs for economic evaluations conducted alongside randomized trials. METHODS: A list of potential items for inclusion was developed by examining existing HEAPs. An electronic Delphi survey was conducted among professional health economists. Respondents were asked to rate potential items from 1 (least important) to 9 (most important), suggest additional items, and comment on proposed items (round 1). A second survey (round 2) was emailed to participants, including the participant's own scores from round 1 along with summary results from the whole panel; participants were asked to rerate each item. Consensus criteria for inclusion in the final list were predefined as >70% of participants rating an item 7-9 and <15% rating it 1-3 after round 2. A final item selection meeting was held to scrutinize the results and adjudicate on items lacking consensus. RESULTS: 62 participants completed round 1 of the survey. The initial list included 72 potential items; all 72 were carried forward to round 2, and no new items were added. 48 round 1 respondents (77.4%) completed round 2 and reached consensus on 53 items. At the final meeting, the expert panel (n = 9) agreed that 58 items should be included in the essential list, moved 9 items to an optional list, and dropped 5 items. CONCLUSIONS: Via expert consensus opinion, this study identified 58 items that are considered essential in a HEAP.

Early economic evaluation to guide the development of a spectroscopic liquid biopsy for the detection of brain cancer.

OBJECTIVES: An early economic evaluation to inform the translation into clinical practice of a spectroscopic liquid biopsy for the detection of brain cancer. Two specific aims are (1) to update an existing economic model with results from a prospective study of diagnostic accuracy and (2) to explore the potential of brain tumor-type predictions to affect patient outcomes and healthcare costs. METHODS: A cost-effectiveness analysis from a UK NHS perspective of the use of spectroscopic liquid biopsy in primary and secondary care settings, as well as a cost-consequence analysis of the addition of tumor-type predictions was conducted. Decision tree models were constructed to represent simplified diagnostic pathways. Test diagnostic accuracy parameters were based on a prospective validation study. Four price points (GBP 50-200, EUR 57-228) for the test were considered. RESULTS: In both settings, the use of liquid biopsy produced QALY gains. In primary care, at test costs below GBP 100 (EUR 114), testing was cost saving. At GBP 100 (EUR 114) per test, the ICER was GBP 13,279 (EUR 15,145), whereas at GBP 200 (EUR 228), the ICER was GBP 78,300 (EUR 89,301). In secondary care, the ICER ranged from GBP 11,360 (EUR 12,956) to GBP 43,870 (EUR 50,034) across the range of test costs. CONCLUSIONS: The results demonstrate the potential for the technology to be cost-effective in both primary and secondary care settings. Additional studies of test use in routine primary care practice are needed to resolve the remaining issues of uncertainty-prevalence in this patient population and referral behavior.

Healthcare use and costs in the last year of life: a national population data linkage study.

BACKGROUND: People who are nearing the end of life are high users of healthcare. The cost to providers is high and the value of care is uncertain. OBJECTIVES: To describe the pattern, trajectory and drivers of secondary care use and cost by people in Scotland in their last year of life. METHODS: Retrospective whole-population secondary care administrative data linkage study of Scottish decedents of 60 years and over between 2012 and 2017 (N=274 048). RESULTS: Secondary care use was high in the last year of life with a sharp rise in inpatient admissions in the last 3 months. The mean cost was £10 000. Cause of death was associated with differing patterns of healthcare use: dying of cancer was preceded by the greatest number of hospital admissions and dementia the least. Greater age was associated with lower admission rates and cost. There was higher resource use in the urban areas. No difference was observed by deprivation. CONCLUSIONS: Hospitalisation near the end of life was least frequent for older people and those living rurally, although length of stay for both groups, when they were admitted, was longer. Research is required to understand if variation in hospitalisation is due to variation in the quantity or quality of end-of-life care available, varying community support, patient preferences or an inevitable consequence of disease-specific needs.

Long-term outcome of partial P450 side-chain cleavage enzyme deficiency in three brothers: the importance of early diagnosis.

OBJECTIVE: CYP11A1 mutations cause P450 side-chain cleavage (scc) deficiency, a rare form of congenital adrenal hyperplasia with a wide clinical spectrum. We detail the phenotype and evolution in a male sibship identified by HaloPlex targeted capture array. FAMILY STUDY: The youngest of three brothers from a non-consanguineous Scottish family presented with hyperpigmentation at 3.7 years. Investigation showed grossly impaired glucocorticoid function with ACTH elevation, moderately impaired mineralocorticoid function, and normal external genitalia. The older brothers were found to be pigmented also, with glucocorticoid impairment but normal electrolytes. Linkage studies in 2002 showed that all three brothers had inherited the same critical regions of the maternal X chromosome suggesting an X-linked disorder, but analysis of NR0B1 (DAX-1, adrenal hypoplasia) and ABCD1 (adrenoleukodystrophy) were negative. In 2016, next-generation sequencing revealed compound heterozygosity for the rs6161 variant in CYP11A1 (c.940G>A, p.Glu314Lys), together with a severely disruptive frameshift mutation (c.790_802del, K264Lfs*5). The brothers were stable on hydrocortisone and fludrocortisone replacement, testicular volumes (15-20 mL), and serum testosterone levels (24.7, 33.3, and 27.2 nmol/L) were normal, but FSH (41.2 µ/L) was elevated in the proband. The latter had undergone left orchidectomy for suspected malignancy at the age of 25 years and was attending a fertility clinic for oligospermia. Initial histology was reported as showing nodular Leydig cell hyperplasia. However, histological review using CD56 staining confirmed testicular adrenal rest cell tumour (TART). CONCLUSION: This kinship with partial P450scc deficiency demonstrates the importance of precise diagnosis in primary adrenal insufficiency to ensure appropriate counselling and management, particularly of TART.

Barriers and Facilitators to Model Replication Within Health Economics.

BACKGROUND: Model replication is important because it enables researchers to check research integrity and transparency and, potentially, to inform the model conceptualization process when developing a new or updated model. OBJECTIVE: The aim of this study was to evaluate the replicability of published decision analytic models and to identify the barriers and facilitators to replication. METHODS: Replication attempts of 5 published economic modeling studies were made. The replications were conducted using only publicly available information within the manuscripts and supplementary materials. The replicator attempted to reproduce the key results detailed in the paper, for example, the total cost, total outcomes, and if applicable, incremental cost-effectiveness ratio reported. Although a replication attempt was not explicitly defined as a success or failure, the replicated results were compared for percentage difference to the original results. RESULTS: In conducting the replication attempts, common barriers and facilitators emerged. For most case studies, the replicator needed to make additional assumptions when recreating the model. This was often exacerbated by conflicting information being presented in the text and the tables. Across the case studies, the variation between original and replicated results ranged from -4.54% to 108.00% for costs and -3.81% to 0.40% for outcomes. CONCLUSION: This study demonstrates that although models may appear to be comprehensively reported, it is often not enough to facilitate a precise replication. Further work is needed to understand how to improve model transparency and in turn increase the chances of replication, thus ensuring future usability.

Real-world evidence was feasible for estimating effectiveness of chemotherapy in breast cancer: a cohort study.

OBJECTIVE: Evidence-based guidelines recommend adjuvant chemotherapy in early stage breast cancer whenever treatment benefit is considered sufficient to outweigh the associated risks. However, many groups of patients were either excluded from or underrepresented in the clinical trials that form the evidence base for this recommendation. This study aims to determine whether using administrative health care data-real world data-and econometric methods for causal analysis to provide "real world evidence" (RWE) are feasible methods for addressing this gap. METHODS: Cases of primary breast cancer in women from 2001 to 2015 were extracted from the Scottish cancer registry (SMR06) and linked to other routine health records (inpatient and outpatient visits). Four methods were used to estimate the effect of adjuvant chemotherapy on disease-specific and overall mortality: (1) regression with adjustment for covariates, (2) propensity score matching, (3) instrumental variables analysis, and (4) regression discontinuity design. Hazard ratios for breast cancer mortality and all-cause mortality were compared to those from a meta-analysis of randomized trials. RESULTS: A total of 39,805 cases were included in the analyses. Regression adjustment, propensity score matching, and instrumental variables were feasible, whereas regression discontinuity was not. Effectiveness estimates were similar between RWE and randomized trials for breast cancer mortality but not for all-cause mortality. CONCLUSIONS: RWE methods are a feasible means to generate estimates of effectiveness of adjuvant chemotherapy in early stage breast cancer. However, such estimates must be interpreted in the context of the available randomized evidence and the potential biases of the observational methods.

Chemotherapy effectiveness in trial-underrepresented groups with early breast cancer: A retrospective cohort study.

BACKGROUND: Adjuvant chemotherapy in early stage breast cancer has been shown to reduce mortality in a large meta-analysis of over 100 randomised trials. However, these trials largely excluded patients aged 70 years and over or with higher levels of comorbidity. There is therefore uncertainty about whether the effectiveness of adjuvant chemotherapy generalises to these groups, hindering patient and clinician decision-making. This study utilises administrative healthcare data-real world data (RWD)-and econometric methods for causal analysis to estimate treatment effectiveness in these trial-underrepresented groups. METHODS AND FINDINGS: Women with early breast cancer aged 70 years and over and those under 70 years with a high level of comorbidity were identified and their records extracted from Scottish Cancer Registry (2001-2015) data linked to other routine health records. A high level of comorbidity was defined as scoring 1 or more on the Charlson comorbidity index, being in the top decile of inpatient stays, and/or having 5 or more visits to specific outpatient clinics, all within the 5 years preceding breast cancer diagnosis. Propensity score matching (PSM) and instrumental variable (IV) analysis, previously identified as feasible and valid in this setting, were used in conjunction with Cox regression to estimate hazard ratios for death from breast cancer and death from all causes. The analysis adjusts for age, clinical prognostic factors, and socioeconomic deprivation; the IV method may also adjust for unmeasured confounding factors. Cohorts of 9,653 and 7,965 were identified for women aged 70 years and over and those with high comorbidity, respectively. In the ≥70/high comorbidity cohorts, median follow-up was 5.17/6.53 years and there were 1,935/740 deaths from breast cancer. For women aged 70 years and over, the PSM-estimated HR was 0.73 (95% CI 0.64-0.95), while for women with high comorbidity it was 0.67 (95% CI 0.51-0.86). This translates to a mean predicted benefit in terms of overall survival at 10 years of approximately3% (percentage points) and 4%, respectively. A limitation of this analysis is that use of observational data means uncertainty remains both from sampling uncertainty and from potential bias from residual confounding. CONCLUSIONS: The results of this study, as RWD, should be interpreted with caution and in the context of existing and emerging randomised data. The relative effectiveness of adjuvant chemotherapy in reducing mortality in patients with early stage breast cancer appears to be generalisable to the selected trial-underrepresented groups.

Independent validation of the PREDICT breast cancer prognosis prediction tool in 45,789 patients using Scottish Cancer Registry data.

BACKGROUND: PREDICT is a widely used online prognostication and treatment benefit tool for patients with early stage breast cancer. The aim of this study was to conduct an independent validation exercise of the most up-to-date version of the PREDICT algorithm (version 2) using real-world outcomes from the Scottish population of women with breast cancer. METHODS: Patient data were obtained for all Scottish Cancer Registry (SCR) records with a diagnosis of primary invasive breast cancer diagnosed in the period between January 2001 and December 2015. Prognostic scores were calculated using the PREDICT version 2 algorithm. External validity was assessed by statistical analysis of discrimination and calibration. Discrimination was assessed by area under the receiver-operator curve (AUC). Calibration was assessed by comparing the predicted number of deaths to the observed number of deaths across relevant sub-groups. RESULTS: A total of 45,789 eligible cases were selected from 61,437 individual records. AUC statistics ranged from 0.74 to 0.77. Calibration results showed relatively close agreement between predicted and observed deaths. The 5-year complete follow-up sample reported some overestimation (11.5%), while the 10-year complete follow-up sample displayed more limited overestimation (1.7%). CONCLUSIONS: Validation results suggest that the PREDICT tool remains essentially relevant for contemporary patients with early stage breast cancer.

Survival estimates stratified by the Nottingham Prognostic Index for early breast cancer: a systematic review and meta-analysis of observational studies.

BACKGROUND: Estimates of survival for women diagnosed with early staged breast cancer are available based on stratification into prognostic categories defined using the Nottingham Prognostic Index (NPI). This review aimed to identify and summarize the estimated survival statistics from separate sources in the literature and to explore the extent of between-study heterogeneity in survival estimates. METHODS: Observational studies in women diagnosed with early and locally advanced breast cancer reporting overall survival by NPI category were identified using a systematic literature search. An exploratory meta-analysis was conducted to describe survival estimates and assess between-study heterogeneity. RESULTS: Twenty-eight studies were identified. Nineteen studies with sufficient data on overall survival were included in meta-analysis. A high level of heterogeneity in survival estimates was evident with I2 values in the range of 90 to 98%. CONCLUSIONS: The substantial differences between studies in the relationship between NPI categories and survival at 5 and 10 years poses challenges for use of this prognostic score in both clinical settings and in decision-analytic model-based economic evaluations.

Health economic evaluation of a serum-based blood test for brain tumour diagnosis: exploration of two clinical scenarios.

OBJECTIVES: To determine the potential costs and health benefits of a serum-based spectroscopic triage tool for brain tumours, which could be developed to reduce diagnostic delays in the current clinical pathway. DESIGN: A model-based health pre-trial economic assessment. Decision tree models were constructed based on simplified diagnostic pathways. Models were populated with parameters identified from rapid reviews of the literature and clinical expert opinion. SETTING: Explored as a test in both primary and secondary care (neuroimaging) in the UK health service, as well as application to the USA. PARTICIPANTS: Calculations based on an initial cohort of 10 000 patients. In primary care, it is estimated that the volume of tests would approach 75 000 per annum. The volume of tests in secondary care is estimated at 53 000 per annum. MAIN OUTCOME MEASURES: The primary outcome measure was quality-adjusted life-years (QALY), which were employed to derive incremental cost-effectiveness ratios (ICER) in a cost-effectiveness analysis. RESULTS: Results indicate that using a blood-based spectroscopic test in both scenarios has the potential to be highly cost-effective in a health technology assessment agency decision-making process, as ICERs were well below standard threshold values of £20 000-£30 000 per QALY. This test may be cost-effective in both scenarios with test sensitivities and specificities as low as 80%; however, the price of the test would need to be lower (less than approximately £40). CONCLUSION: Use of this test as triage tool in primary care has the potential to be both more effective and cost saving for the health service. In secondary care, this test would also be deemed more effective than the current diagnostic pathway.

Evaluation of a Stratified National Breast Screening Program in the United Kingdom: An Early Model-Based Cost-Effectiveness Analysis.

OBJECTIVES: To identify the incremental costs and consequences of stratified national breast screening programs (stratified NBSPs) and drivers of relative cost-effectiveness. METHODS: A decision-analytic model (discrete event simulation) was conceptualized to represent four stratified NBSPs (risk 1, risk 2, masking [supplemental screening for women with higher breast density], and masking and risk 1) compared with the current UK NBSP and no screening. The model assumed a lifetime horizon, the health service perspective to identify costs (£, 2015), and measured consequences in quality-adjusted life-years (QALYs). Multiple data sources were used: systematic reviews of effectiveness and utility, published studies reporting costs, and cohort studies embedded in existing NBSPs. Model parameter uncertainty was assessed using probabilistic sensitivity analysis and one-way sensitivity analysis. RESULTS: The base-case analysis, supported by probabilistic sensitivity analysis, suggested that the risk stratified NBSPs (risk 1 and risk-2) were relatively cost-effective when compared with the current UK NBSP, with incremental cost-effectiveness ratios of £16,689 per QALY and £23,924 per QALY, respectively. Stratified NBSP including masking approaches (supplemental screening for women with higher breast density) was not a cost-effective alternative, with incremental cost-effectiveness ratios of £212,947 per QALY (masking) and £75,254 per QALY (risk 1 and masking). When compared with no screening, all stratified NBSPs could be considered cost-effective. Key drivers of cost-effectiveness were discount rate, natural history model parameters, mammographic sensitivity, and biopsy rates for recalled cases. A key assumption was that the risk model used in the stratification process was perfectly calibrated to the population. CONCLUSIONS: This early model-based cost-effectiveness analysis provides indicative evidence for decision makers to understand the key drivers of costs and QALYs for exemplar stratified NBSP.

How do individuals' health behaviours respond to an increase in the supply of health care? Evidence from a natural experiment.

The efficacy of the management of long-term conditions depends in part on whether healthcare and health behaviours are complements or substitutes in the health production function. On the one hand, individuals might believe that improved health care can raise the marginal productivity of their own health behaviour and decide to complement health care with additional effort in healthier behaviours. On the other hand, health care can lower the cost of unhealthy behaviours by compensating for their negative effects. Individuals may therefore reduce their effort in healthier lifestyles. Identifying which of these effects prevails is complicated by the endogenous nature of treatment decisions and individuals' behavioural responses. We explore whether the introduction in 2004 of the Quality and Outcomes Framework (QOF), a financial incentive for family doctors to improve the quality of healthcare, affected the population's weight, smoking and drinking behaviours by applying a sharp regression discontinuity design to a sample of 32,102 individuals in the Health Survey for England (1997-2009). We find that individuals with the targeted health conditions improved their lifestyle behaviours. This complementarity was only statistically significant for smoking, which reduced by 0.7 cigarettes per person per day, equal to 18% of the mean. We investigate whether this change was attributable to the QOF by testing for other discontinuity points, including the introduction of a smoking ban in 2007 and changes to the QOF in 2006. We also examine whether medication and smoking cessation advice are potential mechanisms and find no statistically significant discontinuities for these aspects of health care supply. Our results suggest that a general improvement in healthcare generated by provider incentives can have positive unplanned effects on patients' behaviours.