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Background: Cholesterol guidelines typically prioritize primary prevention statin therapy on the basis of 10-year risk of cardiovascular disease. The advent of generic pricing may justify expansion of statin eligibility. Moreover, 10-year risk may not be the optimal approach for statin prioritization. We estimated the cost-effectiveness of expanding preventive statin eligibility and evaluated novel approaches to prioritization from a Scottish health sector perspective. Methods: A computer simulation model predicted long-term health and cost outcomes in Scottish adults ≥40 years of age. Epidemiologic analysis was completed using the Scottish Heart Health Extended Cohort, Scottish Morbidity Records, and National Records of Scotland. A simulation cohort was constructed with data from the Scottish Health Survey 2011 and contemporary population estimates. Treatment and cost inputs were derived from published literature and health service cost data. The main outcome measure was the lifetime incremental cost-effectiveness ratio, evaluated as cost (2020 GBP) per quality-adjusted life-year (QALY) gained. Three approaches to statin prioritization were analyzed: 10-year risk scoring using the ASSIGN score, age-stratified risk thresholds to increase treatment rates in younger individuals, and absolute risk reduction (ARR)–guided therapy to increase treatment rates in individuals with elevated cholesterol levels. For each approach, 2 policies were considered: treating the same number of individuals as those with an ASSIGN score ≥20% (age-stratified risk threshold 20, ARR 20) and treating the same number of individuals as those with an ASSIGN score ≥10% (age-stratified risk threshold 10, ARR 10). Results: Compared with an ASSIGN score ≥20%, reducing the risk threshold for statin initiation to 10% expanded eligibility from 804 000 (32% of adults ≥40 years of age without CVD) to 1 445 500 individuals (58%). This policy would be cost-effective (incremental cost-effectiveness ratio, £12 300/QALY [95% CI, £7690/QALY–£26 500/QALY]). Incremental to an ASSIGN score ≥20%, ARR 20 produced ≈8800 QALYs and was cost-effective (£7050/QALY [95% CI, £4560/QALY–£10 700/QALY]). Incremental to an ASSIGN score ≥10%, ARR 10 produced ≈7950 QALYs and was cost-effective (£11 700/QALY [95% CI, £9250/QALY–£16 900/QALY]). Both age-stratified risk threshold strategies were dominated (ie, more expensive and less effective than alternative treatment strategies). Conclusions: Generic pricing has rendered preventive statin therapy cost-effective for many adults. ARR–guided therapy is more effective than 10-year risk scoring and is cost-effective.

Background Cholesterol reduction with proprotein convertase subtilisin-kexin type 9 inhibitors reduces ischemic events; however, the cost-effectiveness in statin-treated patients with recent acute coronary syndrome remains uncertain. Objectives This study sought to determine whether further cholesterol reduction with alirocumab would be cost-effective in patients with a recent acute coronary syndrome on optimal statin therapy. Methods A cost-effectiveness model leveraging patient-level data from ODYSSEY OUTCOMES (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab) was developed to estimate costs and outcomes over a lifetime horizon. Patients (n = 18,924) had a recent acute coronary syndrome and were on high-intensity or maximum-tolerated statin therapy, with a baseline low-density lipoprotein cholesterol (LDL-C) level ≥70 mg/dl, non–high-density lipoprotein cholesterol ≥100 mg/dl, or apolipoprotein B ≥80 mg/l. Alirocumab 75 mg or placebo was administered subcutaneously every 2 weeks. Alirocumab was blindly titrated to 150 mg if LDL-C remained ≥50 mg/dl or switched to placebo if 2 consecutive LDL-C levels were Results Across the overall population recruited to the ODYSSEY OUTCOMES trial, using an annual treatment cost of US$5,850, the mean overall incremental cost-effectiveness ratio was US$92,200 per QALY (base case). The cost was US$41,800 per QALY in patients with baseline LDL-C ≥100 mg/dl, whereas in those with LDL-C Conclusions In patients with a recent acute coronary syndrome on optimal statin therapy, alirocumab improves cardiovascular outcomes at costs considered intermediate value, with good value in patients with baseline LDL-C ≥100 mg/dl but less economic value with LDL-C NCT01663402 )

Health economic evaluations are comparative analyses of alternative courses of action in terms of their costs and consequences. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, published in 2013, was created to ensure health economic evaluations are identifiable, interpretable, and useful for decision making. It was intended as guidance to help authors report accurately which health interventions were being compared and in what context, how the evaluation was undertaken, what the findings were, and other details that may aid readers and reviewers in interpretation and use of the study. The new CHEERS 2022 statement replaces the previous CHEERS reporting guidance. It reflects the need for guidance that can be more easily applied to all types of health economic evaluation, new methods and developments in the field, and the increased role of stakeholder involvement including patients and the public. It is also broadly applicable to any form of intervention intended to improve the health of individuals or the population, whether simple or complex, and without regard to context (such as healthcare, public health, education, and social care). This Explanation and Elaboration Report presents the new CHEERS 2022 28-item checklist with recommendations and explanation and examples for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer-reviewed journals and the peer reviewers and editors assessing them for publication. Nevertheless, we anticipate familiarity with reporting requirements will be useful for analysts when planning studies. It may also be useful for health technology assessment bodies seeking guidance on reporting, given that there is an increasing emphasis on transparency in decision making.

The arrival of precision oncology is challenging the evidence standards under which technologies are evaluated for regulatory approval as well as for health technology assessment (HTA) purposes. Several key concepts are discussed to highlight the source of the challenges in evaluating these products, particularly those impacting the HTA of histology-independent therapies. These include the basket trial design, high uncertainty in (potentially substantial) benefits for histology-independent therapies, and the inability to identify and quantify benefits of standard of care in daily practice when the biomarker is not currently used in practice. There is little precedent for a technology with the unique mixture of challenges for HTA of histology-independent therapies and they will be evaluated using standard HTA, as there currently is no evidence suggesting the standard HTA framework is not appropriate. A number of questions proposed to help guide HTA bodies when assessing the appropriateness of local processes to optimally evaluate histology-independent therapies. Pragmatic solutions are further proposed to decrease uncertainty in the benefits of histology independent therapies as well as fill gaps in comparative evidence. The proposed solutions ensure a consistent and streamlined approach to evaluation across histology-independent products, although with varying strengths and limitations. Alongside these solutions, sponsors should engage early with HTA bodies/payers and regulatory agencies through parallel/joint scientific advice to facilitate the integration of both regulatory and HTA perspectives into one clinical development programme, potentially reconciling evidence requirements.

Summary Background The risk of case-fatality following hospitalisation for asthma has not been well characterised. We describe trends in 30 day case-fatality following hospitalisation for asthma in adults in Scotland from 1981 to 2009. Methods Using the Scottish Morbidity Record Scheme (SMR01) with all asthma hospitalisations for adults (≥18 years) with ICD9 493 and ICD10 J45–J46 in the principal diagnostic position at discharge (1981–2009). These data were linked to mortality data from the General Register Office for Scotland (GROS), with asthma case-fatality defined as death within 30 days of asthma admission (in or out of hospital). Logistic regression was used to explore the impact of age, sex, previous asthma admission (in the 12 months prior to hospitalisation), socioeconomic deprivation, year of admission and co-morbidity on 30-day case-fatality. Results There were a total of 116 457 asthma hospitalisations; a total of 1000 (0.9%) hospitalisations resulted in a post-admission death (within 30 days of admission). Odds ratios for unadjusted and adjusted case-fatality showed a decreased risk of case-fatality from the mid-1990s onwards when compared to case-fatality in 1981. Advancing age and co-morbid diagnoses of respiratory failure, cancer, renal failure, cor pulmonale, coronary heart disease and respiratory infection were associated with increased likelihood of death. Conclusions 30 day case-fatality has declined over the last three decades, comparable to case-fatality reported in other parts of the UK. This decline may be in part due to improved guidelines, protocols and disease management for asthma over the last 30 years. The likelihood of death 30 days following an asthma admission increased with age group and was associated with respiratory failure, renal failure and cancer.

Spurred by a desire to improve quality of care and to understand the relative value of medical treatments, there has been a recent surge of interest in publicly funded comparative effectiveness research (CER) in the US. As health technology assessment (HTA) shares some of the same goals as CER, and publicly funded HTA has been a feature within other industrialized countries for many years, a review of HTA activities in some of these countries can be a helpful source of information for the US debate. Informed by a literature review, and in two cases augmented by informant interviews, we reviewed the organization of HTA activities in five jurisdictions: Canada, Sweden, Scotland, the Netherlands and Australia. We provide a summary description of the healthcare system in each country as well as a description of the key features of their HTA bodies, with a particular focus on the processes of HTA for listing medications on public formularies. Four of the committees evaluating medications for formulary inclusion are funded by, but remain at arm's length from, the government (Canada, Australia, Sweden and Scotland), while the process is fully embedded within the government in the Netherlands. Each of these jurisdictions has a stated preference for comparative outcomes evidence from randomized controlled trials, but will, under certain circumstances, accept randomized evidence using surrogate markers, other comparators that are not directly relevant or non-randomized evidence. Health technology evaluation committees largely comprise health professionals, with public representatives included in the Canadian, Australian and Scottish committees. Scotland is the only one of the five jurisdictions reviewed to have industry representation on the evaluation committee. We identified seven characteristics that are shared across the jurisdictions reviewed and that potentially serve as insights for development of CER in the US: (i) the process must be responsive to stakeholders' interests, in that the turn-around time for assessments must be minimized, transparency must be maximized, the process must be considered fair using universally agreed standards and the process must be modifiable based on stakeholders' requirements; (ii) the assessment of medical technologies other than drugs may present different challenges and is managed separately in other HTA organizations; (iii) because of the link between HTA and reimbursement decisions, completion of the HTA process following regulatory approval can delay market access to new technologies, thus closer integration between regulatory approval and HTA processes is being explored internationally; (iv) there is a direct or indirect link to reimbursement in the jurisdictions explored - without this link the role of CER in the US will remain advisory; (v) each jurisdiction reviewed benefits from a single payer that is informed by the process - given the diverse multipayer environment in the US, CER in the US may usefully focus on generating comparative effectiveness evidence; (vi) a common metric for assessing intended and unintended effects of treatment allows comparison across different technologies; and (vii) one stated focus of CER is on therapeutic benefit among 'high-priority populations', including specific demographic groups (the elderly and children, racial and ethnic minorities) and individuals with disabilities, multiple chronic conditions and specific genomic factors. This will be difficult to achieve because epidemiological evidence of differences in therapeutic benefit among subgroups is detected through effect modification, or more specifically, statistical evidence of effect measure modification, typically on relative measures of effect. Few randomized trials have enough power to detect effect modification and these have been uncommon in the scientific literature. As consideration is given to the development of a publicly funded CER body in the US, much can be learned from the international experience. Nevertheless, there are some distinctive features of the US healthcare system that must be taken into account when assessing the transferability of these insights.

To estimate the cost-effectiveness of 10 mg rosuvastatin daily for older patients with systolic heart failure in the Controlled Rosuvastatin Multinational Study in Heart Failure (CORONA) trial.This within trial analysis of CORONA used major cardiovascular (CV) events as the outcome measure. Resource use was valued and the costs of hospitalizations, procedures, and statin use compared. Cost-effectiveness was estimated as cost per major CV event avoided. There were significantly fewer major CV events in the rosuvastatin group compared with the placebo group (1.04 vs. 1.20 per patient; difference 0.164; 95% CI: 0.075-0.254, P This economic analysis showed that a significant reduction in major CV events with rosuvastatin led to significantly reduced costs of CV hospitalizations and procedures. The reduction in associated costs for major CV events was found to offset partially (by 44%) the cost of rosuvastatin treatment in patients with systolic heart failure.

To determine the cost-effectiveness of salvage cryotherapy (SC) in men with radiation recurrent prostate cancer (RRPC).Cost-utility analysis using decision analytic modelling by a Markov model.Compared SC and androgen deprivation therapy (ADT) in a cohort of patients with RRPC (biopsy proven local recurrence, no evidence of metastatic disease). A literature review captured published data to inform the decision model, and resource use data were from the Scottish Prostate Cryotherapy Service. The model was run in monthly cycles for RRPC men, mean age of 70 years. The model was run over the patient lifetime, to assess changes in patient health states and the associated quality of life, survival and cost impacts. Results are reported in terms of the discounted incremental costs and discounted incremental quality-adjusted life years (QALYs) gained between the 2 alternative interventions. Probabilistic sensitivity analysis used a 10,000 iteration Monte Carlo simulation.SC has a high upfront treatment cost, but delays the ongoing monthly cost of ADT. SC is the dominant strategy over the patient lifetime; it is more effective with an incremental 0.56 QALY gain (95% CI 0.28 to 0.87), and less costly with a reduced lifetime cost of £29,719 (€37,619) (95% CI -51,985 to -9243). For a ceiling ratio of £30,000, SC has a 100% probability to be cost-effective. The cost neutral point was at 3.5 years, when the upfront cost of SC (plus any subsequent cumulative cost of side effects and ADT) equates the cumulative cost in the ADT arm. Limitations of our model may arise from its insensitivity to parameter or structural uncertainty.The platform for SC versus ADT cost-effective analysis can be employed to evaluate other treatment modalities or strategies in RRPC. SC is the dominant strategy, costing less over a patient's lifetime with improvements in QALYs.This economic analysis was undertaken as part of the CROP RCT study72677390; it was a pre-trial economic model developed and analysed during the pre-results stage of the RCT.

Introduction A substantial proportion of low birth weight is attributable to the mother's cultural and socioeconomic circumstances. Early childhood programmes have been widely developed to improve child outcomes. In the UK, the Health in Pregnancy (HiP) grant, a universal conditional cash transfer of £190, was introduced for women reaching the 25th week of pregnancy with a due date on/or after 6 April 2009 and subsequently withdrawn for women reaching the 25th week of pregnancy on/or after 1 January 2011. The current study focuses on the evaluation of the effectiveness and cost-effectiveness of the HiP grant. Methods and analysis The population under study will be all singleton births in Scotland over the periods of January 2004 to March 2009 (preintervention), April 2009 to April 2011 (intervention) and May 2011 to December 2013 (postintervention). Data will be extracted from the Scottish maternity and neonatal database. The analysis period 2004–2013 should yield over 585 000 births. The primary outcome will be birth weight among singleton births. Other secondary outcomes will include gestation at booking, booking before 25 weeks; measures of size and stage; gestational age at delivery; weight-for-dates, term at birth; birth outcomes and maternal smoking. The main statistical method we will use is interrupted time series. Outcomes will be measured on individual births nested within mothers, with mothers themselves clustered within data zones. Multilevel regression models will be used to determine whether the outcomes changed during the period in which the HiP grants was in effect. Subgroup analyses will be conducted for those groups most likely to benefit from the payments. Ethics and dissemination Approval for data collection, storage and release for research purpose has been given (6 May 2014, PAC38A/13) by the Privacy Advisory Committee. The results of this study will be disseminated through peer-reviewed publications in journals, national and international conferences.

When choosing between mutually exclusive treatment options, it is common to construct a cost-effectiveness frontier on the cost-effectiveness plane that represents efficient points from among the treatment choices. Treatment options internal to the frontier are considered inefficient and are excluded either by strict dominance or by appealing to the principle of extended dominance. However, when uncertainty is considered, options excluded under the baseline analysis may form part of the cost-effectiveness frontier. By adopting a Bayesian approach, where distributions for model parameters are specified, uncertainty in the decision concerning which treatment option should be implemented is addressed directly. The approach is illustrated using an example from a recently published cost-effectiveness analysis of different possible treatment strategies for gastroesophageal reflux disease. It is argued that probabilistic analyses should be encouraged because they have potential to quantify the strength of evidence in favor of particular treatment choices.

The relation between methodological advances in estimation of confidence intervals (CIs) for incremental cost-effectiveness ratios (ICER) and estimation of cost effectiveness in the presence of censoring has not been explored. The authors address the joint problem of estimating ICER precision in the presence of censoring.Using patient-level data (n = 168) on cost and survival from a published placebo-controlled trial, the authors compared 2 methods of measuring uncertainty with censored data: 1) Bootstrap with censor adjustment (BCA); 2) Fieller's method with censor adjustment (FCA). The authors estimate the FCA over all possible values for the correlation (rho) between costs and effects (range = -1 to + 1) and also examine the use of the correlation between cases without censoring adjustment (i.e., simple time-on-study) for costs and effects as an approximation for rho.Using time-on-study, which considers all censored observations as responders (deaths), yields 0.64 life-years gained at an additional cost of 87.9 for a cost per life-year of 137 (95% CI by bootstrap -5.9 to 392). Censoring adjustment corrects for the bias in the time-on-study approach and reduces the cost per life-year estimate to 132 (=72/0.54). Confidence intervals with censor adjustment were approximately 40% wider than the base-case without adjustment. Using the Fieller method with an approximation of rho based on the uncensored cost and effect correlation provides a 95% CI of (-48 to 529), which is very close to the BCA interval of (-52 to 504).Adjustment for censoring is necessary in cost-effectiveness studies to obtain unbiased estimates of ICER with appropriate uncertainty limits. In this study, BCA and FCA methods, the latter with approximated covariance, are simple to compute and give similar confidence intervals.

Jonathan Cook and colleagues systematically reviewed the literature for methods of determining the target difference for use in calculating the necessary sample size for clinical trials, and discuss which methods are best for various types of trials. Please see later in the article for the Editors' Summary, Background Randomised controlled trials (RCTs) are widely accepted as the preferred study design for evaluating healthcare interventions. When the sample size is determined, a (target) difference is typically specified that the RCT is designed to detect. This provides reassurance that the study will be informative, i.e., should such a difference exist, it is likely to be detected with the required statistical precision. The aim of this review was to identify potential methods for specifying the target difference in an RCT sample size calculation. Methods and Findings A comprehensive systematic review of medical and non-medical literature was carried out for methods that could be used to specify the target difference for an RCT sample size calculation. The databases searched were MEDLINE, MEDLINE In-Process, EMBASE, the Cochrane Central Register of Controlled Trials, the Cochrane Methodology Register, PsycINFO, Science Citation Index, EconLit, the Education Resources Information Center (ERIC), and Scopus (for in-press publications); the search period was from 1966 or the earliest date covered, to between November 2010 and January 2011. Additionally, textbooks addressing the methodology of clinical trials and International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) tripartite guidelines for clinical trials were also consulted. A narrative synthesis of methods was produced. Studies that described a method that could be used for specifying an important and/or realistic difference were included. The search identified 11,485 potentially relevant articles from the databases searched. Of these, 1,434 were selected for full-text assessment, and a further nine were identified from other sources. Fifteen clinical trial textbooks and the ICH tripartite guidelines were also reviewed. In total, 777 studies were included, and within them, seven methods were identified—anchor, distribution, health economic, opinion-seeking, pilot study, review of the evidence base, and standardised effect size. Conclusions A variety of methods are available that researchers can use for specifying the target difference in an RCT sample size calculation. Appropriate methods may vary depending on the aim (e.g., specifying an important difference versus a realistic difference), context (e.g., research question and availability of data), and underlying framework adopted (e.g., Bayesian versus conventional statistical approach). Guidance on the use of each method is given. No single method provides a perfect solution for all contexts. Please see later in the article for the Editors' Summary, Editors' Summary Background A clinical trial is a research study in which human volunteers are randomized to receive a given intervention or not, and outcomes are measured in both groups to determine the effect of the intervention. Randomized controlled trials (RCTs) are widely accepted as the preferred study design because by randomly assigning participants to groups, any differences between the two groups, other than the intervention under study, are due to chance. To conduct a RCT, investigators calculate how many patients they need to enroll to determine whether the intervention is effective. The number of patients they need to enroll depends on how effective the intervention is expected to be, or would need to be in order to be clinically important. The assumed difference between the two groups is the target difference. A larger target difference generally means that fewer patients need to be enrolled, relative to a smaller target difference. The target difference and number of patients enrolled contribute to the study's statistical precision, and the ability of the study to determine whether the intervention is effective. Selecting an appropriate target difference is important from both a scientific and ethical standpoint. Why Was This Study Done? There are several ways to determine an appropriate target difference. The authors wanted to determine what methods for specifying the target difference are available and when they can be used. What Did the Researchers Do and Find? To identify studies that used a method for determining an important and/or realistic difference, the investigators systematically surveyed the research literature. Two reviewers screened each of the abstracts chosen, and a third reviewer was consulted if necessary. The authors identified seven methods to determine target differences. They evaluated the studies to establish similarities and differences of each application. Points about the strengths and limitations of the method and how frequently the method was chosen were also noted. What Do these Findings Mean? The study draws attention to an understudied but important part of designing a clinical trial. Enrolling the right number of patients is very important—too few patients and the study may not be able to answer the study question; too many and the study will be more expensive and more difficult to conduct, and will unnecessarily expose more patients to any study risks. The target difference may also be helpful in interpreting the results of the trial. The authors discuss the pros and cons of different ways to calculate target differences and which methods are best for which types of studies, to help inform researchers designing such studies. Additional Information Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001645. Wikipedia has an entry on sample size determination that discusses the factors that influence sample size calculation, including the target difference and the statistical power of a study (statistical power is the ability of a study to find a difference between treatments when a true difference exists). (Note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages.) The University of Ottawa has an article that explains how different factors influence the power of a study