Beyond 10-Year Risk: A Cost-Effectiveness Analysis of Statins for the Primary Prevention of Cardiovascular Disease.

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.

Oral versus Intravenous Antibiotics for Bone and Joint Infection.

BACKGROUND: The management of complex orthopedic infections usually includes a prolonged course of intravenous antibiotic agents. We investigated whether oral antibiotic therapy is noninferior to intravenous antibiotic therapy for this indication. METHODS: We enrolled adults who were being treated for bone or joint infection at 26 U.K. centers. Within 7 days after surgery (or, if the infection was being managed without surgery, within 7 days after the start of antibiotic treatment), participants were randomly assigned to receive either intravenous or oral antibiotics to complete the first 6 weeks of therapy. Follow-on oral antibiotics were permitted in both groups. The primary end point was definitive treatment failure within 1 year after randomization. In the analysis of the risk of the primary end point, the noninferiority margin was 7.5 percentage points. RESULTS: Among the 1054 participants (527 in each group), end-point data were available for 1015 (96.3%). Treatment failure occurred in 74 of 506 participants (14.6%) in the intravenous group and 67 of 509 participants (13.2%) in the oral group. Missing end-point data (39 participants, 3.7%) were imputed. The intention-to-treat analysis showed a difference in the risk of definitive treatment failure (oral group vs. intravenous group) of -1.4 percentage points (90% confidence interval [CI], -4.9 to 2.2; 95% CI, -5.6 to 2.9), indicating noninferiority. Complete-case, per-protocol, and sensitivity analyses supported this result. The between-group difference in the incidence of serious adverse events was not significant (146 of 527 participants [27.7%] in the intravenous group and 138 of 527 [26.2%] in the oral group; P=0.58). Catheter complications, analyzed as a secondary end point, were more common in the intravenous group (9.4% vs. 1.0%). CONCLUSIONS: Oral antibiotic therapy was noninferior to intravenous antibiotic therapy when used during the first 6 weeks for complex orthopedic infection, as assessed by treatment failure at 1 year. (Funded by the National Institute for Health Research; OVIVA Current Controlled Trials number, ISRCTN91566927 .).

Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations.

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 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, as well as 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 health care, public health, education, social care, etc). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer reviewed journals as well as the peer reviewers and editors assessing them for publication. However, 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, as there is an increasing emphasis on transparency in decision making.

Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 Explanation and Elaboration: A Report of the ISPOR CHEERS II Good Practices Task Force.

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.

Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) Statement: Updated Reporting Guidance for Health Economic Evaluations.

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 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, as well as 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 health care, public health, education, social care, etc). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer reviewed journals as well as the peer reviewers and editors assessing them for publication. However, 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, as there is an increasing emphasis on transparency in decision making.

Healing the past, reimagining the present, investing in the future: What should be the role of race as a proxy covariate in health economics informed health care policy?

In this perspective, the assertion that race-free risk assessment would harm patients of all races is critiqued from the viewpoint that race is not just another covariate in our arsenal. Although race may be associated with outcome, it is nevertheless a proxy for a myriad of other potential explanatory variables that could be genetic/biological but in many circumstances are more likely to be sociological/socioeconomic. It is argued that the pursuit of health maximization through the use of socially constructed variables like race must be done sensitively, recognizing that racial covariates in the medical arena can be subject to structural, institutional or personal biases. Even when such biases are thought to be minimized, the appearance of such bias may be sufficient to justify the removal of its use, particularly where employing a racial covariate could further increase existing disparities. While racial covariates may have descriptive value in helping to understand such disparities, it is beholden on the scientific community to explore alternatives to racial covariates that may provide the same or perhaps even better prognostic value in our analyses.

Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations.

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 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, as well as 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 health care, public health, education, social care, etc). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer reviewed journals as well as the peer reviewers and editors assessing them for publication. However, 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, as there is an increasing emphasis on transparency in decision making.

Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations.

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 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, as well as 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 health care, public health, education, social care, etc). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer reviewed journals as well as the peer reviewers and editors assessing them for publication. However, 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, as there is an increasing emphasis on transparency in decision making.

Consolidated health economic evaluation reporting standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations.

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 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, as well as 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 health care, public health, education, social care, etc.). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer-reviewed journals, as well as the peer reviewers and editors assessing them for publication. However, 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, as there is an increasing emphasis on transparency in decision making.

Estimating (quality-adjusted) life-year losses associated with deaths: With application to COVID-19.

Many epidemiological models of the COVID-19 pandemic have focused on preventing deaths. Questions have been raised as to the frailty of those succumbing to the COVID-19 infection. In this paper we employ standard life table methods to illustrate how the potential quality-adjusted life-year (QALY) losses associated with COVID-19 fatalities could be estimated, while adjusting for comorbidities in terms of impact on both mortality and quality of life. Contrary to some suggestions in the media, we find that even relatively elderly patients with high levels of comorbidity can still lose substantial life years and QALYs. The simplicity of the method facilitates straightforward international comparisons as the pandemic evolves. In particular, we compare five different countries and show that differences in the average QALY losses for each COVID-19 fatality is driven mainly by differing age distributions for those dying of the disease.

SCOT: a comparison of cost-effectiveness from a large randomised phase III trial of two durations of adjuvant Oxaliplatin combination chemotherapy for colorectal cancer.

BACKGROUND: The Short Course Oncology Therapy (SCOT) study is an international, multicentre, non-inferiority randomised controlled trial assessing the efficacy, toxicity, and cost-effectiveness of 3 months (3 M) versus the usually given 6 months (6 M) of adjuvant chemotherapy in colorectal cancer. METHODS: In total, 6088 patients with fully resected high-risk stage II or stage III colorectal cancer were randomised and followed up for 3-8 years. The within-trial cost-effectiveness analysis from a UK health-care perspective is presented using the resource use data, quality of life (EQ-5D-3L), time on treatment (ToT), disease-free survival after treatment (DFS) and overall survival (OS) data. Quality-adjusted partitioned survival analysis and Kaplan-Meier Sample Average Estimator estimated QALYs and costs. Probabilistic sensitivity and subgroup analysis was undertaken. RESULTS: The 3 M arm is less costly (-£4881; 95% CI: -£6269; -£3492) and entails (non-significant) QALY gains (0.08; 95% CI: -0.086; 0.230) due to a better significant quality of life. The net monetary benefit was significantly higher in 3 M under a wide range of monetary values of a QALY. The subgroup analysis found similar results for patients in the CAPOX regimen. However, for the FOLFOX regimen, 3 M had lower QALYs than 6 M (not statistically significant). CONCLUSIONS: Overall, 3 M dominates 6 M with no significant detrimental impact on QALYs. The results provide the economic case that a 3 M treatment strategy should be considered a new standard of care.

External Validation of Health Economic Decision Models for Chronic Obstructive Pulmonary Disease (COPD): Report of the Third COPD Modeling Meeting.

OBJECTIVES: To validate outcomes of presently available chronic obstructive pulmonary disease (COPD) cost-effectiveness models against results of two large COPD trials-the 3-year TOwards a Revolution in COPD Health (TORCH) trial and the 4-year Understanding Potential Long-term Impacts on Function with Tiotropium (UPLIFT) trial. METHODS: Participating COPD modeling groups simulated the outcomes for the placebo-treated groups of the TORCH and UPLIFT trials using baseline characteristics of the trial populations as input. Groups then simulated treatment effectiveness by using relative reductions in annual decline in lung function and exacerbation frequency observed in the most intensively treated group compared with placebo as input for the models. Main outcomes were (change in) total/severe exacerbations and mortality. Furthermore, the absolute differences in total exacerbations and quality-adjusted life-years (QALYs) were used to approximate the cost per exacerbation avoided and the cost per QALY gained. RESULT: Of the six participating models, three models reported higher total exacerbation rates than observed in the TORCH trial (1.13/patient-year) (models: 1.22-1.48). Four models reported higher rates than observed in the UPLIFT trial (0.85/patient-year) (models: 1.13-1.52). Two models reported higher mortality rates than in the TORCH trial (15.2%) (models: 20.0% and 30.6%) and the UPLIFT trial (16.3%) (models: 24.8% and 36.0%), whereas one model reported lower rates (9.8% and 12.1%, respectively). Simulation of treatment effectiveness showed that the absolute reduction in total exacerbations, the gain in QALYs, and the cost-effectiveness ratios did not differ from the trials, except for one model. CONCLUSIONS: Although most of the participating COPD cost-effectiveness models reported higher total exacerbation rates than observed in the trials, estimates of the absolute treatment effect and cost-effectiveness ratios do not seem different from the trials in most models.

Cost-Effectiveness Model for Chemoimmunotherapy Options in Patients with Previously Untreated Chronic Lymphocytic Leukemia Unsuitable for Full-Dose Fludarabine-Based Therapy.

OBJECTIVES: To evaluate the cost-effectiveness of treatment with anti-CD20 monoclonal antibody obinutuzumab plus chlorambucil (GClb) in untreated patients with chronic lymphocytic leukemia unsuitable for full-dose fludarabine-based therapy. METHODS: A Markov model was used to assess the cost-effectiveness of GClb versus other chemoimmunotherapy options. The model comprised three mutually exclusive health states: "progression-free survival (with/without therapy)", "progression (refractory/relapsed lines)", and "death". Each state was assigned a health utility value representing patients' quality of life and a specific cost value. Comparisons between GClb and rituximab plus chlorambucil or only chlorambucil were performed using patient-level clinical trial data; other comparisons were performed via a network meta-analysis using information gathered in a systematic literature review. To support the model, a utility elicitation study was conducted from the perspective of the UK National Health Service. RESULTS: There was good agreement between the model-predicted progression-free and overall survival and that from the CLL11 trial. On incorporating data from the indirect treatment comparisons, it was found that GClb was cost-effective with a range of incremental cost-effectiveness ratios below a threshold of £30,000 per quality-adjusted life-year gained, and remained so during deterministic and probabilistic sensitivity analyses under various scenarios. CONCLUSIONS: GClb was estimated to increase both quality-adjusted life expectancy and treatment costs compared with several commonly used therapies, with incremental cost-effectiveness ratios below commonly referenced UK thresholds. This article offers a real example of how to combine direct and indirect evidence in a cost-effectiveness analysis of oncology drugs.

Patient Heterogeneity in Health Economic Decision Models for Chronic Obstructive Pulmonary Disease: Are Current Models Suitable to Evaluate Personalized Medicine?

OBJECTIVES: To assess how suitable current chronic obstructive pulmonary disease (COPD) cost-effectiveness models are to evaluate personalized treatment options for COPD by exploring the type of heterogeneity included in current models and by validating outcomes for subgroups of patients. METHODS: A consortium of COPD modeling groups completed three tasks. First, they reported all patient characteristics included in the model and provided the level of detail in which the input parameters were specified. Second, groups simulated disease progression, mortality, quality-adjusted life-years (QALYs), and costs for hypothetical subgroups of patients that differed in terms of sex, age, smoking status, and lung function (forced expiratory volume in 1 second [FEV1] % predicted). Finally, model outcomes for exacerbations and mortality for subgroups of patients were validated against published subgroup results of two large COPD trials. RESULTS: Nine COPD modeling groups participated. Most models included sex (seven), age (nine), smoking status (six), and FEV1% predicted (nine), mainly to specify disease progression and mortality. Trial results showed higher exacerbation rates for women (found in one model), higher mortality rates for men (two models), lower mortality for younger patients (four models), and higher exacerbation and mortality rates in patients with severe COPD (four models). CONCLUSIONS: Most currently available COPD cost-effectiveness models are able to evaluate the cost-effectiveness of personalized treatment on the basis of sex, age, smoking, and FEV1% predicted. Treatment in COPD is, however, more likely to be personalized on the basis of clinical parameters. Two models include several clinical patient characteristics and are therefore most suitable to evaluate personalized treatment, although some important clinical parameters are still missing.

Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model: A Web-based program designed to evaluate the cost-effectiveness of disease management programs in heart failure.

BACKGROUND: Heart failure disease management programs can influence medical resource use and quality-adjusted survival. Because projecting long-term costs and survival is challenging, a consistent and valid approach to extrapolating short-term outcomes would be valuable. METHODS: We developed the Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model, a Web-based simulation tool designed to integrate data on demographic, clinical, and laboratory characteristics; use of evidence-based medications; and costs to generate predicted outcomes. Survival projections are based on a modified Seattle Heart Failure Model. Projections of resource use and quality of life are modeled using relationships with time-varying Seattle Heart Failure Model scores. The model can be used to evaluate parallel-group and single-cohort study designs and hypothetical programs. Simulations consist of 10,000 pairs of virtual cohorts used to generate estimates of resource use, costs, survival, and incremental cost-effectiveness ratios from user inputs. RESULTS: The model demonstrated acceptable internal and external validity in replicating resource use, costs, and survival estimates from 3 clinical trials. Simulations to evaluate the cost-effectiveness of heart failure disease management programs across 3 scenarios demonstrate how the model can be used to design a program in which short-term improvements in functioning and use of evidence-based treatments are sufficient to demonstrate good long-term value to the health care system. CONCLUSION: The Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model provides researchers and providers with a tool for conducting long-term cost-effectiveness analyses of disease management programs in heart failure.

Economic Evaluation of Cinacalcet in the United States: The EVOLVE Trial.

BACKGROUND: Previous economic evaluations of cinacalcet in patients with secondary hyperparathyroidism (sHPT) relied on the combination of surrogate end points in clinical trials and epidemiologic studies. OBJECTIVES: The objective was to conduct an economic evaluation of cinacalcet on the basis of the EValuation Of Cinacalcet HCl Therapy to Lower CardioVascular Events (EVOLVE) trial from a US payer perspective. METHODS: We developed a semi-Markov model to assess the cost-effectiveness of cinacalcet in addition to conventional therapy, compared with conventional therapy alone, in patients with moderate-to-severe sHPT receiving hemodialysis. We used treatment effect estimates from the unadjusted intent-to-treat (ITT) analysis and prespecified covariate-adjusted ITT analysis as our main analyses. We assessed model sensitivity to variations in individual inputs and overall decision uncertainty through probabilistic sensitivity analyses. RESULTS: The incremental cost-effectiveness ratio (ICER) for cinacalcet was $61,705 per life-year and $79,562 per quality-adjusted life-year (QALY) gained using the covariate-adjusted ITT analysis. Probabilistic sensitivity analysis suggested a 73.2% chance of the ICER being below a willingness-to-pay threshold of $100,000. Treatment effects from unadjusted ITT analysis yielded an ICER of $115,876 per QALY. The model was most sensitive to the treatment effect on mortality. CONCLUSIONS: In the unadjusted ITT analysis, cinacalcet does not represent a cost- effective use of health care resources when applying a willingness-to-pay threshold of $100,000 per QALY. When using the covariate-adjusted ITT treatment effect, which represents the least biased estimate, however, cinacalcet is a cost-effective therapy for patients with moderate-to-severe sHPT on hemodialysis.

Methods for specifying the target difference in a randomised controlled trial: the Difference ELicitation in TriAls (DELTA) systematic review.

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.

Questionnaire to assess relevance and credibility of modeling studies for informing health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report.

The evaluation of the cost and health implications of agreeing to cover a new health technology is best accomplished using a model that mathematically combines inputs from various sources, together with assumptions about how these fit together and what might happen in reality. This need to make assumptions, the complexity of the resulting framework, the technical knowledge required, as well as funding by interested parties have led many decision makers to distrust the results of models. To assist stakeholders reviewing a model's report, questions pertaining to the credibility of a model were developed. Because credibility is insufficient, questions regarding relevance of the model results were also created. The questions are formulated such that they are readily answered and they are supplemented by helper questions that provide additional detail. Some responses indicate strongly that a model should not be used for decision making: these trigger a "fatal flaw" indicator. It is hoped that the use of this questionnaire, along with the three others in the series, will help disseminate what to look for in comparative effectiveness evidence, improve practices by researchers supplying these data, and ultimately facilitate their use by health care decision makers.

Projecting long-term graft and patient survival after transplantation.

OBJECTIVE: In spite of increases in short-term kidney transplant survival rates and reductions in acute rejection rates, increasing long-term graft survival rates remains a major challenge. The objective here was to project long-term graft- and survival-related outcomes occurring among renal transplant recipients based on short-term outcomes including acute rejection and estimated glomerular filtration rates observed in randomized trials. METHODS: We developed a two-phase decision model including a trial phase and a Markov state transition phase to project long-term outcomes over the lifetimes of hypothetical renal graft recipients who survived the trial period with a functioning graft. Health states included functioning graft stratified by level of renal function, failed graft, functioning regraft, and death. Transitions between health states were predicted using statistical models that accounted for renal function, acute rejection, and new-onset diabetes after transplant and for donor and recipient predictors of long-term graft and patient survival. Models were estimated using data from 38,015 renal transplant recipients from the United States Renal Data System. The model was populated with data from a 3-year, randomized phase III trial comparing belatacept to cyclosporine. RESULTS: The decision model was well calibrated with data from the United States Renal Data System. Long-term extrapolation of Belatacept Evaluation of Nephroprotection and Efficacy as Firstline Immunosuppression Trial was projected to yield a 1.9-year increase in time alive with a functioning graft and a 1.2 life-year increase over a 20-year time horizon. CONCLUSIONS: This is the first long-term follow-up model of renal transplant patients to be based on renal function, acute rejection, and new-onset diabetes. It is a useful tool for undertaking comparative effectiveness and cost-effectiveness studies of immunosuppressive medications.

Cost-effectiveness models for chronic obstructive pulmonary disease: cross-model comparison of hypothetical treatment scenarios.

OBJECTIVES: To compare different chronic obstructive pulmonary disease (COPD) cost-effectiveness models with respect to structure and input parameters and to cross-validate the models by running the same hypothetical treatment scenarios. METHODS: COPD modeling groups simulated four hypothetical interventions with their model and compared the results with a reference scenario of no intervention. The four interventions modeled assumed 1) 20% reduction in decline in lung function, 2) 25% reduction in exacerbation frequency, 3) 10% reduction in all-cause mortality, and 4) all these effects combined. The interventions were simulated for a 5-year and lifetime horizon with standardization, if possible, for sex, age, COPD severity, smoking status, exacerbation frequencies, mortality due to other causes, utilities, costs, and discount rates. Furthermore, uncertainty around the outcomes of intervention four was compared. RESULTS: Seven out of nine contacted COPD modeling groups agreed to participate. The 5-year incremental cost-effectiveness ratios (ICERs) for the most comprehensive intervention, intervention four, was €17,000/quality-adjusted life-year (QALY) for two models, €25,000 to €28,000/QALY for three models, and €47,000/QALY for the remaining two models. Differences in the ICERs could mainly be explained by differences in input values for disease progression, exacerbation-related mortality, and all-cause mortality, with high input values resulting in low ICERs and vice versa. Lifetime results were mainly affected by the input values for mortality. The probability of intervention four to be cost-effective at a willingness-to-pay value of €50,000/QALY was 90% to 100% for five models and about 70% and 50% for the other two models, respectively. CONCLUSIONS: Mortality was the most important factor determining the differences in cost-effectiveness outcomes between models.