New International Pharmaco-Economic Collaboration on Alzheimer's Disease (IPECAD) Open-Source Model Framework for the Health Technology Assessment of Early Alzheimer's Disease Treatment: Development and Use Cases.

OBJECTIVES: Reimbursement decisions for new Alzheimer's disease (AD) treatments are informed by economic evaluations. An open-source model with intuitive structure for model cross-validation can support the transparency and credibility of such evaluations. We describe the new International Pharmaco-Economic Collaboration on Alzheimer's Disease (IPECAD) open-source model framework (version 2) for the health-economic evaluation of early AD treatment and use it for cross-validation and addressing uncertainty. METHODS: A cohort state-transition model using a categorized composite domain (cognition and function) was developed by replicating an existing reference model and testing it for internal validity. Then, features of existing Institute for Clinical and Economic Review (ICER) and Alzheimer's Disease Archimedes Condition-Event Simulator (AD-ACE) models assessing lecanemab treatment were implemented for model cross-validation. Additional uncertainty scenarios were performed on choice of efficacy outcome from trial, natural disease progression, treatment effect waning and stopping rules, and other methodological choices. The model is available open-source as R code, spreadsheet, and web-based version via https://github.com/ronhandels/IPECAD. RESULTS: In the IPECAD model incremental life-years, quality-adjusted life-years (QALY) gains and cost savings were 21% to 31% smaller compared with the ICER model and 36% to 56% smaller compared with the AD-ACE model. IPECAD model results were particularly sensitive to assumptions on treatment effect waning and stopping rules and choice of efficacy outcome from trial. CONCLUSIONS: We demonstrated the ability of a new IPECAD open-source model framework for researchers and decision makers to cross-validate other (Health Technology Assessment submission) models and perform additional uncertainty analyses, setting an example for open science in AD decision modeling and supporting important reimbursement decisions.

The value of maintaining cognition in patients with mild cognitive impairment: The innovation headroom and potential cost-effectiveness of roflumilast.

INTRODUCTION: Early health-technology assessment can support discussing scarce resource allocation among stakeholders. We explored the value of maintaining cognition in patients with mild cognitive impairment (MCI) by estimating: (1) the innovation headroom and (2) the potential cost effectiveness of roflumilast treatment in this population. METHODS: The innovation headroom was operationalized by a fictive 100% efficacious treatment effect, and the roflumilast effect on memory word learning test was assumed to be associated with 7% relative risk reduction of dementia onset. Both were compared to Dutch setting usual care using the adapted International Pharmaco-Economic Collaboration on Alzheimer's Disease (IPECAD) open-source model. RESULTS: The total innovation headroom expressed as net health benefit was 4.2 (95% bootstrap interval: 2.9-5.7) quality-adjusted life years (QALYs). The potential cost effectiveness of roflumilast was k€34 per QALY. DISCUSSION: The innovation headroom in MCI is substantial. Although the potential cost effectiveness of roflumilast treatment is uncertain, further research on its effect on dementia onset is likely valuable.

Cost-effectiveness models for Alzheimer's disease and related dementias: IPECAD modeling workshop cross-comparison challenge.

INTRODUCTION: The credibility of model-based economic evaluations of Alzheimer's disease (AD) interventions is central to appropriate decision-making in a policy context. We report on the International PharmacoEconomic Collaboration on Alzheimer's Disease (IPECAD) Modeling Workshop Challenge. METHODS: Two common benchmark scenarios, for the hypothetical treatment of AD mild cognitive impairment (MCI) and mild dementia, were developed jointly by 29 participants. Model outcomes were summarized, and cross-comparisons were discussed during a structured workshop. RESULTS: A broad concordance was established among participants. Mean 10-year restricted survival and time in MCI in the control group ranged across 10 MCI models from 6.7 to 9.5 years and 3.4 to 5.6 years, respectively; and across 4 mild dementia models from 5.4 to 7.9 years (survival) and 1.5 to 4.2 years (mild dementia). DISCUSSION: The model comparison increased our understanding of methods, data used, and disease progression. We established a collaboration framework to assess cost-effectiveness outcomes, an important step toward transparent and credible AD models.

On the role of data, statistics and decisions in a pandemic.

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Decision-analytic modeling for early health technology assessment of medical devices - a scoping review.

Objective: The goal of this review was to identify decision-analytic modeling studies in early health technology assessments (HTA) of high-risk medical devices, published over the last three years, and to provide a systematic overview of model purposes and characteristics. Additionally, the aim was to describe recent developments in modeling techniques. Methods: For this scoping review, we performed a systematic literature search in PubMed and Embase including studies published in English or German. The search code consisted of terms describing early health technology assessment and terms for decision-analytic models. In abstract and full-text screening, studies were excluded that were not modeling studies for a high-risk medical device or an in-vitro diagnostic test. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram was used to report on the search and exclusion of studies. For all included studies, study purpose, framework and model characteristics were extracted and reported in systematic evidence tables and a narrative summary. Results: Out of 206 identified studies, 19 studies were included in the review. Studies were either conducted for hypothetical devices or for existing devices after they were already available on the market. No study extrapolated technical data from early development stages to estimate potential value of devices in development. All studies except one included cost as an outcome. Two studies were budget impact analyses. Most studies aimed at adoption and reimbursement decisions. The majority of studies were on in-vitro diagnostic tests for personalized and targeted medicine. A timed automata model, to our knowledge a model type new to HTA, was tested by one study. It describes the agents in a clinical pathway in separate models and, by allowing for interaction between the models, can reflect complex individual clinical pathways and dynamic system interactions. Not all sources of uncertainty for in-vitro tests were explicitly modeled. Elicitation of expert knowledge and judgement was used for substitution of missing empirical data. Analysis of uncertainty was the most valuable strength of decision-analytic models in early HTA, but no model applied sensitivity analysis to optimize the test positivity cutoff with regard to the benefit-harm balance or cost-effectiveness. Value-of-information analysis was rarely performed. No information was found on the use of causal inference methods for estimation of effect parameters from observational data. Conclusion: Our review provides an overview of the purposes and model characteristics of nineteen recent early evaluation studies on medical devices. The review shows the growing importance of personalized interventions and confirms previously published recommendations for careful modeling of uncertainties surrounding diagnostic devices and for increased use of value-of-information analysis. Timed automata may be a model type worth exploring further in HTA. In addition, we recommend to extend the application of sensitivity analysis to optimize positivity criteria for in-vitro tests with regard to benefit-harm or cost-effectiveness. We emphasize the importance of causal inference methods when estimating effect parameters from observational data.

Causal evidence in health decision making: methodological approaches of causal inference and health decision science.

Objectives: Public health decision making is a complex process based on thorough and comprehensive health technology assessments involving the comparison of different strategies, values and tradeoffs under uncertainty. This process must be based on best available evidence and plausible assumptions. Causal inference and health decision science are two methodological approaches providing information to help guide decision making in health care. Both approaches are quantitative methods that use statistical and modeling techniques and simplifying assumptions to mimic the complexity of the real world. We intend to review and lay out both disciplines with their aims, strengths and limitations based on a combination of textbook knowledge and expert experience. Methods: To help understanding and differentiating the methodological approaches of causal inference and health decision science, we reviewed both methods with the focus on aims, research questions, methods, assumptions, limitations and challenges, and software. For each methodological approach, we established a group of four experts from our own working group to carefully review and summarize each method, followed by structured discussion rounds and written reviews, in which the experts from all disciplines including HTA and medicine were involved. The entire expert group discussed objectives, strengths and limitations of both methodological areas, and potential synergies. Finally, we derived recommendations for further research and provide a brief outlook on future trends. Results: Causal inference methods aim for drawing causal conclusions from empirical data on the relationship of pre-specified interventions on a specific target outcome and apply a counterfactual framework and statistical techniques to derive causal effects of exposures or interventions from these data. Causal inference is based on a causal diagram, more specifically, a directed acyclic graph (DAG), which encodes the assumptions regarding the causal relations between variables. Depending on the type of confounding and selection bias, traditional statistical methods or more complex g-methods are needed to derive valid causal effects. Besides the correct specification of the DAG and the statistical model, assumptions such as consistency, positivity, and exchangeability must be checked when aiming at causal inference. Health decision science aims for guiding policy decision making regarding health interventions considering and balancing multiple competing objectives of a decision based on data from multiple sources and studies, for example prevalence studies, clinical trials and long-term observational routine effectiveness studies, and studies on preferences and costs. It involves decision analysis, a systematic, explicit and quantitative framework to guide decisions under uncertainty. Decision analyses are based on decision-analytic models to mimic the course of disease as well as aspects and consequences of the intervention in order to quantitatively optimize the decision. Depending on the type of decision problem, decision trees, state-transition models, discrete event simulation models, dynamic transmission models, or other model types are applied. Models must be validated against observed data, and comprehensive sensitivity analyses must be performed to assess uncertainty. Besides the appropriate choice of the model type and the valid specification of the model structure, it must be checked if input parameters of effects can be interpreted as causal parameters in the model. Otherwise results will be biased. Conclusions: Both causal inference and health decision science aim for providing best causal evidence for informed health decision making. The strengths and limitations of both methods differ and a good understanding of both methods is essential for correct application but also for correct interpretation of findings from the described methods. Importantly, decision-analytic modeling should be combined with causal inference when developing guidance and recommendations regarding decisions on health care interventions.

Probability bound analysis: A novel approach for quantifying parameter uncertainty in decision-analytic modeling and cost-effectiveness analysis.

Decisions about health interventions are often made using limited evidence. Mathematical models used to inform such decisions often include uncertainty analysis to account for the effect of uncertainty in the current evidence base on decision-relevant quantities. However, current uncertainty quantification methodologies, including probabilistic sensitivity analysis (PSA), require modelers to specify a precise probability distribution to represent the uncertainty of a model parameter. This study introduces a novel approach for representing and propagating parameter uncertainty, probability bounds analysis (PBA), where the uncertainty about the unknown probability distribution of a model parameter is expressed in terms of an interval bounded by lower and upper bounds on the unknown cumulative distribution function (p-box) and without assuming a particular form of the distribution function. We give the formulas of the p-boxes for common situations (given combinations of data on minimum, maximum, median, mean, or standard deviation), describe an approach to propagate p-boxes into a black-box mathematical model, and introduce an approach for decision-making based on the results of PBA. We demonstrate the characteristics and utility of PBA vs PSA using two case studies. In sum, this study provides modelers with practical tools to conduct parameter uncertainty quantification given the constraints of available data and with the fewest assumptions.

Targeted COVID-19 Vaccination (TAV-COVID) Considering Limited Vaccination Capacities-An Agent-Based Modeling Evaluation.

(1) Background: The Austrian supply of COVID-19 vaccine is limited for now. We aim to provide evidence-based guidance to the authorities in order to minimize COVID-19-related hospitalizations and deaths in Austria. (2) Methods: We used a dynamic agent-based population model to compare different vaccination strategies targeted to the elderly (65 ≥ years), middle aged (45-64 years), younger (15-44 years), vulnerable (risk of severe disease due to comorbidities), and healthcare workers (HCW). First, outcomes were optimized for an initially available vaccine batch for 200,000 individuals. Second, stepwise optimization was performed deriving a prioritization sequence for 2.45 million individuals, maximizing the reduction in total hospitalizations and deaths compared to no vaccination. We considered sterilizing and non-sterilizing immunity, assuming a 70% effectiveness. (3) Results: Maximum reduction of hospitalizations and deaths was achieved by starting vaccination with the elderly and vulnerable followed by middle-aged, HCW, and younger individuals. Optimizations for vaccinating 2.45 million individuals yielded the same prioritization and avoided approximately one third of deaths and hospitalizations. Starting vaccination with HCW leads to slightly smaller reductions but maximizes occupational safety. (4) Conclusion: To minimize COVID-19-related hospitalizations and deaths, our study shows that elderly and vulnerable persons should be prioritized for vaccination until further vaccines are available.

Cost-effectiveness Analysis of 99mTc-sestamibi SPECT/CT to Guide Management of Small Renal Masses.

BACKGROUND: Incidentally detected small renal masses (SRMs) may be one of several benign or malignant tumor histologies, and are heterogeneous in oncologic potential. Renal mass biopsy can be used to determine the histology of SRMs. However, this invasive approach has significant limitations. Technetium-99m sestamibi single photon emission computed tomography/computed tomography (99mTc-sestamibi SPECT/CT) is a promising imaging tool that can aid in identifying benign renal oncocytomas and hybrid oncocytic/chromophobe tumors. OBJECTIVE: To evaluate the clinical and economic value of 99mTc-sestamibi SPECT/CT in guiding the management of SRMs. DESIGN, SETTING, AND PARTICIPANTS: We developed a decision analysis model to estimate the costs and health outcomes of competing management strategies for a healthy 65-yr-old patient with an asymptomatic SRM. INTERVENTION: Empiric surgery (reference); real-world clinical practice (RWCP) consisting of empiric surgery, thermal ablation, and active surveillance (alternative reference); renal mass biopsy (option 1); 99mTc-sestamibi SPECT/CT (option 2); and 99mTc-sestamibi SPECT/CT followed by biopsy to confirm benign SRMs (option 3). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We assessed lifetime health utilities, measured in quality-adjusted life years (QALYs), and direct medical costs from a health payer perspective. We calculated the incremental cost-effectiveness ratio (ICER) for options 1-3 versus the reference and alternative reference arms, with a willingness-to-pay threshold of $50 000/QALY. Univariate, multivariate, and probabilistic sensitivity analyses were performed. RESULTS AND LIMITATIONS: Option 3 had a very low risk of untreated malignant tumors (0.2%, vs 2.1% for option 1, 4.2% for option 2, and 0% for empiric surgery) and the highest probability of leaving benign tumors untreated (84.4%, vs 53.9% for option 1, 51.7% for option 2, and 0% for empiric surgery). Option 3 dominated empiric surgery and options 1 and 2 (ie, lower costs and higher QALYs). Compared with RWCP, options 1-3 were all cost effective; option 3 had the lowest ICER of $18 821/QALY. These findings were robust to alternative input values. Study limitations included data uncertainties and a limited number of centers from which 99mTc-sestamibi SPECT/CT performance data were collected. CONCLUSIONS: 99mTc-sestamibi SPECT/CT followed by confirmatory biopsy helps avoid surgery for benign SRMs, minimizes untreated malignant SRMs, and is cost effective compared with existing strategies. PATIENT SUMMARY: Our research suggests that by using a noninvasive imaging test, known as technetium-99m sestamibi single photon emission computed tomography/computed tomography, to diagnose small renal masses, urologists may avoid unnecessary surgery for benign tumors and minimize the risk of leaving a malignant tumor untreated. Moreover, the use of this strategy to diagnose small renal masses is cost effective for the health care system.

Cost-effectiveness Analysis of Non-risk-adapted Active Surveillance for Postorchiectomy Management of Clinical Stage I Seminoma.

BACKGROUND: Cancer-specific survival for men with clinical stage I (CSI) seminoma approaches 100%, regardless of the management approach chosen after orchiectomy. Given the young age and high survival rate of these patients, there has been a shift toward minimizing treatment-related morbidity and cost. In this context, non-risk-adapted active surveillance (NRAS) has emerged as a desirable management strategy. OBJECTIVE: To evaluate the clinical, quality of life, and economic values of postorchiectomy NRAS for CSI seminoma. DESIGN, SETTING, AND PARTICIPANTS: We developed a decision analytic Markov model to estimate the costs and health outcomes of competing postorchiectomy management strategies for otherwise healthy 30-yr-old men with CSI seminoma. INTERVENTION: Real-world current practice, comprising active surveillance and adjuvant therapies (reference arm), was compared with empiric adjuvant radiotherapy (option 1), empiric adjuvant chemotherapy (option 2), risk-adapted active surveillance (RAAS; option 3), and NRAS (option 4). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Quality-adjusted life-years (QALYs), medical costs, incremental cost-effectiveness ratio, mortality, and unnecessary treatment avoidance were estimated over a 10-yr period. Uncertainties in model input values were accounted for using univariate, scenario, and probabilistic sensitivity analyses. RESULTS AND LIMITATIONS: NRAS dominated all other management options, offering the lowest per-patient health care cost ($3839) and the highest QALYs gained (7.74) over 10 yr. On probabilistic sensitivity analysis, NRAS had the highest chance of being most cost effective. Although NRAS resulted in the highest rate of salvage chemotherapy (20% vs 6% radiotherapy, 6% chemotherapy, 15% current practice, and 16% RAAS), it had the same mortality rate compared to current practice (2.5%). NRAS also allowed 80% of patients to avoid unnecessary treatment compared with 46% for current practice and 52% for RAAS. Study limitations included model simplifications, model parameter assumptions, as well as the absence of patient preference as a decision factor. CONCLUSIONS: NRAS maintains high cure rates for CSI seminoma, minimizes unnecessary treatment, and is cost effective compared with other management strategies. PATIENT SUMMARY: Clinical stage I (CSI) seminoma is one of the most common forms of testicular cancer. Surgery is the first step in the treatment of men with this disease, and some men may receive additional treatment with radiation or chemotherapy afterward. As most men are cured with surgery alone, non-risk-adapted active surveillance (NRAS), which involves routine monitoring with imaging and blood tests for disease recurrence after surgery, has become a desirable treatment option. Our study shows that in addition to maintaining high survival rates and avoiding unnecessary radiation and chemotherapy, NRAS is cost effective for the health care system.

ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA.

The novel Coronavirus disease (COVID-19) was first identified in Wuhan, China in December 2019 but later spread to other parts of the world. The disease as at the point of writing this paper has been declared a pandemic by the World Health Organization (WHO). The application of mathematical models, artificial intelligence, big data, and similar methodologies are potential tools to predict the extent of the spread and effectiveness of containment strategies to stem the transmission of this disease. In societies with constrained data infrastructures, modeling and forecasting COVID-19 becomes an extremely difficult endeavor. Nonetheless, we propose an online forecasting mechanism that streams data from the Nigeria Center for Disease Control to update the parameters of an ensemble model which in turn provides updated COVID-19 forecasts every 24 hours. The ensemble combines an Auto-Regressive Integrated Moving Average model (ARIMA), Prophet - an additive regression model developed by Facebook, and a Holt-Winters Exponential Smoothing model combined with Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The outcomes of these efforts are expected to provide academic thrust in guiding the policymakers in the deployment of containment strategies and/or assessment of containment interventions in stemming the spread of the disease in Nigeria.

Inverse Design Strategies for 3D Surfaces Formed by Mechanically Guided Assembly.

Deterministic transformations of 2D patterns of materials into well-controlled 3D mesostructures serve as the basis for manufacturing methods that can bypass limitations of conventional 3D micro/nanofabrication. Here, guided mechanical buckling processes provide access to a rich range of complex 3D mesostructures in high-performance materials, from inorganic and organic semiconductors, metals and dielectrics, to ceramics and even 2D materials (e.g., graphene, MoS2 ). Previous studies demonstrate that iterative computational procedures can define design parameters for certain targeted 3D configurations, but without the ability to address complex shapes. A technical need is in efficient, generalized inverse design algorithms that directly yield sets of optimized parameters. Here, such schemes are introduced, where the distributions of thicknesses across arrays of separated or interconnected ribbons provide scalable routes to 3D surfaces with a broad range of targeted shapes. Specifically, discretizing desired shapes into 2D ribbon components allows for analytic solutions to the inverse design of centrally symmetric and even general surfaces, in an approximate manner. Combined theoretical, numerical, and experimental studies of ≈20 different 3D structures with characteristic sizes (e.g., ribbon width) ranging from ≈200 µm to ≈2 cm and with geometries that resemble hemispheres, fire balloons, flowers, concave lenses, saddle surfaces, waterdrops, and rodents, illustrate the essential ideas.

Decision-Analytic Modeling Studies in Prevention and Treatment of Iodine Deficiency and Thyroid Disorders: A Systematic Overview.

Background: Prevention and treatment of iodine deficiency-related diseases remain an important public health challenge. Iodine deficiency can have severe health consequences, such as cretinism, goiter, or other thyroid disorders, and it has economic implications. Our aim was to give an overview of studies applying decision-analytic modeling to evaluate the effectiveness and/or cost-effectiveness of iodine deficiency-related prevention strategies or treatments related to thyroid disorders. Methods: We performed a systematic literature search in PubMed/MEDLINE (Medical Literature Analysis and Retrieval System Online), EMBASE (Excerpta Medica Database), Tuft's Cost-Effectiveness Analysis Registry, and National Health System Economic Evaluation Database (NHS EED) to identify studies published between 1985 and 2018 comparing different prevention or treatment strategies for iodine deficiency and thyroid disorders by applying a mathematical decision-analytic model. Studies were required to evaluate patient-relevant health outcomes (e.g., remaining life years, quality-adjusted life years [QALYs]). Results: Overall, we found 3950 studies. After removal of duplicates, abstract/title, and full-text screening, 17 studies were included. Eleven studies evaluated screening programs (mainly newborns and pregnant women), five studies focused on treatment approaches (Graves' disease, toxic thyroid adenoma), and one study was about primary prevention (consequences of iodine supplementation on offspring). Most of the studies were conducted within the U.S. health care context (n = 7). Seven studies were based on a Markov state-transition model, nine studies on a decision tree model, and in one study, an initial decision tree and a long-term Markov state-transition model were combined. The analytic time horizon ranged from 1 year to lifetime. QALYs were evaluated as health outcome measure in 15 of the included studies. In all studies, a cost-effectiveness analysis was performed. None of the models reported a formal model validation. In most cases, the authors of the modeling studies concluded that screening is potentially cost-effective or even cost-saving. The recommendations for treatment approaches were rather heterogeneous and depending on the specific research question, population, and setting. Conclusions: Overall, we predominantly identified decision-analytic modeling studies evaluating specific screening programs or treatment approaches; however, there was no model evaluating primary prevention programs on a population basis. Conclusions deriving from these studies, for example, that prevention is cost-saving, need to be carefully interpreted as they rely on many assumptions.

Early Economic Evaluation of Diagnostic Technologies: Experiences of the NIHR Diagnostic Evidence Co-operatives.

Diagnostic tests are expensive and time-consuming to develop. Early economic evaluation using decision modeling can reduce commercial risk by providing early evidence on cost-effectiveness. The National Institute for Health Research Diagnostic Evidence Co-operatives (DECs) was established to catalyze evidence generation for diagnostic tests by collaborating with commercial developers; DEC researchers have consequently made extensive use of early modeling. The aim of this article is to summarize the experiences of the DECs using early modeling for diagnostics. We draw on 8 case studies to illustrate the methods, highlight methodological strengths and weaknesses particular to diagnostics, and provide advice. The case studies covered diagnosis, screening, and treatment stratification. Treatment effectiveness was a crucial determinant of cost-effectiveness in all cases, but robust evidence to inform this parameter was sparse. This risked limiting the usability of the results, although characterization of this uncertainty in turn highlighted the value of further evidence generation. Researchers evaluating early models must be aware of the importance of treatment effect evidence when reviewing the cost-effectiveness of diagnostics. Researchers planning to develop an early model of a test should also 1) consult widely with clinicians to ensure the model reflects real-world patient care; 2) develop comprehensive models that can be updated as the technology develops, rather than taking a "quick and dirty" approach that may risk producing misleading results; and 3) use flexible methods of reviewing evidence and evaluating model results, to fit the needs of multiple decision makers. Decision models can provide vital information for developers at an early stage, although limited evidence mean researchers should proceed with caution.

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.

A Systematic Review of Decision-Analytic Models for Evaluating Cost-Effectiveness of Asthma Interventions.

OBJECTIVE: To demonstrate the landscape of model-based economic studies in asthma and highlight where there is room for improvement in the design and reporting of studies. DESIGN: A systematic review of the methodologies of model-based, cost-effectiveness analyses of asthma-related interventions was conducted. Models were evaluated for adherence to best-practice modeling and reporting guidelines and assumptions about the natural history of asthma. METHODS: A systematic search of English articles was performed in MEDLINE, EMBASE, and citations within reviewed articles. Studies were summarized and evaluated based on their adherence to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS). We also studied the underlying assumptions about disease progression, heterogeneity in disease course, comorbidity, and treatment effects. RESULTS: Forty-five models of asthma were included (33 Markov models, 10 decision trees, 2 closed-form equations). Novel biological treatments were evaluated in 12 studies. Some of the CHEERS' reporting recommendations were not satisfied, especially for models published in clinical journals. This was particularly the case for the choice of the modeling framework and reporting on heterogeneity. Only 13 studies considered any subgroups, and 2 explicitly considered the impact of comorbidities. Adherence to CHEERS requirements and the quality of models generally improved over time. CONCLUSION: It would be difficult to replicate the findings of contemporary model-based evaluations of asthma-related interventions given that only a minority of studies reported the essential parameters of their studies. Current asthma models generally lack consideration of disease heterogeneity and do not seem to be ready for evaluation of precision medicine technologies.

Modeling the Economic Impact of Interventions for Older Populations with Multimorbidity: A Method of Linking Multiple Single-Disease Models.

Introduction. Individuals from older populations tend to have more than 1 health condition (multimorbidity). Current approaches to produce economic evidence for clinical guidelines using decision-analytic models typically use a single-disease approach, which may not appropriately reflect the competing risks within a population with multimorbidity. This study aims to demonstrate a proof-of-concept method of modeling multiple conditions in a single decision-analytic model to estimate the impact of multimorbidity on the cost-effectiveness of interventions. Methods. Multiple conditions were modeled within a single decision-analytic model by linking multiple single-disease models. Individual discrete event simulation models were developed to evaluate the cost-effectiveness of preventative interventions for a case study assuming a UK National Health Service perspective. The case study used 3 diseases (heart disease, Alzheimer's disease, and osteoporosis) that were combined within a single linked model. The linked model, with and without correlations between diseases incorporated, simulated the general population aged 45 years and older to compare results in terms of lifetime costs and quality-adjusted life-years (QALYs). Results. The estimated incremental costs and QALYs for health care interventions differed when 3 diseases were modeled simultaneously (£840; 0.234 QALYs) compared with aggregated results from 3 single-disease models (£408; 0.280QALYs). With correlations between diseases additionally incorporated, both absolute and incremental costs and QALY estimates changed in different directions, suggesting that the inclusion of correlations can alter model results. Discussion. Linking multiple single-disease models provides a methodological option for decision analysts who undertake research on populations with multimorbidity. It also has potential for wider applications in informing decisions on commissioning of health care services and long-term priority setting across diseases and health care programs through providing potentially more accurate estimations of the relative cost-effectiveness of interventions.

Assessing cost-effectiveness of early intervention in Alzheimer's disease: An open-source modeling framework.

INTRODUCTION: We develop a framework to model disease progression across Alzheimer's disease (AD) and to assess the cost-effectiveness of future disease-modifying therapies (DMTs) for people with mild cognitive impairment (MCI) due to AD. METHODS: Using data from the US National Alzheimer's Coordinating Center, we apply survival analysis to estimate transition from predementia to AD dementia and ordered probit regression to estimate transitions across AD dementia stages. We investigate the cost-effectiveness of a hypothetical treatment scenario for people in MCI due to AD. RESULTS: We present an open-access model-based decision-analytic framework. Assuming a modest DMT treatment effect in MCI, we predict extended life expectancy and a reduction in time with AD dementia. DISCUSSION: Any future DMT for AD is expected to pose significant economic challenges across all health-care systems, and decision-analytic modeling will be required to assess costs and outcomes. Further developments are needed to inform these health policy considerations.

Implementing universal Lynch syndrome screening (IMPULSS): protocol for a multi-site study to identify strategies to implement, adapt, and sustain genomic medicine programs in different organizational contexts.

BACKGROUND: Systematic screening of all colorectal tumors for Lynch Syndrome (LS) has been recommended since 2009. Currently, implementation of LS screening in healthcare systems remains variable, likely because LS screening involves the complex coordination of multiple departments and individuals across the healthcare system. Our specific aims are to (1) describe variation in LS screening implementation across multiple healthcare systems; (2) identify conditions associated with both practice variation and optimal implementation; (3) determine the relative effectiveness, efficiency, and costs of different LS screening protocols by healthcare system; and (4) develop and test in a real-world setting an organizational toolkit for LS screening program implementation and improvement. This toolkit will promote effective implementation of LS screening in various complex health systems. METHODS: This study includes eight healthcare systems with 22 clinical sites at varied stages of implementing LS screening programs. Guided by the Consolidated Framework for Implementation Research (CFIR), we will conduct in-depth semi-structured interviews with patients and organizational stakeholders and perform economic evaluation of site-specific implementation costs. These processes will result in a comprehensive cross-case analysis of different organizational contexts. We will utilize qualitative data analysis and configurational comparative methodology to identify facilitators and barriers at the organizational level that are minimally sufficient and necessary for optimal LS screening implementation. DISCUSSION: The overarching goal of this project is to combine our data with theories and tools from implementation science to create an organizational toolkit to facilitate implementation of LS screening in various real-world settings. Our organizational toolkit will account for issues of complex coordination of care involving multiple stakeholders to enhance implementation, sustainability, and ongoing improvement of evidence-based LS screening programs. Successful implementation of such programs will ultimately reduce suffering of patients and their family members from preventable cancers, decrease waste in healthcare system costs, and inform strategies to facilitate the promise of precision medicine. TRIAL REGISTRATION: N/A.

Evaluating Alternative Designs of a Multilevel HIV Intervention in Maharashtra, India: The Impact of Stakeholder Constraints.

Background. Multilevel interventions combine individual component interventions, and their design can be informed by decision analysis. Our objective was to identify the optimal combination of interventions for alcohol-using HIV+ individuals on antiretroviral drug therapy in Maharashtra, India, explicitly considering stakeholder constraints. Methods. Using an HIV simulation, we evaluated the expected net monetary benefit (ENMB), the probability of lying on the efficiency frontier (PEF), and annual program costs of 5,836 unique combinations of 15 single-focused HIV risk-reduction interventions. We evaluated scenarios of 1) no constraints (i.e., maximize expected value), 2) short-term budget constraints (limits on annual programmatic costs of US$200,000 and $400,000), and 3) a constraint stemming from risk aversion (requiring that the strategy has >50% PEF). Results. With no constraints, the combination including long individual alcohol counseling, text-message adherence support, long group counseling for sex-risk, and long individual counseling for sex-risk (annual cost = $428,886; PEF ∼27%) maximized ENMB and would be the optimal design. With a cost constraint of $400,000, the combination including long individual alcohol counseling, text-message adherence support, brief group counseling for sex-risk, and long individual counseling for sex-risk (annual cost = $374,745; PEF ∼4%) maximized ENMB. With a cost constraint of $200,000, the combination including long individual alcohol counseling, text-message adherence support, and brief group counseling for sex-risk (annual cost = $187,335; PEF ∼54%) maximized ENMB. With the risk aversion constraint, the same configuration (long individual alcohol counseling, text-message support, and brief group counseling for sex-risk) maximized health benefit. Conclusion. Evaluating the costs, risks, and projected benefits of alternatives supports informed decision making prior to initiating study; however, stakeholder constraints should be explicitly included and discussed when using decision analyses to guide study design.