Decision-analytic Modelling Approaches Research Articles

Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments.

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectivenessand future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

The Cost of Neurodevelopmental Disability: Scoping Review of Economic Evaluation Methods.

The provision of effective care models for children with neurodevelopmental delay or disability can be challenging in resource constrained healthcare systems. Economic evaluations have an important role in informing resource allocation decisions. This review systematically examined the scope and methods of economic models evaluating interventions for supporting neurodevelopment among children with common neurodevelopmental disorders and identified methods of economic models and presented policy implications. This scoping review employed the Arksey and O’Malley framework and aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Four electronic databases were systematically searched to identify eligible model-based economic evaluations of neurodevelopmental care models published since 2000. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist was used to assess quality of reporting. Data were systematically extracted, tabulated, and qualitatively synthesised across diagnostic categories. Searches identified 1431 unique articles. Twelve studies used a decision analytic model to evaluate care for neurodevelopmental disorders and were included in the review. Included studies focused on attention-deficit/hyperactivity disorder (ADHD, n=6), autism spectrum disorder (ASD, n=3), cerebral palsy (n=2), and dyslexia (n=1). The most used decision analytic modelling approach was a Markov model (n=6), followed by a decision tree (n=3), and a combination of decision tree and Markov model (n=3). Most studies (n=7) adopted a societal perspective for reporting costs. None of the reviewed studies modelled impact on families and caregivers. Four studies reported cost-savings, three identified greater quality of life, and three identified cost increases.

Additional offer of sigmoidoscopy in colorectal cancer screening in Germany: rationale and protocol of the decision-analytic modelling approach in the SIGMO study

IntroductionIn Germany, statutory insured persons are entitled to a stool test (faecal immunochemical test (FIT)) or colonoscopy for colorectal cancer (CRC) screening, depending on age and sex, yet participation rates...

A widely-applicable structural maintenance decision-analytic modelling approach assisted by information value computation

Improving the quality of maintenance decision-making under uncertainty is important for safety, reliability and cost management of marine assets. It is worth studying how condition inspections can be objective-oriented and inspection information be fully utilized in the maintenance decision-making process. Herein a new maintenance decision-analytic modelling approach assisted by value of information (VoI) computation is proposed and applied to a safety-critical marine structure. Three maintenance decision-making cases and three inspection strategies are tested. Below advantages of the proposed approach represent contribution to the current literature. The approach can yield optimal maintenance decisions in various decision-making cases, while condition based maintenance (CBM) is efficient only in the cases where inspections add value. The approach yields maintenance decisions associated with the same or lower life cycle costs (LCC) than decision rule-based approaches (e.g. CBM), and gives more improved decisions (associated with lower LCC) with more in-depth inspections. The advantages are benefited from VoI computation, decision-analytic modelling, and probabilistic modelling of uncertainty, crack growth and inspection outcomes. Maintenance decisions are derived based on integrated crack information via Bayesian updating, rather than only on inspection results. The approach can be used to optimize inspection and maintenance strategies for structural assets.

Quantitative Evidence Synthesis Methods for the Assessment of the Effectiveness of Treatment Sequences for Clinical and Economic Decision Making: A Review and Taxonomy of Simplifying Assumptions.

Sequential use of alternative treatments for chronic conditions represents a complex intervention pathway; previous treatment and patient characteristics affect both the choice and effectiveness of subsequent treatments. This paper critically explores the methods for quantitative evidence synthesis of the effectiveness of sequential treatment options within a health technology assessment (HTA) or similar process. It covers methods for developing summary estimates of clinical effectiveness or the clinical inputs for the cost-effectiveness assessment and can encompass any disease condition. A comprehensive review of current approaches is presented, which considers meta-analytic methods for assessing the clinical effectiveness of treatment sequences and decision-analytic modelling approaches used to evaluate the effectiveness of treatment sequences. Estimating the effectiveness of a sequence of treatments is not straightforward or trivial and is severely hampered by the limitations of the evidence base. Randomised controlled trials (RCTs) of sequences were often absent or very limited. In the absence of sufficient RCTs of whole sequences, there is no single best way to evaluate treatment sequences; however, some approaches could be re-used or adapted, sharing ideas across different disease conditions. Each has advantages and disadvantages, and is influenced by the evidence available, extent of treatment sequences (number of treatment lines or permutations), and complexity of the decision problem. Due to the scarcity of data, modelling studies applied simplifying assumptions to data on discrete treatments. A taxonomy for all possible assumptions was developed, providing a unique resource to aid the critique of existing decision-analytic models.Electronic supplementary materialThe online version of this article (10.1007/s40273-020-00980-w) contains supplementary material, which is available to authorized users.

A cost effectiveness study into the detection of functionally significant coronary artery disease in patients with chronic coronary syndrome: a decision-analytic modelling approach

Abstract Background ESC guidelines recommend functional or anatomical imaging for stable coronary artery disease (CAD) diagnosis. We investigated cost-effective diagnostic strategies for CAD detection with invasive coronary angiography (ICA) and fractional flow reserve (FFR) as reference standard [1,2], using NHS reference costs. Methods Deterministic and probabilistic decision-analytic models for diagnostic strategies in low (25%), intermediate (50%) and high (75%) risk CAD were devised. Strategies: standalone or combined testing with computed tomographic coronary angiography (CTCA), stress echocardiography (SE), CT-FFR, single-photon emission computed tomography (SPECT), cardiac magnetic resonance (CMR), positron emission tomography (PET), ICA, and ICA-FFR. Proportion of correct diagnosis served as measure of clinical effectiveness. Incremental cost-effectiveness ratios were calculated for dominant strategies. Cost-effectiveness acceptability curves (CEAC) tested variation of cost-effectiveness threshold (CET). Results Base case (Table 1) consistent with probabilistic analysis (Figure 1 left). CEACs (Figure 1 right). Conclusions Direct ICA is not cost-effective. Functional testing has significant role in low/intermediate risk. CMR is cost-effective in all risk and most likely cost-effective in CETs <£10,000. ICA-FFR yields highest correct diagnoses in all at highest cost. Future long-term follow-up studies with quality of life measures are needed.

A Review of Recent Decision-Analytic Models Used to Evaluate the Economic Value of Cancer Treatments.

Cost-effectiveness analysis provides information on the potential value of new cancer treatments, which is particularly pertinent for decision makers as demand for treatment grows while healthcare budgets remain fixed. A range of decision-analytic modelling approaches can be used to estimate cost effectiveness. This study summarises the key modelling approaches considered in oncology, alongside their advantages and limitations. A review was conducted to identify single technology appraisals (STAs) submitted to the National Institute for Health and Care Excellence (NICE) and published papers reporting full economic evaluations of cancer treatments published within the last 5 years. The review was supplemented with the existing methods literature discussing cancer modelling. In total, 100 NICE STAs and 124 published studies were included. Partitioned-survival analysis (n = 54) and discrete-time state transition structures (n = 41) were the main structures submitted to NICE. Conversely, the published studies reported greater use of discrete-time state transition models (n = 102). Limited justification of model structure was provided by authors, despite an awareness in the existing literature that the model structure should be considered thoroughly and can greatly influence cost-effectiveness results. Justification for the choice of model structure was limited and studies would be improved with a thorough rationale for this choice. The strengths and weaknesses of each approach should be considered by future researchers. Alternative methods (such as multi-state modelling) are likely to be utilised more frequently in the future, and so justification of these more advanced methods is paramount to their acceptability to inform healthcare decision making.Electronic supplementary materialThe online version of this article (10.1007/s40258-019-00513-3) contains supplementary material, which is available to authorized users.

Evaluating Cost-Effectiveness Models for Pharmacologic Interventions in Adults with Heart Failure: A Systematic Literature Review.

BackgroundHeart failure (HF) is a well-recognized public health concern and imposes high economic and societal costs. Decision analytic models exist for evaluating the economic ramifications associated with HF. Despite this, studies that appraise these modelling approaches for augmenting best-practice decisions remain scarce.ObjectiveOur objective was to conduct a systematic literature review (SLR) of published economic models for the management of HF and describe their general and methodological features.MethodsThis SLR employed a combination of relevant search terms associated with HF, which were used in a number of databases, including MEDLINE, Embase, the National Health Service Economic Evaluation Database, Cost-Effectiveness Analysis Registry, ScHARR Health Utilities Database and Cochrane Library Database. A number of model features (i.e. model structure, specification, outcomes assessed, scenario and sensitivity analysis, key model drivers) were extracted and subsequently summarized.ResultsOf 64 publications retained, a selection of modelling approaches were identified, including Markov (n = 28), trial-based analytic (n = 22), discrete-event simulation (n = 6), survival analytic (n = 7) and decision-tree modelling (n = 1) approaches. The bulk of publications employed either a cost-utility (n = 27) or cost-effectiveness (n = 36) analysis and evaluated more than one study outcome, which typically included overall costs (n = 59), incremental cost-effectiveness ratios (n = 55), life-years gained (n = 48) and willingness-to-pay thresholds (n = 37). Most publications focused on patients with chronic HF (n = 40) and used New York Heart Association (NYHA) disease classifications to categorize patients and determine disease severity. Few (n = 19) publications documented the use of hospitalization states for modelling patient outcomes and associated costs. A quality assessment of the included publications revealed most articles demonstrated reasonable methodological value.ConclusionsWe identified numerous decision analytic modelling approaches for evaluating the cost effectiveness of pharmacologic treatments in HF. A Markov cohort model approach was most commonly used, and most models relied on NYHA classes as a proxy of HF severity, disease progression and prognosis.Electronic supplementary materialThe online version of this article (10.1007/s40273-018-0755-x) contains supplementary material, which is available to authorized users.

Impact of increasing treatment rates on cost-effectiveness of subcutaneous immunotherapy (SCIT) in respiratory allergy: a decision analytic modelling approach.

Specific immunotherapy is the only causal treatment in respiratory allergy. Due to high treatment cost and possible severe side effects subcutaneous immunotherapy (SCIT) is not indicated in all patients. Nevertheless, reported treatment rates seem to be low. This study aims to analyze the effects of increasing treatment rates of SCIT in respiratory allergy in terms of costs and quality-adjusted life years (QALYs). A state-transition Markov model simulates the course of disease of patients with allergic rhinitis, allergic asthma and both diseases over 10years including a symptom-free state and death. Treatment comprises symptomatic pharmacotherapy alone or combined with SCIT. The model compares two strategies of increased and status quo treatment rates. Transition probabilitiesare based on routine data. Costs are calculated from the societal perspective applying German unit costs to literature-derived resource consumption. QALYs are determined by translating the mean change in non-preference-based quality of life scores to a change in utility. Key parameters are subjected to deterministic sensitivity analyses. Increasing treatment rates is a cost-effective strategy with an incremental cost-effectiveness ratio (ICER) of 3484€/QALY compared to the status quo. The most influential parameters are SCIT discontinuation rates, treatment effects on the transition probabilities and cost of SCIT. Across all parameter variations, the best case leads to dominance of increased treatment rates while the worst case ICER is 34,315€/QALY. Excluding indirect cost leads to a twofold increase in the ICER. Measures to increase SCIT initiation rates should be implemented and also address improving adherence.

Selecting pH cut-offs for the safe verification of nasogastric feeding tube placement: a decision analytical modelling approach

ObjectivesThe existing British National Patient Safety Agency (NPSA) safety guideline recommends testing the pH of nasogastric (NG) tube aspirates. Feeding is considered safe if a pH of 5.5 or lower...

Decision Analytic Modelling Methods For The Assessing The Effectiveness Of Treatment Sequences For Clinical And Economic Decision Making: A Methodological Review

Appropriate recognition of treatment sequencing is crucial to many policy decisions and related economic evaluations. The decision problem can become complex when accounting for extensive treatment sequences and pertinent factors relating to the disease. A methodological review was conducted to identify the breadth of decision analytic modelling approaches used for evaluating treatment sequences. A comprehensive literature review of MEDLINE, Embase and Cochrane library. Studies were categorised according to modelling approach and decision problem. Treatment sequencing assumptions were analysed. 70 studies and 48 discrete models were included. A wide range of modelling techniques were identified: cohort-based models (deterministic and stochastic decision trees, Markov, semi-Markov, partitioned survival); individual sampling models (state transition, and discrete event simulation (DES)); and open population-based models (DES and Markov cohort). No study systematically tested different modelling approaches for treatment sequences. Cohort models can be simple and easy to implement. Examples of cohort models adapted successfully to accommodate additional complexity were identified, but these were no longer simple. Individual sampling models are more sophisticated, better able to accommodate greater decision problem complexity, and provide more flexibility. DES appeared the optimum approach in terms of intuitively modelling sequencing algorithms, computational efficacy, and ease of updating, but requires more extensive modelling skills, specialist software, and is data and time intensive. In the absence of sequencing trials, modellers often applied simplifying assumptions to treatment effects obtained from trials of single treatments. These assumptions were frequently not validated, nor their impact assessed: an important limitation of these models. Modelling treatment sequences may require a complex model, which can be time consuming to develop and implement. Using simplistic assumptions, regarding sequencing effects, results in significant uncertainty around the effectiveness and cost-effectiveness estimates, the extent of which is generally unknown. This needs to be recognised in decision making, and further evaluated.

From accuracy to patient outcome and cost-effectiveness evaluations of diagnostic tests and biomarkers: an exemplary modelling study

BackgroundProper evaluation of new diagnostic tests is required to reduce overutilization and to limit potential negative health effects and costs related to testing. A decision analytic modelling approach may be worthwhile when a diagnostic randomized controlled trial is not feasible. We demonstrate this by assessing the cost-effectiveness of modified transesophageal echocardiography (TEE) compared with manual palpation for the detection of atherosclerosis in the ascending aorta.MethodsBased on a previous diagnostic accuracy study, actual Dutch reimbursement data, and evidence from literature we developed a Markov decision analytic model. Cost-effectiveness of modified TEE was assessed for a life time horizon and a health care perspective. Prevalence rates of atherosclerosis were age-dependent and low as well as high rates were applied. Probabilistic sensitivity analysis was applied.ResultsThe model synthesized all available evidence on the risk of stroke in cardiac surgery patients. The modified TEE strategy consistently resulted in more adapted surgical procedures and, hence, a lower risk of stroke and a slightly higher number of life-years. With 10% prevalence of atherosclerosis the incremental cost-effectiveness ratio was €4,651 and €481 per quality-adjusted life year in 55-year-old men and women, respectively. In all patients aged 65 years or older the modified TEE strategy was cost saving and resulted in additional health benefits.ConclusionsDecision analytic modelling to assess the cost-effectiveness of a new diagnostic test based on characteristics, costs and effects of the test itself and of the subsequent treatment options is both feasible and valuable. Our case study on modified TEE suggests that it may reduce the risk of stroke in cardiac surgery patients older than 55 years at acceptable cost-effectiveness levels.

PMD84 Assessing Cost Effectiveness and Value of Further Research When Data are Sparse: Negative Pressure Wound Therapy for Severe Pressure Ulcers

Health care resources are scarce and decisions have to be made about how to allocate funds. Often these decisions are based on sparse or imperfect evidence. One such example is negative pressure wound therapy (NPWT), which is a widely used treatment for severe pressure ulcers; however, there is currently no robust evidence that it is effective or cost effective. This work considers the decision to adopt NPWT given a range of alternative treatments, using a decision analytic modelling approach. Literature searches were conducted to identify existing evidence on model parameters. Given the limited evidence base, a second source of evidence, beliefs elicited from experts, was used. Judgements from experts on relevant (uncertain) quantities were obtained through a formal elicitation exercise. Additionally, data derived from a pilot trial were also used to inform the model. The three sources of evidence were collated, and the impact of each on cost effectiveness was evaluated. Negative pressure wound therapy was expected to be less costly and more effective than any other treatments. The decision to adopt NPWT was however very uncertain (probability of being cost effective of 0.55). The expected value of perfect information for the relevant UK population was approximately £98 million. Specifically, the results suggest that a study evaluating the effectiveness of NPWT might be worthwhile. The trial design that offered most value was a three armed trial, with follow-up of at least 1 year and approximately 500 participants per arm. The analyses presented demonstrate how allocation decisions about medical technologies can be explicitly informed when data are sparse and, how this kind of analyses can be used to guide future research prioritisation, not only indicating whether further research is worthwhile but what type of research is needed and how it should be designed.

Decision-Analytic Models to Simulate Health Outcomes and Costs in Heart Failure

Chronic heart failure (CHF) is a critical public health issue with increasing effect on the healthcare budgets of developed countries. Various decision-analytic modelling approaches exist to estimate the cost effectiveness of health technologies for CHF. We sought to systematically identify these models and describe their structures. We performed a systematic literature review in MEDLINE/PreMEDLINE, EMBASE, EconLit and the Cost-Effectiveness Analysis Registry using a combination of search terms for CHF and decision-analytic models. The inclusion criterion required 'use of a mathematical model evaluating both costs and health consequences for CHF management strategies'. Studies that were only economic evaluations alongside a clinical trial or that were purely descriptive studies were excluded. We identified 34 modelling studies investigating different interventions including screening (n = 1), diagnostics (n = 1), pharmaceuticals (n = 15), devices (n = 13), disease management programmes (n = 3) and cardiac transplantation (n = 1) in CHF. The identified models primarily focused on middle-aged to elderly patients with stable but progressed heart failure with systolic left ventricular dysfunction. Modelling approaches varied substantially and included 27 Markov models, three discrete-event simulation models and four mathematical equation sets models; 19 studies reported QALYs. Three models were externally validated. In addition to a detailed description of study characteristics, the model structure and output, the manuscript also contains a synthesis and critical appraisal for each of the modelling approaches. Well designed decision models are available for the evaluation of different CHF health technologies. Most models depend on New York Heart Association (NYHA) classes or number of hospitalizations as proxy for disease severity and progression. As the diagnostics and biomarkers evolve, there is the hope for better intermediate endpoints for modelling disease progression as those that are currently in use all have limitations.

The cost effectiveness of screening for genital Chlamydia trachomatis infection in Australia

In Australia, there is no published study on the cost effectiveness of screening for chlamydia. The aim of this study was to examine the cost effectiveness of a hypothetical screening programme for chlamydia based on annual opportunistic testing of all women 25 years of age or younger consulting a general practitioner, compared with no screening. A decision-analytic modelling approach was used to determine the incremental cost effectiveness ratio (ICER) of screening compared with no screening over 25 years. The analysis measured Australian health-care costs and benefits were assessed in terms of quality-adjusted life years (QALYs). The analysis resulted in a cost per QALY of 2968 dollars for screening. One-way sensitivity analyses on all variables, and multi-way sensitivity analyses on some variables, showed a wide range for the cost effectiveness, from dominance (where screening is effective and saves money overall) to an ICER of 67,715 dollars per QALY. The results indicate that annual opportunistic screening for chlamydia in women under 25 is a potentially worthwhile undertaking. However, the analysis also highlights uncertainties around the natural history of chlamydia and the effectiveness of chlamydia screening. Given these uncertainties, the need for further primary data collection in these areas becomes apparent.