Simulation sample sizes for Monte Carlo partial EVPI calculations

Partial expected value of perfect information (EVPI) quantifies the value of removing uncertainty about unknown parameters in a decision model. EVPIs can be computed via Monte Carlo methods. An outer loop samples values of the parameters of interest, and an inner loop samples the remaining parameters from their conditional distribution. This nested Monte Carlo approach can result in biased estimates if small numbers of inner samples are used and can require a large number of model runs for accurate partial EVPI estimates. We present a simple algorithm to estimate the EVPI bias and confidence interval width for a specified number of inner and outer samples. The algorithm uses a relatively small number of model runs (we suggest approximately 600), is quick to compute, and can help determine how many outer and inner iterations are needed for a desired level of accuracy. We test our algorithm using three case studies. (C) 2010 Elsevier B.V. All rights reserved

Choice and judgement in developing models for health technology assessment; a qualitative study

Introduction: The role of models in supporting health policy decisions is reliant on model credibility. Credibility is fundamentally determined by the choices and judgements that people make in the process of developing a model. However, the method of uncovering choices and making judgements in model development is largely unreported and is not addressed by modelling methods guidance. Methods: This qualitative study was part of a project examining errors in health technology assessment models. In-depth interviews with academic and commercial modellers were used to obtain descriptions of the model development process. Data were analysed using framework analysis and interpreted in the context of the methodological literature. Results: The activities involved in developing models were characterised according to the themes; understanding the decision problem, conceptual modelling, model implementation, model checking, and engaging with the decision maker. Finding and using evidence was frequently mentioned across these themes. There was marked variation between practitioners in the extent to which conceptual modelling was recognised as an activity distinct from model implementation. Discussion: Methodological approaches to addressing model credibility described in the wider modelling literature highlight the necessity to disentangle the conceptual modelling and implementation activities. Whilst interviewees talked of judgements and choice making throughout model development, discussion indicated that these were based upon skills and experience with no discussion of formal approaches. Methods are required that provide for a systematic approach to uncovering choices, to generating a shared view of consensus and divergence, and for making judgements and choices in model development

Genetic therapeutic advancements for Dravet Syndrome

Dravet Syndrome is a genetic epileptic syndrome characterized by severe and intractable seizures associated with cognitive, motor, and behavioral impairments. The disease is also linked with increased mortality mainly due to sudden unexpected death in epilepsy. Over 80% of cases are due to a de novo mutation in one allele of the SCN1A gene, which encodes the α-subunit of the voltage-gated ion channel NaV1.1. Dravet Syndrome is usually refractory to antiepileptic drugs, which only alleviate seizures to a small extent. Viral, non-viral genetic therapy, and gene editing tools are rapidly enhancing and providing new platforms for more effective, alternative medicinal treatments for Dravet syndrome. These strategies include gene supplementation, CRISPR-mediated transcriptional activation, and the use of antisense oligonucleotides. In this review, we summarize our current knowledge of novel genetic therapies that are currently under development for Dravet syndrome

SPHR Diabetes Prevention Model: Detailed Description of Model Background, Methods, Assumptions and Parameters

Type-2 diabetes is a complex disease with multiple risk factors and health consequences whose prevention is a major public health priority. We have developed a microsimulation model written in the R programming language that can evaluate the effectiveness and cost-effectiveness of a comprehensive range of different diabetes prevention interventions, either in the general population or in subgroups at high risk of diabetes. Within the model individual patients with different risk factors for diabetes follow metabolic trajectories (for body mass index, cholesterol, systolic blood pressure and glycaemia), develop diabetes, complications of diabetes and related disorders including cardiovascular disease and cancer, and eventually die. Lifetime costs and quality-adjusted life-years are collected for each patient. The model allows assessment of the wider social impact on employment and the equity impact of different interventions. Interventions may be population-based, community-based or individually targeted, and administered singly or layered together. The model is fully enabled for probabilistic sensitivity analysis (PSA) to provide an estimate of decision uncertainty. This discussion paper provides a detailed description of the model background, methods and assumptions, together with details of all parameters used in the model, their sources and distributions for PSA

Impact of Type 2 diabetes prevention programmes based on risk identification and lifestyle intervention intensity strategies: a cost-effectiveness analysis

Aim To develop a cost-effectiveness model to compare Type 2 diabetes prevention programmes that target different at-risk population subgroups through lifestyle interventions of varying intensity. Methods An individual patient simulation model simulated the development of diabetes in a representative sample of adults without diabetes from the UK population. The model incorporates trajectories for HbA1c, 2-h glucose, fasting plasma glucose, BMI, systolic blood pressure, total cholesterol and HDL cholesterol. In the model, patients can be diagnosed with diabetes, cardiovascular disease, microvascular complications of diabetes, cancer, osteoarthritis and depression, or can die. The model collects costs and utilities over a lifetime horizon. The perspective is the UK National Health Service and Personal Social Services. We used the model to evaluate the population-wide impact of targeting a lifestyle intervention of varying intensity to six population subgroups defined as at high risk for diabetes. Results The intervention produces 0.0020 to 0.0026 incremental quality-adjusted life-years and saves £15 to £23 per person in the general population, depending on the subgroup targeted. Cost-effectiveness increases with intervention intensity. The most cost-effective options were to target South-Asian people and those with HbA1c levels > 42 mmol/mol (6%). Conclusion The model indicates that diabetes prevention interventions are likely to be cost-saving. The criteria for selecting at-risk individuals differentially has an impact on diabetes and cardiovascular disease outcomes, and on the timing of costs and benefits. The model is not currently able to account for potential differential uptake or efficacy between subgroups. These findings have implications for deciding who should be targeted for diabetes prevention interventions.NIH

Calculating partial expected value of perfect information via Monte Carlo sampling algorithms

Partial expected value of perfect information (EVPI) calculations can quantify the value of learning about particular subsets of uncertain parameters in decision models. Published case studies have used different computational approaches. This article examines the computation of partial EVPI estimates via Monte Carlo sampling algorithms. The mathematical definition shows 2 nested expectations, which must be evaluated separately because of the need to compute a maximum between them. A generalized Monte Carlo sampling algorithm uses nested simulation with an outer loop to sample parameters of interest and, conditional upon these, an inner loop to sample remaining uncertain parameters. Alternative computation methods and shortcut algorithms are discussed and mathematical conditions for their use considered. Maxima of Monte Carlo estimates of expectations are biased upward, and the authors show that the use of small samples results in biased EVPI estimates. Three case studies illustrate 1) the bias due to maximization and also the inaccuracy of shortcut algorithms 2) when correlated variables are present and 3) when there is nonlinearity in net benefit functions. If relatively small correlation or nonlinearity is present, then the shortcut algorithm can be substantially inaccurate. Empirical investigation of the numbers of Monte Carlo samples suggests that fewer samples on the outer level and more on the inner level could be efficient and that relatively small numbers of samples can sometimes be used. Several remaining areas for methodological development are set out. A wider application of partial EVPI is recommended both for greater understanding of decision uncertainty and for analyzing research priorities

High-dose etoposide with granulocyte colony-stimulating factor for mobilization of peripheral blood progenitor cells: efficacy and toxicity at three dose levels.

High-dose etoposide (2.0-2.4 g m(-2)) with granulocyte colony-stimulating factor (G-CSF) is an effective strategy to mobilize peripheral blood progenitor cells (PBPCs), although in some patients this is associated with significant toxicity. Sixty-three patients with malignancy were enrolled into this non-randomized sequential study. The majority (55/63, 87%) had received at least two prior regimens of chemotherapy, and seven patients had previously failed to mobilize following high-dose cyclophosphamide with G-CSF. Consecutive patient groups received etoposide at three dose levels [2.0 g m(-2) (n = 22), 1.8 g m(-2) (n = 20) and 1.6 g m(-2) (n = 21)] followed by daily G-CSF. Subsequent leukaphereses were assayed for CD34+ cell content, with a target total collection of 2.0 x 10(6) CD34+ cells kg(-1). Toxicity was assessed by the development of significant mucositis, the requirement for parenteral antibiotics or blood component support and rehospitalization incidence. Ten patients (16%) had less than the minimum target yield collected. Median collections in the three groups were 4.7 (2 g m(-2)), 5.7 (1.8 g m(-2)) and 6.5 (1.6 g m(-2)) x 10(6) CD34+ cells kg(-1). Five of the seven patients who had previously failed cyclophosphamide mobilization achieved more than the target yield. Rehospitalization incidence was significantly lower in patients receiving 1.6 g m(-2) etoposide than in those receiving 2.0 g m(-2) (P = 0.03). These data suggest that high-dose etoposide with G-CSF is an efficient mobilization regimen in the majority of heavily pretreated patients, including those who have previously failed on high-dose cyclophosphamide with G-CSF. An etoposide dose of 1.6 g m(-2) appears to be as effective as higher doses but less toxic

Effectiveness and cost-effectiveness of an educational intervention for practice teams to deliver problem focused therapy for insomnia: rationale and design of a pilot cluster randomised trial

Background: Sleep problems are common, affecting over a third of adults in the United Kingdom and leading to reduced productivity and impaired health-related quality of life. Many of those whose lives are affected seek medical help from primary care. Drug treatment is ineffective long term. Psychological methods for managing sleep problems, including cognitive behavioural therapy for insomnia (CBTi) have been shown to be effective and cost effective but have not been widely implemented or evaluated in a general practice setting where they are most likely to be needed and most appropriately delivered. This paper outlines the protocol for a pilot study designed to evaluate the effectiveness and cost-effectiveness of an educational intervention for general practitioners, primary care nurses and other members of the primary care team to deliver problem focused therapy to adult patients presenting with sleep problems due to lifestyle causes, pain or mild to moderate depression or anxiety. Methods and design: This will be a pilot cluster randomised controlled trial of a complex intervention. General practices will be randomised to an educational intervention for problem focused therapy which includes a consultation approach comprising careful assessment (using assessment of secondary causes, sleep diaries and severity) and use of modified CBTi for insomnia in the consultation compared with usual care (general advice on sleep hygiene and pharmacotherapy with hypnotic drugs). Clinicians randomised to the intervention will receive an educational intervention (2 × 2 hours) to implement a complex intervention of problem focused therapy. Clinicians randomised to the control group will receive reinforcement of usual care with sleep hygiene advice. Outcomes will be assessed via self-completion questionnaires and telephone interviews of patients and staff as well as clinical records for interventions and prescribing. Discussion: Previous studies in adults have shown that psychological treatments for insomnia administered by specialist nurses to groups of patients can be effective within a primary care setting. This will be a pilot study to determine whether an educational intervention aimed at primary care teams to deliver problem focused therapy for insomnia can improve sleep management and outcomes for individual adult patients presenting to general practice. The study will also test procedures and collect information in preparation for a larger definitive cluster-randomised trial. The study is funded by The Health Foundation