Expected Value Of Partial Perfect Information Research Articles

Cost-utility and value of information analysis of tisagenlecleucel for relapsed/refractory diffuse large B-cell lymphoma in the Irish healthcare setting

ABSTRACT Background The evidence base of tisagenlecleucel is uncertain. Objective To evaluate the cost-effectiveness of tisagenlecleucel. To conduct expected value of perfect information (EVPI) and partial EVPI (EVPPI) analyses. Study Design A three-state partitioned survival model. A short-term decision tree partitioned patients in the tisagenlecleucel arm according to infusion status. Survival was extrapolated to 5 years; general population mortality with a standardised mortality ratio was then applied. EVPI and EVPPI were scaled up to population according to the incidence of the decision. Setting Irish healthcare payer. Participants Patients with relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL). Interventions Tisagenlecleucel versus Salvage Chemotherapy (with or without haematopoietic stem cell transplant). Main Outcome Measure Incremental cost-effectiveness ratio (ICER). Population EVPI and EVPPI. Results At list prices, the ICER was €119,509 per quality-adjusted life year (QALY) (incremental costs €218,092; incremental QALYs 1.82). Probability of cost-effectiveness, at a €45,000 per QALY threshold, was 0%. Population EVPI was €0.00. Population EVPI, at the price of tisagenlecleucel that reduced the ICER to €45,000 per QALY, was €3,989,438. Here, survival analysis had the highest population EVPPI (€1,128,053). Conclusion Tisagenlecleucel is not cost-effective, versus salvage chemotherapy (with or without haematopoietic stem cell transplant), for R/R DLBCL in Ireland. At list prices, further research to decrease decision uncertainty may not be of value.

PRIORITIZING FUTURE RESEARCH ON NONDRUG DEMENTIA CARE INTERVENTIONS

Abstract We previously used an evidence-based mathematical model to evaluate the cost-effectiveness of psychosocial interventions that reduce the risk of a nursing home admission for people with dementia from a healthcare payer perspective. We found the incremental cost-effectiveness of MIND, an in-home intervention for people with mild-moderate dementia, compared to usual care was $271,456 per quality-adjusted life-year (QALY). The incremental cost-effectiveness of NYU Caregiver Intervention (NYUCI), which is for people with moderate dementia, compared to usual care was $3,964/QALY. Here we quantify the uncertainty around our cost-effectiveness estimates. First, we calculated the expected value of partial perfect information (EVPPI), which is the value of eliminating uncertainty around the treatment effect (i.e., risk of entering a nursing home) of MIND and NYUCI, and represents the maximum willingness-to-pay for a study to inform these estimates. Given a willingness-to-pay of $110,000/QALY, population EVPPI for MIND and the NYUCI were $81,000,000 and $395,000,000, respectively. Second, we calculated the expected value of sample information (EVSI), the expected net benefit of sampling (ENBS) and the optimal sample size (OSS). EVSI is the amount of uncertainty reduced from a pragmatic trial evaluating the risk of entering a nursing home for people in the intervention compared to usual care. ENBS is the return of a pragmatic trial with a fixed ($1,050,000) and per person ($2,000) cost to conduct the study. The OSS is the sample size that maximizes ENBS and was 3,571 for MIND and 5,357 for NYUCI. There is value in conducting pragmatic trials on MIND and NYUCI.

Estimated Monetary Value of Future Research Clarifying Uncertainties Around the Optimal Adult Hearing Screening Schedule

Adult hearing screening is not routinely performed, and most individuals with hearing loss (HL) have never had their hearing tested as adults. To project the monetary value of future research clarifying uncertainties around the optimal adult hearing screening schedule. In this economic evaluation, a validated decision model of HL (DeciBHAL-US: Decision model of the Burden of Hearing loss Across the Lifespan) was used to simulate current detection and treatment of HL vs hearing screening schedules. Key model inputs included HL incidence (0.06%-10.42%/y), hearing aid uptake (0.54%-8.14%/y), screening effectiveness (1.62 × hearing aid uptake), utility benefits of hearing aids (+0.11), and hearing aid device costs ($3690). Distributions to model parameters for probabilistic uncertainty analysis were assigned. The expected value of perfect information (EVPI) and expected value of partial perfect information (EVPPI) using a willingness to pay of $100 000 per quality-adjusted life-year (QALY) was estimated. The EVPI and EVPPI estimate the upper bound of the dollar value of future research. This study was based on 40-year-old persons over their remaining lifetimes in a US primary care setting. Screening schedules beginning at ages 45, 55, 65, and 75 years, and frequencies of every 1 or 5 years. The main outcomes were QALYs and costs (2020 US dollars) from a health system perspective. The average incremental cost-effectiveness ratio for yearly screening beginning at ages 55 to 75 years ranged from $39 200 to $80 200/QALY. Yearly screening beginning at age 55 years was the optimal screening schedule in 38% of probabilistic uncertainty analysis simulations. The population EVPI, or value of reducing all uncertainty, was $8.2 to $12.6 billion varying with willingness to pay and the EVPPI, or value of reducing all screening effectiveness uncertainty, was $2.4 billion. In this economic evaluation of US adult hearing screening, large uncertainty around the optimal adult hearing screening schedule was identified. Future research on hearing screening has a high potential value so is likely justified.

Tisagenlecleucel for relapsed/refractory acute lymphoblastic leukemia in the Irish healthcare setting: cost-effectiveness and value of information analysis.

This study evaluates the cost-effectiveness of tisagenlecleucel (a CAR T-cell therapy), versus blinatumomab, for the treatment of pediatric and young adult patients with relapsed/refractory acute lymphoblastic leukemia (R/R ALL) in the Irish healthcare setting. The value of conducting further research, to investigate the value of uncertainty associated with the decision problem, is assessed by means of expected value of perfect information (EVPI) and partial EVPI (EVPPI) analyses. A three-state partitioned survival model was developed. A short-term decision tree partitioned patients in the tisagenlecleucel arm according to infusion status. Survival was extrapolated to 60months; general population mortality with a standardized mortality ratio was then applied. Estimated EVPI and EVPPI were scaled up to population according to the incidence of the decision. At list prices, the incremental cost-effectiveness ratio was EUR 73,086 per quality-adjusted life year (QALY) (incremental costs EUR 156,928; incremental QALYs 2.15). The probability of cost-effectiveness, at the willingness-to-pay threshold of EUR 45,000 per QALY, was 16 percent. At this threshold, population EVPI was EUR 314,455; population EVPPI was below EUR 100,000 for each parameter category. Tisagenlecleucel is not cost effective, versus blinatumomab, for the treatment of pediatric and young adult patients with R/R ALL in Ireland (at list prices). Further research to decrease decision (parameter) uncertainty, at the defined willingness-to-pay threshold, may not be of value. However, there is a high degree of uncertainty underpinning the analysis, which may not be captured by EVPI analysis.

Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information.

The expected value of partial perfect information (EVPPI) provides an upper bound on the value of collecting further evidence on a set of inputs to a cost-effectiveness decision model. Standard Monte Carlo estimation of EVPPI is computationally expensive as it requires nested simulation. Alternatives based on regression approximations to the model have been developed but are not practicable when the number of uncertain parameters of interest is large and when parameter estimates are highly correlated. The error associated with the regression approximation is difficult to determine, while MC allows the bias and precision to be controlled. In this article, we explore the potential of quasi Monte Carlo (QMC) and multilevel Monte Carlo (MLMC) estimation to reduce the computational cost of estimating EVPPI by reducing the variance compared with MC while preserving accuracy. We also develop methods to apply QMC and MLMC to EVPPI, addressing particular challenges that arise where Markov chain Monte Carlo (MCMC) has been used to estimate input parameter distributions. We illustrate the methods using 2 examples: a simplified decision tree model for treatments for depression and a complex Markov model for treatments to prevent stroke in atrial fibrillation, both of which use MCMC inputs. We compare the performance of QMC and MLMC with MC and the approximation techniques of generalized additive model (GAM) regression, Gaussian process (GP) regression, and integrated nested Laplace approximations (INLA-GP). We found QMC and MLMC to offer substantial computational savings when parameter sets are large and correlated and when the EVPPI is large. We also found that GP and INLA-GP were biased in those situations, whereas GAM cannot estimate EVPPI for large parameter sets.

Is further research on adult pneumococcal vaccine uptake improvement programs worthwhile? Α value of information analysis

BackgroundPneumococcal vaccination policy for US adults is evolving, but previous research has shown that programs to increase vaccine uptake are economically favorable, despite parameter uncertainty. Using value of information (VOI) analysis and prior analyses, we examine the value of further research on vaccine uptake program parameters. MethodsIn US 50–64-year-olds, current vaccine recommendations with and without an uptake program were analyzed. In older adults, current recommendations and an alternative strategy (polysaccharide vaccine for all, adding conjugate vaccine only for the immunocompromised) with and without uptake programs were examined. Uptake program parameters were derived from a clinical trial (absolute uptake increase 12.3% [range 0–25%], per-person cost $1.78 [range $0.70–$2.26]), with other parameters obtained from US databases. VOI analyses incorporated probabilistic sensitivity analysis outputs into R-based regression techniques. ResultsIn 50–64-year-olds, an uptake program cost $54,900/QALY gained compared to no uptake program. For ages ≥65, the program cost $287,000/QALY gained with the alternative strategy and $765,000/QALY with current recommendations. In younger adults, population-level expected value of perfect information (EVPI) was $59.7 million at $50,000/QALY gained and $2.8 million at $100,000/QALY gained. In older adults, EVPI values ranged from ~$1 million to $34.5 million at $100,000 and $200,000/QALY thresholds. The population expected value of partial perfect information (EVPPI) for combined uptake program cost and uptake improvement parameters in the younger population was $368,700 at $50,000/QALY and $43,900 at $100,000/QALY gained thresholds. In older adults, population EVPPI for vaccine uptake program parameters was $0 at both thresholds, reaching a maximum value of $445,000 at a $225,000/QALY threshold. Other model parameters comprised larger components of the global EVPI. ConclusionVOI results do not support further research on pneumococcal vaccine uptake programs in adults at commonly cited US cost-effectiveness benchmarks. Further research to reduce uncertainty in other aspects of adult pneumococcal vaccination is justifiable.

Value of Information Analytical Methods: Report 2 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force

The allocation of healthcare resources among competing priorities requires an assessment of the expected costs and health effects of investing resources in the activities and of the opportunity cost of the expenditure. To date, much effort has been devoted to assessing the expected costs and health effects, but there remains an important need to also reflect the consequences of uncertainty in resource allocation decisions and the value of further research to reduce uncertainty. Decision making with uncertainty may turn out to be suboptimal, resulting in health loss. Consequently, there may be value in reducing uncertainty, through the collection of new evidence, to better inform resource decisions. This value can be quantified using value of information (VOI) analysis. This report from the ISPOR VOI Task Force describes methods for computing 4 VOI measures: the expected value of perfect information, expected value of partial perfect information (EVPPI), expected value of sample information (EVSI), and expected net benefit of sampling (ENBS). Several methods exist for computing EVPPI and EVSI, and this report provides guidance on selecting the most appropriate method based on the features of the decision problem. The report provides a number of recommendations for good practice when planning, undertaking, or reviewing VOI analyses. The software needed to compute VOI is discussed, and areas for future research are highlighted.

Directly Acting Oral Anticoagulants for the Prevention of Stroke in Atrial Fibrillation in England and Wales: Cost-Effectiveness Model and Value of Information Analysis.

Objectives. Determine the optimal, licensed, first-line anticoagulant for prevention of ischemic stroke in patients with non-valvular atrial fibrillation (AF) in England and Wales from the UK National Health Service (NHS) perspective and estimate value to decision making of further research. Methods. We developed a cost-effectiveness model to compare warfarin (international normalized ratio target range 2–3) with directly acting (or non–vitamin K antagonist) oral anticoagulants (DOACs) apixaban 5 mg, dabigatran 150 mg, edoxaban 60 mg, and rivaroxaban 20 mg, over 30 years post treatment initiation. In addition to death, the 17-state Markov model included the events stroke, bleed, myocardial infarction, and intracranial hemorrhage. Input parameters were informed by systematic literature reviews and network meta-analysis. Expected value of perfect information (EVPI) and expected value of partial perfect information (EVPPI) were estimated to provide an upper bound on value of further research. Results. At willingness-to-pay threshold £20,000, all DOACs have positive expected incremental net benefit compared to warfarin, suggesting they are likely cost-effective. Apixaban has highest expected incremental net benefit (£7533), followed by dabigatran (£6365), rivaroxaban (£5279), and edoxaban (£5212). There was considerable uncertainty as to the optimal DOAC, with the probability apixaban has highest net benefit only 60%. Total estimated population EVPI was £17.94 million (17.85 million, 18.03 million), with relative effect between apixaban versus dabigatran making the largest contribution with EVPPI of £7.95 million (7.66 million, 8.24 million). Conclusions. At willingness-to-pay threshold £20,000, all DOACs have higher expected net benefit than warfarin but there is considerable uncertainty between the DOACs. Apixaban had the highest expected net benefit and greatest probability of having highest net benefit, but there is considerable uncertainty between DOACs. A head-to-head apixaban versus dabigatran trial may be of value.

Cost-effectiveness analysis of mechanical thrombectomy plus tissue-type plasminogen activator compared with tissue-type plasminogen activator alone for acute ischemic stroke in France

Background and purposeRecent studies demonstrated the benefit of mechanical thrombectomy (MT) plus intravenous tissue-type plasminogen activator (IV-tPA) (MT-IV-tPA) in acute ischemic stroke. This study aimed to estimate the cost-utility of MT-IV-tPA compared with IV-tPA alone from the perspective of the French National Health Insurance. MethodsWe developed a decision tree for the first 3 months after stroke onset and a Markov model until 10 years post-stroke. The health states of the Markov model were according to the modified Rankin Scale (mRS): independent (mRS=0-2), dependent (mRS=3-5), dead (mRS=6). Recurrent stroke was the fourth health stage of our model. We conducted systematic literature reviews and meta-analyses to estimate the cost and utility of each health state, and the transition probabilities between health states. A microcosting study was conducted to estimate the cost of MT. We estimated the incremental cost-effectiveness ratio of MT-IV-tPA and conducted a probabilistic analysis in order to estimate the probability that MT-IV-tPA is cost-effective compared to IV-tPA, the expected value of perfect information (EVPI), and the expected value of partial perfect information (EVPPI), given the uncertainty surrounding the value of our model's parameters. ResultsThe total mean (standard deviation (SD) cost of MT was €6708.9 (2357.0). The incremental cost-effectiveness ratio (ICER) of the strategy using IV-tPA combined to MT costs was €14,715 per QALY gained as compared to a strategy using IV-tPA alone. The probabilistic analysis showed that the probability of MT-IV-TPA being cost-effective was 85.4% at threshold willingness-to-pay of €30,000 per QALY gained, reaching 98% at €50,000 per QALY gained. ConclusionAlthough there is no universally accepted willingness-to-pay threshold in France, our analysis suggest that MT combined to IV-tPA can be considered a cost-effective treatment compared with IV-tPA alone.

Decision-making under uncertainty: using MLMC for efficient estimation of EVPPI

In this paper we develop a very efficient approach to the Monte Carlo estimation of the expected value of partial perfect information (EVPPI) that measures the average benefit of knowing the value of a subset of uncertain parameters involved in a decision model. The calculation of EVPPI is inherently a nested expectation problem, with an outer expectation with respect to one random variable $X$ and an inner conditional expectation with respect to the other random variable $Y$. We tackle this problem by using a Multilevel Monte Carlo (MLMC) method (Giles 2008) in which the number of inner samples for $Y$ increases geometrically with level, so that the accuracy of estimating the inner conditional expectation improves and the cost also increases with level. We construct an antithetic MLMC estimator and provide sufficient assumptions on a decision model under which the antithetic property of the estimator is well exploited, and consequently a root-mean-square accuracy of $\varepsilon$ can be achieved at a cost of $O(\varepsilon^{-2})$. Numerical results confirm the considerable computational savings compared to the standard, nested Monte Carlo method for some simple testcases and a more realistic medical application.

Decision-theoretic sensitivity analysis for reservoir development under uncertainty using multilevel quasi-Monte Carlo methods

At various stages of petroleum reservoir development, we encounter a large degree of geological uncertainty under which a rational decision has to be made. In order to identify which parameter or group of parameters significantly affects the output of a decision model, we investigate decision-theoretic sensitivity analysis and its computational issues in this paper. In particular, we employ the so-called expected value of partial perfect information (EVPPI) as a sensitivity index and apply multilevel Monte Carlo (MLMC) methods to efficient estimation of EVPPI. In a recent paper by Giles and Goda, an antithetic MLMC estimator for EVPPI is proposed and its variance analysis is conducted under some assumptions on a decision model. In this paper, for an improvement on the performance of the MLMC estimator, we incorporate randomized quasi-Monte Carlo methods within the inner sampling, which results in an multilevel quasi-Monte Carlo (MLQMC) estimator. We apply both the antithetic MLMC and MLQMC estimators to a simple waterflooding decision problem under uncertainty on absolute permeability and relative permeability curves. Through numerical experiments, we compare the performances of the MLMC and MLQMC estimators and confirm a significant advantage of the MLQMC estimator.

Expected Value of Partial Perfect Information in Hybrid Models Using Dynamic Discretization

In decision theory models, expected value of partial perfect information (EVPPI) is an important analysis technique that is used to identify the value of acquiring further information on individual variables. EVPPI can be used to prioritize the parts of a model that should be improved or identify the parts where acquiring additional data or expert knowledge is most beneficial. Calculating EVPPI of continuous variables is challenging, and several sampling and approximation techniques have been proposed. This paper proposes a novel approach for calculating EVPPI in hybrid influence diagram (HID) models (these are influence diagrams (IDs) containing both discrete and continuous nodes). The proposed approach transforms the HID into a hybrid Bayesian network and makes use of the dynamic discretization and the junction tree algorithms to calculate the EVPPI. This is an approximate solution (no feasible exact solution is possible generally for HIDs) but we demonstrate it accurately calculates the EVPPI values. Moreover, unlike the previously proposed simulation-based EVPPI methods, our approach eliminates the requirement of manually determining the sample size and assessing convergence. Hence, it can be used by decision-makers who do not have deep understanding of programming languages and sampling techniques. We compare our approach to the previously proposed techniques based on two case studies.

Implementing Generalized Additive Models to Estimate the Expected Value of Sample Information in a Microsimulation Model: Results of Three Case Studies.

The expected value of sample information (EVSI) can help prioritize research but its application is hampered by computational infeasibility, especially for complex models. We investigated an approach by Strong and colleagues to estimate EVSI by applying generalized additive models (GAM) to results generated from a probabilistic sensitivity analysis (PSA). For 3 potential HIV prevention and treatment strategies, we estimated life expectancy and lifetime costs using the Cost-effectiveness of Preventing AIDS Complications (CEPAC) model, a complex patient-level microsimulation model of HIV progression. We fitted a GAM-a flexible regression model that estimates the functional form as part of the model fitting process-to the incremental net monetary benefits obtained from the CEPAC PSA. For each case study, we calculated the expected value of partial perfect information (EVPPI) using both the conventional nested Monte Carlo approach and the GAM approach. EVSI was calculated using the GAM approach. For all 3 case studies, the GAM approach consistently gave similar estimates of EVPPI compared with the conventional approach. The EVSI behaved as expected: it increased and converged to EVPPI for larger sample sizes. For each case study, generating the PSA results for the GAM approach required 3 to 4 days on a shared cluster, after which EVPPI and EVSI across a range of sample sizes were evaluated in minutes. The conventional approach required approximately 5 weeks for the EVPPI calculation alone. Estimating EVSI using the GAM approach with results from a PSA dramatically reduced the time required to conduct a computationally intense project, which would otherwise have been impractical. Using the GAM approach, we can efficiently provide policy makers with EVSI estimates, even for complex patient-level microsimulation models.

Prioritizing Future Research on Allopurinol and Febuxostat for the Management of Gout: Value of Information Analysis.

The aim of this study was to quantify the value of conducting additional research and reducing uncertainty regarding the cost effectiveness of allopurinol and febuxostat for the management of gout. We used a previously developed Markov model that evaluated the cost effectiveness of nine urate-lowering strategies: no treatment, allopurinol-only fixed dose (300mg), allopurinol-only dose escalation (up to 800mg), febuxostat-only fixed dose (80mg), febuxostat-only dose escalation (up to 120mg), allopurinol-febuxostat sequential therapy fixed dose, allopurinol-febuxostat sequential therapy dose escalation, febuxostat-allopurinol sequential therapy fixed dose, and febuxostat-allopurinol sequential therapy dose escalation. Each strategy was evaluated over the lifetime of a hypothetical gout patient. We calculated population expected value of perfect information (EVPI). We used a linear regression meta-modeling approach to calculate population expected value of partial perfect information (EVPPI), and a Gaussian approximation to calculate the population expected value of sample information for parameters (EVSI) and the expected net benefit of sampling (ENBS) for four potential study designs: (1) an allopurinol efficacy trial; (2) a febuxostat efficacy trial; (3) a prospective observational study evaluating health utilities; and (4) a comprehensive study evaluating the efficacy of allopurinol and febuxostat and health utilities. A 5-year decision time horizon was used in the base-case analysis. EVPI varied by a decision maker's willingness-to-pay (WTP) per quality-adjusted life-year (QALY) and was $US900 million for WTP of $US60,000 per QALY. Population EVPPI was highest across all WTP values for study design #4. For study design #4 and a WTP of $US60,000 per QALY, the optimal sample size was 735 patients per study arm. Future studies are needed to evaluate the effectiveness of allopurinol and febuxostat dose escalation.

A Review of Methods for Analysis of the Expected Value of Information.

In recent years, value-of-information analysis has become more widespread in health economic evaluations, specifically as a tool to guide further research and perform probabilistic sensitivity analysis. This is partly due to methodological advancements allowing for the fast computation of a typical summary known as the expected value of partial perfect information (EVPPI). A recent review discussed some approximation methods for calculating the EVPPI, but as the research has been active over the intervening years, that review does not discuss some key estimation methods. Therefore, this paper presents a comprehensive review of these new methods. We begin by providing the technical details of these computation methods. We then present two case studies in order to compare the estimation performance of these new methods. We conclude that a method based on nonparametric regression offers the best method for calculating the EVPPI in terms of accuracy, computational time, and ease of implementation. This means that the EVPPI can now be used practically in health economic evaluations, especially as all the methods are developed in parallel with R functions and a web app to aid practitioners.

Some Health States Are Better Than Others: Using Health State Rank Order to Improve Probabilistic Analyses.

Probabilistic sensitivity analyses (PSA) may lead policy makers to take nonoptimal actions due to misestimates of decision uncertainty caused by ignoring correlations. We developed a method to establish joint uncertainty distributions of quality-of-life (QoL) weights exploiting ordinal preferences over health states. Our method takes as inputs independent, univariate marginal distributions for each QoL weight and a preference ordering. It establishes a correlation matrix between QoL weights intended to preserve the ordering. It samples QoL weight values from their distributions, ordering them with the correlation matrix. It calculates the proportion of samples violating the ordering, iteratively adjusting the correlation matrix until this proportion is below an arbitrarily small threshold. We compare our method with the uncorrelated method and other methods for preserving rank ordering in terms of violation proportions and fidelity to the specified marginal distributions along with PSA and expected value of partial perfect information (EVPPI) estimates, using 2 models: 1) a decision tree with 2 decision alternatives and 2) a chronic hepatitis C virus (HCV) Markov model with 3 alternatives. All methods make tradeoffs between violating preference orderings and altering marginal distributions. For both models, our method simultaneously performed best, with largest performance advantages when distributions reflected wider uncertainty. For PSA, larger changes to the marginal distributions induced by existing methods resulted in differing conclusions about which strategy was most likely optimal. For EVPPI, both preference order violations and altered marginal distributions caused existing methods to misestimate the maximum value of seeking additional information, sometimes concluding that there was no value. Analysts can characterize the joint uncertainty in QoL weights to improve PSA and value-of-information estimates using Open Source implementations of our method.

A systematic review, evidence synthesis and meta-analysis of quantitative and qualitative studies evaluating the clinical effectiveness, the cost-effectiveness, safety and acceptability of interventions to prevent postnatal depression.

Postnatal depression (PND) is a major depressive disorder in the year following childbirth, which impacts on women, their infants and their families. A range of interventions has been developed to prevent PND. To (1) evaluate the clinical effectiveness, cost-effectiveness, acceptability and safety of antenatal and postnatal interventions for pregnant and postnatal women to prevent PND; (2) apply rigorous methods of systematic reviewing of quantitative and qualitative studies, evidence synthesis and decision-analytic modelling to evaluate the preventive impact on women, their infants and their families; and (3) estimate cost-effectiveness. We searched MEDLINE, EMBASE, Science Citation Index and other databases (from inception to July 2013) in December 2012, and we were updated by electronic alerts until July 2013. Two reviewers independently screened titles and abstracts with consensus agreement. We undertook quality assessment. All universal, selective and indicated preventive interventions for pregnant women and women in the first 6 postnatal weeks were included. All outcomes were included, focusing on the Edinburgh Postnatal Depression Scale (EPDS), diagnostic instruments and infant outcomes. The quantitative evidence was synthesised using network meta-analyses (NMAs). A mathematical model was constructed to explore the cost-effectiveness of interventions contained within the NMA for EPDS values. From 3072 records identified, 122 papers (86 trials) were included in the quantitative review. From 2152 records, 56 papers (44 studies) were included in the qualitative review. The results were inconclusive. The most beneficial interventions appeared to be midwifery redesigned postnatal care [as shown by the mean 12-month EPDS score difference of -1.43 (95% credible interval -4.00 to 1.36)], person-centred approach (PCA)-based and cognitive-behavioural therapy (CBT)-based intervention (universal), interpersonal psychotherapy (IPT)-based intervention and education on preparing for parenting (selective), promoting parent-infant interaction, peer support, IPT-based intervention and PCA-based and CBT-based intervention (indicated). Women valued seeing the same health worker, the involvement of partners and access to several visits from a midwife or health visitor trained in person-centred or cognitive-behavioural approaches. The most cost-effective interventions were estimated to be midwifery redesigned postnatal care (universal), PCA-based intervention (indicated) and IPT-based intervention in the sensitivity analysis (indicated), although there was considerable uncertainty. Expected value of partial perfect information (EVPPI) for efficacy data was in excess of £150M for each population. Given the EVPPI values, future trials assessing the relative efficacies of promising interventions appears to represent value for money. In the NMAs, some trials were omitted because they could not be connected to the main network of evidence or did not provide EPDS scores. This may have introduced reporting or selection bias. No adjustment was made for the lack of quality of some trials. Although we appraised a very large number of studies, much of the evidence was inconclusive. Interventions warrant replication within randomised controlled trials (RCTs). Several interventions appear to be cost-effective relative to usual care, but this is subject to considerable uncertainty. Several interventions appear to be cost-effective relative to usual care, but this is subject to considerable uncertainty. Future research conducting RCTs to establish which interventions are most clinically effective and cost-effective should be considered. This study is registered as PROSPERO CRD42012003273. The National Institute for Health Research Health Technology Assessment programme.

Cost effectiveness and value of information analyses of islet cell transplantation in the management of 'unstable' type 1 diabetes mellitus.

BackgroundIslet cell transplantation is a method to stabilize type 1 diabetes patients with hypoglycemia unawareness and unstable blood glucose levels by reducing insulin dependency and protecting against severe hypoglycemia through restoring endogenous insulin secretion. This study analyses the current cost-effectiveness of this technology and estimates the value of further research to reduce uncertainty around cost-effectiveness.MethodsWe performed a cost-utility analysis using a Markov cohort model with a mean patient age of 49 to simulate costs and health outcomes over a life-time horizon. Our analysis used intensive insulin therapy (IIT) as comparator and took the provincial healthcare provider perspective. Cost and effectiveness data for up to four transplantations per patient came from the University of Alberta hospital.Costs are expressed in 2012 Canadian dollars and effectiveness in quality-adjusted life-years (QALYs) and life years. To characterize the uncertainty around expected outcomes, we carried out a probabilistic sensitivity analysis within the Bayesian decision-analytic framework. We performed a value-of-information analysis to identify priority areas for future research under various scenarios. We applied a structural sensitivity analysis to assess the dependence of outcomes on model characteristics.ResultsCompared to IIT, islet cell transplantation using non-generic (generic) immunosuppression had additional costs of $150,006 ($112,023) per additional QALY, an average gain of 3.3 life years, and a probability of being cost-effective of 0.5 % (28.3 %) at a willingness-to-pay threshold of $100,000 per QALY. At this threshold the non-generic technology has an expected value of perfect information (EVPI) of $260,744 for Alberta. This increases substantially in cost-reduction scenarios. The research areas with the highest partial EVPI are costs, followed by natural history, and effectiveness and safety.ConclusionsCurrent transplantation technology provides substantial improvements in health outcomes over conventional therapy for highly selected patients with ‘unstable’ type 1 diabetes. However, it is much more costly and so is not cost-effective. The value of further research into the cost-effectiveness is dependent upon treatment costs. Further, we suggest the value of information should not only be derived from current data alone when knowing that this data will most likely change in the future.Electronic supplementary materialThe online version of this article (doi:10.1186/s12902-016-0097-7) contains supplementary material, which is available to authorized users.

A cost-utility analysis of sacral anterior root stimulation (SARS) compared with medical treatment in patients with complete spinal cord injury with a neurogenic bladder

Background ContextSacral anterior root stimulation (SARS) and posterior sacral rhizotomy restores the ability to urinate on demand with low residual volumes, which is a key for preventing urinary complications that account for 10% of the causes of death in patients with spinal cord injury with a neurogenic bladder. Nevertheless, comparative cost-effectiveness results on a long time horizon are lacking to adequately inform decisions of reimbursement. PurposeThis study aimed to estimate the long-term cost-utility of SARS using the Finetech-Brindley device compared with medical treatment (anticholinergics+catheterization). Study Design/SettingsThe following study design is used for the paper: Markov model elaborated with a 10-year time horizon; with four irreversible states: (1) initial treatment, (2) year 1 of surgery for urinary complication, (3) year >1 of surgery for urinary complication, and (4) death; and reversible states: urinary calculi; Finetech-Brindley device failures. Patient SampleThe sample consisted of theoretical cohorts of patients with a complete spinal cord lesion since ≥1 year, and a neurogenic bladder. Outcome MeasuresEffectiveness was expressed as quality adjusted life years (QALYs). Costs were valued in EUR 2013 in the perspective of the French health system. MethodsA systematic review and meta-analyses were performed to estimate transition probabilities and QALYs. Costs were estimated from the literature, and through simulations using the 2013 French prospective payment system classification. Probabilistic analyses were conducted to handle parameter uncertainty. ResultsIn the base case analysis (2.5% discount rate), the cost-utility ratio was 12,710 EUR per QALY gained. At a threshold of 30,000 EUR per QALY the probability of SARS being cost-effective compared with medical treatment was 60%. If the French Healthcare System reimbursed SARS for 80 patients per year during 10 years (anticipated target population), the expected incremental net health benefit would be 174 QALYs, and the expected value of perfect information (EVPI) would be 4.735 million EUR. The highest partial EVPI is reached for utility values and costs (1.3–1.6 million EUR). ConclusionsOur model shows that SARS using Finetech-Brindley device offers the most important benefit and should be considered cost-effective at a cost-effectiveness threshold of 30,000 EUR per QALY. Despite a high uncertainty, EVPI and partial EVPI may indicate that further research would not be profitable to inform decision-making.

Use of value of information in UK health technology assessments.

The aim of this study was to identify and critically appraise the use of Value of Information (VOI) analyses undertaken as part of health technology assessment (HTA) reports in England and Wales. A systematic review of National Institute for Health Research (NIHR) funded HTA reports published between 2004 and 2013 identified the use of VOI methods and key analytical details in terms of: (i) types of VOI methodology used; (ii) parameters and key assumptions; and (iii) conclusions drawn in terms of the need for further research. A total of 512 HTA reports were published during the relevant timeframe. Of these, 203 reported systematic review and economic modeling studies and 25 of these had used VOI method(s). Over half of the twenty-five studies (n = 13) conducted both EVPI (Expected Value of Perfect Information) and EVPPI (Expected Value of Partial Perfect Information) analyses. Eight studies conducted EVPI analysis, three studies conducted EVPI, EVPPI, and EVSI (Expected Value of Sampling Information) analyses and one study conducted EVSI analysis only. The level of detail reporting the methods used to conduct the VOI analyses varied. This review has shown that the frequency of the use of VOI methods is increasing at a slower pace compared with the published volume of HTA reports. This review also suggests that analysts reporting VOI method(s) in HTA reports should aim to describe the method(s) in sufficient detail to enable and encourage decision-makers guiding research prioritization decisions to use the potentially valuable outputs from quantitative VOI analyses.