Cytoreductive surgery plus hyperthermic intraoperative peritoneal chemotherapy for people with peritoneal metastases from colorectal, ovarian or gastric origin: A systematic review of randomized controlled trials

BACKGROUND

There is uncertainty in the relative benefits and harms of hyperthermic intraoperative peritoneal chemotherapy (HIPEC) when added to cytoreductive surgery (CRS) +/- systemic chemotherapy or systemic chemotherapy alone in people with peritoneal metastases from colorectal, gastric, or ovarian cancers.

METHODS

We searched randomized controlled trials (RCTs) in the medical literature until April 14, 2022 and applied methods used for high-quality systematic reviews.

FINDINGS

We included a total of eight RCTs (seven RCTs included in quantitative analysis as one RCT did not provide data in an analyzable format). All comparisons other than ovarian cancer contained only one trial. For gastric cancer, there is high uncertainty about the effect of CRS + HIPEC + systemic chemotherapy. For stage III or greater epithelial ovarian cancer undergoing interval cytoreductive surgery, CRS + HIPEC + systemic chemotherapy probably decreases all-cause mortality compared to CRS + systemic chemotherapy. For colorectal cancer, CRS + HIPEC + systemic chemotherapy probably results in little to no difference in all-cause mortality and may increase the serious adverse events proportions compared to CRS +/- systemic chemotherapy, but probably decreases all-cause mortality compared to fluorouracil-based systemic chemotherapy alone.

INTERPRETATION

The role of CRS + HIPEC in gastric peritoneal metastases is uncertain. CRS + HIPEC should be standard of care in women with stage III or greater epithelial ovarian cancer undergoing interval CRS. CRS + systemic chemotherapy should be standard of care for people with colorectal peritoneal metastases, with HIPEC given only as part of a RCT focusing on subgroups and regimes.

PROSPERO REGISTRATION

CRD42019130504.

Identifying the Optimum Strategy for Identifying Adults and Children With Celiac Disease: A Cost-Effectiveness and Value of Information Analysis

OBJECTIVES

Celiac disease (CD) is thought to affect around 1% of people in the United Kingdom, but only approximately 30% are diagnosed. The aim of this work was to assess the cost-effectiveness of strategies for identifying adults and children with CD in terms of who to test and which tests to use.

METHODS

A decision tree and Markov model were used to describe testing strategies and model long-term consequences of CD. The analysis compared a selection of pre-test probabilities of CD above which patients should be screened, as well as the use of different serological tests, with or without genetic testing. Value of information analysis was used to prioritize parameters for future research.

RESULTS

Using serological testing alone in adults, immunoglobulin A (IgA) tissue transglutaminase (tTG) at a 1% pre-test probability (equivalent to population screening) was most cost-effective. If combining serological testing with genetic testing, human leukocyte antigen combined with IgA tTG at a 5% pre-test probability was most cost-effective. In children, the most cost-effective strategy was a 10% pre-test probability with human leukocyte antigen plus IgA tTG. Value of information analysis highlighted the probability of late diagnosis of CD and the accuracy of serological tests as important parameters. The analysis also suggested prioritizing research in adult women over adult men or children.

CONCLUSIONS

For adults, these cost-effectiveness results suggest UK National Screening Committee Criteria for population-based screening for CD should be explored. Substantial uncertainty in the results indicate a high value in conducting further research.

Home Gardening of Yellow Cassava and Orange Maize for the Prevention of Nutritional Blindness in Children: An Economic Evaluation and Value of Information Analysis

OBJECTIVES

Vitamin A deficiency is the leading cause of childhood blindness worldwide, affecting mostly Sub-Saharan Africa. We aimed to predict the cost-effectiveness of home gardening (HG) of yellow cassava and orange maize to prevent nutritional blindness in children below 5 years and to assess the likely value of obtaining additional information in reducing uncertainty surrounding its cost-effectiveness.

METHODS

We developed a Markov model and carried out probabilistic sensitivity analysis with a value of information analysis. We costed resources from a societal perspective and outcomes were measured in disability-adjusted life years (DALYs).

RESULTS

HG was estimated to cost an additional Intl$395.00 per DALY averted, with a 72.27% likelihood of being cost-effective at a threshold of Intl$2800 per DALY. The expected value of information was estimated to be Intl$29 843.50 for 1 child or Intl$925 billion for 31 million Nigerian children affected by the decision. Further research is only worthwhile for 1 parameter (relative risk of low serum retinol; expected value of perfect parameter information Intl$29 854.53 per child and Intl$925 billion for 31 million children).

CONCLUSION

HG of yellow cassava and orange maize is expected to be highly cost-effective in preventing nutritional blindness in Nigerian children. Worthwhile further research includes a cost analysis of the intervention and a high-quality randomized trial to assess the effectiveness of HG on serum retinol levels in young children.

Cost-Effectiveness of Venetoclax Plus Obinutuzumab Versus Chlorambucil Plus Obinutuzumab for the First-Line Treatment of Adult Patients With Chronic Lymphocytic Leukemia: An Extended Societal View

OBJECTIVES

Efficacy of venetoclax plus obinutuzumab (VenO) compared with chlorambucil plus obinutuzumab (ClbO) for treatment-naïve adult patients with chronic lymphocytic leukemia (CLL) with coexisting medical conditions was investigated in CLL14 (NCT02242942). Our aim was to evaluate the cost-effectiveness of VenO versus ClbO for these patients from a Dutch societal perspective.

METHODS

A 3-state partitioned survival model was constructed to evaluate the cost-effectiveness of VenO. The outcome of the analysis was the incremental cost-effectiveness ratio (ICER) with effectiveness measured in quality-adjusted life-years (QALYs) gained. Uncertainty was explored through deterministic and probabilistic sensitivity analyses, scenario analyses, and value of information analysis (VOI).

RESULTS

The base case resulted in a discounted ICER -49 928 EUR/QALY gained (with incremental negative costs and positive effects). None of the ICERs resulted from deterministic sensitivity and scenario analyses exceeded the chosen willingness-to-pay threshold of 20 000 EUR/QALY, and > 99% of the iterations in the probabilistic sensitivity analysis were cost-effective. VOI analyses showed a maximum expected value of eliminating all model parameter uncertainty of 183 591 EUR.

CONCLUSIONS

Our study demonstrated VenO being dominant over ClbO in treatment-naïve adult patients with CLL assuming a Dutch societal perspective. We concluded that our results are robust as tested through sensitivity and scenario analyses. Additionally, the VOI analyses confirmed that our current evidence base is strong enough to generate reliable results for our study. Nevertheless, further research based on real-world data or longer follow-up period could further contribute to the robustness of the current study's conclusions.

A latanoprost cationic emulsion (STN1013001) vs. other latanoprost formulations (Latanoprost) in open angle glaucoma/ocular hypertension and ocular surface disease: an Italian cost-utility analysis

BACKGROUND

STN1013001 is an innovative latanoprost cationic emulsion for open-angle glaucoma/ocular hypertension (OAG/OHT) and ocular surface disease (OSD).

METHODS AND FINDINGS

A 5-year, 7 health states, 1-year cycle early Markov model-supported cost-utility analysis (CUA) of STN1013001 vs. other latanoprost formulations (Latanoprost) followed the Italian National Health Service (INHS) perspective.One-way, probabilistic and scenario sensitivity analyses tested the uncertainty of the baseline results. Value of information analysis (VOIA) investigated the potential cost-effectiveness of collecting further evidence.

RESULTS

Over 5 years, the Markov model-supported CUA predicts STN1013001 to be potentially highly cost-effective vs. Latanoprost (+€57.60 cost at €2020 values; +0.089 Quality-Adjusted Life Years).The Incremental Cost-Utility Ratio (€647.65) falls well below the lower limit of the acceptability range proposed for Italy (€25,000-€40,000).Sensitivity analyses confirmed the robustness of the baseline findings. VOIA highlighted that further information might only be cost-effective for OAG/OHT utilities and OSD-related disutility.

CONCLUSION

STN1013001 is potentially highly cost-effective and strongly dominant vs. Latanoprost for OAG/OHT+OSD patients from the INHS perspective. These findings should be re-assessed using the data from the ongoing Phase III trial (NCT04133311) comparing the efficacy and safety of STN1013001 vs. Latanoprost and with future real-world CUAs upon the availability of STN1013001 on the Italian market.

Cost-Effectiveness and Value-of-Information Analysis Using Machine Learning-Based Metamodeling: A Case of Hepatitis C Treatment

BACKGROUND

Metamodels can address some of the limitations of complex simulation models by formulating a mathematical relationship between input parameters and simulation model outcomes. Our objective was to develop and compare the performance of a machine learning (ML)-based metamodel against a conventional metamodeling approach in replicating the findings of a complex simulation model.

METHODS

We constructed 3 ML-based metamodels using random forest, support vector regression, and artificial neural networks and a linear regression-based metamodel from a previously validated microsimulation model of the natural history hepatitis C virus (HCV) consisting of 40 input parameters. Outcomes of interest included societal costs and quality-adjusted life-years (QALYs), the incremental cost-effectiveness (ICER) of HCV treatment versus no treatment, cost-effectiveness analysis curve (CEAC), and expected value of perfect information (EVPI). We evaluated metamodel performance using root mean squared error (RMSE) and Pearson's R² on the normalized data.

RESULTS

The R² values for the linear regression metamodel for QALYs without treatment, QALYs with treatment, societal cost without treatment, societal cost with treatment, and ICER were 0.92, 0.98, 0.85, 0.92, and 0.60, respectively. The corresponding R² values for our ML-based metamodels were 0.96, 0.97, 0.90, 0.95, and 0.49 for support vector regression; 0.99, 0.83, 0.99, 0.99, and 0.82 for artificial neural network; and 0.99, 0.99, 0.99, 0.99, and 0.98 for random forest. Similar trends were observed for RMSE. The CEAC and EVPI curves produced by the random forest metamodel matched the results of the simulation output more closely than the linear regression metamodel.

CONCLUSIONS

ML-based metamodels generally outperformed traditional linear regression metamodels at replicating results from complex simulation models, with random forest metamodels performing best.

HIGHLIGHTS

Decision-analytic models are frequently used by policy makers and other stakeholders to assess the impact of new medical technologies and interventions. However, complex models can impose limitations on conducting probabilistic sensitivity analysis and value-of-information analysis, and may not be suitable for developing online decision-support tools.Metamodels, which accurately formulate a mathematical relationship between input parameters and model outcomes, can replicate complex simulation models and address the above limitation.The machine learning-based random forest model can outperform linear regression in replicating the findings of a complex simulation model. Such a metamodel can be used for conducting cost-effectiveness and value-of-information analyses or developing online decision support tools.

Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials

Introduction

Adaptive designs allow changes to an ongoing trial based on prespecified early examinations of accrued data. Opportunities are potentially being missed to incorporate health economic considerations into the design of these studies.

Methods

We describe how to estimate the expected value of sample information for group sequential design adaptive trials. We operationalize this approach in a hypothetical case study using data from a pilot trial. We report the expected value of sample information and expected net benefit of sampling results for 5 design options for the future full-scale trial including the fixed-sample-size design and the group sequential design using either the Pocock stopping rule or the O’Brien-Fleming stopping rule with 2 or 5 analyses. We considered 2 scenarios relating to 1) using the cost-effectiveness model with a traditional approach to the health economic analysis and 2) adjusting the cost-effectiveness analysis to incorporate the bias-adjusted maximum likelihood estimates of trial outcomes to account for the bias that can be generated in adaptive trials.

Results

The case study demonstrated that the methods developed could be successfully applied in practice. The results showed that the O’Brien-Fleming stopping rule with 2 analyses was the most efficient design with the highest expected net benefit of sampling in the case study.

Conclusions

Cost-effectiveness considerations are unavoidable in budget-constrained, publicly funded health care systems, and adaptive designs can provide an alternative to costly fixed-sample-size designs. We recommend that when planning a clinical trial, expected value of sample information methods be used to compare possible adaptive and nonadaptive trial designs, with appropriate adjustment, to help justify the choice of design characteristics and ensure the cost-effective use of research funding.

Highlights

Late Pregnancy Ultrasound to Screen for and Manage Potential Birth Complications in Nulliparous Women: A Cost-Effectiveness and Value of Information Analysis

BACKGROUND

Fetal growth restriction is a major risk factor for stillbirth. A routine late-pregnancy ultrasound scan could help detect this, allowing intervention to reduce the risk of stillbirth. Such a scan could also detect fetal presentation and predict macrosomia. A trial powered to detect stillbirth differences would be extremely large and expensive.

OBJECTIVES

It is therefore critical to know whether this would be a good investment of public research funds. The aim of this study is to estimate the cost-effectiveness of various late-pregnancy screening and management strategies based on current information and predict the return on investment from further research.

METHODS

Synthesis of current evidence structured into a decision model reporting expected costs, quality-adjusted life-years, and net benefit over 20 years and value-of-information analysis reporting predicted return on investment from future clinical trials.

RESULTS

Given a willingness to pay of £20 000 per quality-adjusted life-year gained, the most cost-effective strategy is a routine presentation-only scan for all women. Universal ultrasound screening for fetal size is unlikely to be cost-effective. Research exploring the cost implications of induction of labor has the greatest predicted return on investment. A randomized, controlled trial with an endpoint of stillbirth is extremely unlikely to be a value for money investment.

CONCLUSION

Given current value-for-money thresholds in the United Kingdom, the most cost-effective strategy is to offer all pregnant women a presentation-only scan in late pregnancy. A randomized, controlled trial of screening and intervention to reduce the risk of stillbirth following universal ultrasound to detect macrosomia or fetal growth restriction is unlikely to represent a value for money investment.

Early economic evaluation of an intervention to improve uptake of the NHS England Diabetes Prevention Programme

BACKGROUND

Despite reported increases in referral numbers, a large proportion of those with prediabetes still decline participation in the NHS England Diabetes Prevention Programme (NDPP). The aim of this study was to explore whether investment in interventions to improve uptake of the programme has the potential to be cost-effective.

METHODS

An early cost-utility analysis was conducted using a Markov model parameterized based on secondary data sources. We explored different uptake scenarios and the impact that this had on the maximum allowable intervention price based on cost-effectiveness at the UK NICE willingness to pay threshold of £20,000 (US$ 25,913). Value of information analyses were conducted to explore the potential value of further research to resolve uncertainty at each uptake level.

RESULTS

As uptake levels increase, the maximum allowable intervention price and overall expected value of removing decision uncertainty increases. For 5 percentage and 50 percentage points increase in uptake levels, the maximum allowable intervention price is £41.86 (US$ 54.23) and £418.59 (US$ 542.34) per person, and the overall expected value of removing decision uncertainty are £361,818,839 (US$ 468,786,625) and £1,468,712,316 (US$ 1,902,921,063) respectively.

CONCLUSION

There is headroom for investment in interventions that improve uptake to the NDPP, thereby allowing the programme itself to be delivered in a manner that remains cost-effective.

Early invasive strategy in senior patients with non-ST-segment elevation myocardial infarction: is it cost-effective? - a decision-analytic model and value of information analysis

BACKGROUND

Non-ST-elevation myocardial infarction (NSTEMI) is the most common type of heart attack in the UK and it is becoming increasingly prevalent among older people. An early invasive treatment strategy may be effective and cost-effective for treating NSTEMI but evidence is currently unclear.

OBJECTIVES

To assess the cost-effectiveness of the early invasive strategy versus medical management in elderly patients with NSTEMI and to provide guidance for future research in this area.

METHODS

A long-term Markov state transition model was developed. Model inputs were systematically derived from a number of sources most appropriate to a UK relevant analysis, such as published studies and national routine data. Costs were estimated from the perspective of National Health Service and Personal Social Services. The model was developed using TreeAge Pro software. Based on a probabilistic sensitivity analysis, a value of information analysis was carried out to establish the value of decision uncertainty both overall and for specific input parameters.

RESULTS

In 2017 UK £, the incremental cost-effectiveness ratio of the early invasive strategy was £46 916 for each additional quality-adjusted life-year (QALY) gained, with a probability of being cost-effective of 23% at a cost-effectiveness threshold of £20 000/QALY. There was a considerable decision uncertainty with these results. The value of removing all this uncertainty was up to £1 920 000 annually. Most uncertainty related to clinical effectiveness parameters and the optimal study design to remove this uncertainty would be a randomised controlled trial.

CONCLUSION

Based on current evidence, the early invasive strategy is not likely to be cost-effective for elderly patients with NSTEMI. This conclusion should be interpreted with caution mainly due to the absence of NSTEMI-specific data and long-term clinical effectiveness estimates.

"Time Traveling Is Just Too Dangerous" but Some Methods Are Worth Revisiting: The Advantages of Expected Loss Curves Over Cost-Effectiveness Acceptability Curves and Frontier

BACKGROUND

Cost-effectiveness acceptability curves (CEACs) and the cost-effectiveness acceptability frontier (CEAF) are the recommended graphical representations of uncertainty in a cost-effectiveness analysis (CEA). Nevertheless, many limitations of CEACs and the CEAF have been recognized by others. Expected loss curves (ELCs) overcome these limitations by displaying the expected foregone benefits of choosing one strategy over others, the optimal strategy in expectation, and the value of potential future research all in a single figure.

OBJECTIVES

To revisit ELCs, illustrate their benefits using a case study, and promote their adoption by providing open-source code.

METHODS

We used a probabilistic sensitivity analysis of a CEA comparing 6 cerebrospinal fluid biomarker test-and-treat strategies in patients with mild cognitive impairment. We showed how to calculate ELCs for a set of decision alternatives. We used the probabilistic sensitivity analysis of the case study to illustrate the limitations of currently recommended methods for communicating uncertainty and then demonstrated how ELCs can address these issues.

RESULTS

ELCs combine the probability that each strategy is not cost-effective on the basis of current information and the expected foregone benefits resulting from choosing that strategy (ie, how much is lost if we recommended a strategy with a higher expected loss). ELCs display how the optimal strategy switches across willingness-to-pay thresholds and enables comparison between different strategies in terms of the expected loss.

CONCLUSIONS

ELCs provide a more comprehensive representation of uncertainty and overcome current limitations of CEACs and the CEAF. Communication of uncertainty in CEA would benefit from greater adoption of ELCs as a complementary method to CEACs, the CEAF, and the expected value of perfect information.

A Gaussian Approximation Approach for Value of Information Analysis

Most decisions are associated with uncertainty. Value of information (VOI) analysis quantifies the opportunity loss associated with choosing a suboptimal intervention based on current imperfect information. VOI can inform the value of collecting additional information, resource allocation, research prioritization, and future research designs. However, in practice, VOI remains underused due to many conceptual and computational challenges associated with its application. Expected value of sample information (EVSI) is rooted in Bayesian statistical decision theory and measures the value of information from a finite sample. The past few years have witnessed a dramatic growth in computationally efficient methods to calculate EVSI, including metamodeling. However, little research has been done to simplify the experimental data collection step inherent to all EVSI computations, especially for correlated model parameters. This article proposes a general Gaussian approximation (GA) of the traditional Bayesian updating approach based on the original work by Raiffa and Schlaifer to compute EVSI. The proposed approach uses a single probabilistic sensitivity analysis (PSA) data set and involves 2 steps: 1) a linear metamodel step to compute the EVSI on the preposterior distributions and 2) a GA step to compute the preposterior distribution of the parameters of interest. The proposed approach is efficient and can be applied for a wide range of data collection designs involving multiple non-Gaussian parameters and unbalanced study designs. Our approach is particularly useful when the parameters of an economic evaluation are correlated or interact.

Value of Information Analysis of Multiparameter Tests for Chemotherapy in Early Breast Cancer: The OPTIMA Prelim Trial

BACKGROUND

Precision medicine is heralded as offering more effective treatments to smaller targeted patient populations. In breast cancer, adjuvant chemotherapy is standard for patients considered as high-risk after surgery. Molecular tests may identify patients who can safely avoid chemotherapy.

OBJECTIVES

To use economic analysis before a large-scale clinical trial of molecular testing to confirm the value of the trial and help prioritize between candidate tests as randomized comparators.

METHODS

Women with surgically treated breast cancer (estrogen receptor-positive and lymph node-positive or tumor size ≥30 mm) were randomized to standard care (chemotherapy for all) or test-directed care using Oncotype DX™. Additional testing was undertaken using alternative tests: MammaPrint^TM, PAM-50 (Prosigna^TM), MammaTyper^TM, IHC4, and IHC4-AQUA™ (NexCourse Breast™). A probabilistic decision model assessed the cost-effectiveness of all tests from a UK perspective. Value of information analysis determined the most efficient publicly funded ongoing trial design in the United Kingdom.

RESULTS

There was an 86% probability of molecular testing being cost-effective, with most tests producing cost savings (range -£1892 to £195) and quality-adjusted life-year gains (range 0.17-0.20). There were only small differences in costs and quality-adjusted life-years between tests. Uncertainty was driven by long-term outcomes. Value of information demonstrated value of further research into all tests, with Prosigna currently being the highest priority for further research.

CONCLUSIONS

Molecular tests are likely to be cost-effective, but an optimal test is yet to be identified. Health economics modeling to inform the design of a randomized controlled trial looking at diagnostic technology has been demonstrated to be feasible as a method for improving research efficiency.

An integrated approach to evaluating alternative risk prediction strategies: a case study comparing alternative approaches for preventing invasive fungal disease

OBJECTIVES

This article proposes an integrated approach to the development, validation, and evaluation of new risk prediction models illustrated with the Fungal Infection Risk Evaluation study, which developed risk models to identify non-neutropenic, critically ill adult patients at high risk of invasive fungal disease (IFD).

METHODS

Our decision-analytical model compared alternative strategies for preventing IFD at up to three clinical decision time points (critical care admission, after 24 hours, and end of day 3), followed with antifungal prophylaxis for those judged "high" risk versus "no formal risk assessment." We developed prognostic models to predict the risk of IFD before critical care unit discharge, with data from 35,455 admissions to 70 UK adult, critical care units, and validated the models externally. The decision model was populated with positive predictive values and negative predictive values from the best-fitting risk models. We projected lifetime cost-effectiveness and expected value of partial perfect information for groups of parameters.

RESULTS

The risk prediction models performed well in internal and external validation. Risk assessment and prophylaxis at the end of day 3 was the most cost-effective strategy at the 2% and 1% risk threshold. Risk assessment at each time point was the most cost-effective strategy at a 0.5% risk threshold. Expected values of partial perfect information were high for positive predictive values or negative predictive values (£11 million-£13 million) and quality-adjusted life-years (£11 million).

CONCLUSIONS

It is cost-effective to formally assess the risk of IFD for non-neutropenic, critically ill adult patients. This integrated approach to developing and evaluating risk models is useful for informing clinical practice and future research investment.