New medical treatments are costly to develop and unlikely to successfully make their way through clinical trials, market access, and reimbursement. Moreover, new treatments for rare diseases are limited in both patient populations and reimbursement levels, further reducing their attractiveness as an investment. To increase the incentive to develop new treatments, portfolios of investments in new treatments in clinical trials have been proposed to financially de-risk drug development. We explore an important lever for further improving the incentives to develop treatments that is new in the context of biomedical portfolios: response-adaptive pilot studies in clinical trials that allow investments to be adjusted across projects as data are observed and that account for the importance of cost-effectiveness in gaining market access. We propose and analyze a model that shows that even a modest level of response adaptivity can materially improve financial outcomes from portfolios and improve the chances of new treatments making it to market, particularly when investment amounts are limited. We derive expected value of information heuristics to guide clinical trial management in the portfolio and the design of the portfolio, and use an illustrative numerical study motivated by pediatric oncology to show the value of using response-adaptive pilots for improving financial and health access outcomes.

Value-adaptive designs for clinical trials are a novel set of emerging methods for delivering greater value for clinical research. There is increasing interest in using them within publicly funded health systems. A value-adaptive design permits ‘in progress’ changes to be made to the trial according to criteria which reflect its overall value to the healthcare system, including the cost-effectiveness of the technologies under investigation, the cost of running the trial and the total health benefit delivered to patients. These trial designs offer the potential to explicitly balance the costs and benefits of adaptive clinical trials with the health economic benefits expected for populations that are affected by any subsequent health technology adoption decisions. They may also improve the expected value of learning from the budget that is spent within a trial. This paper introduces value-adaptive designs for publicly funded clinical trials. It discusses the idea of delivering ‘value for money’ in health technology assessment, what is meant by being ‘value-adaptive’ and the key features that characterise these designs. The methodology behind one kind of value-adaptive design – the value-based sequential model of a two-armed clinical trial proposed by Chick et al. (2017) – is described and illustrated using three retrospective case studies from the United Kingdom. The paper concludes by reviewing a range of perspectives provided by stakeholders, together with our own thoughts, on the practical opportunities and changes required for implementing a value-adaptive approach. Value-adaptive clinical trial designs offer the potential to align health research funding allocations with population health economic goals. Many of the systems required to deploy value-adaptive designs within a publicly funded health system already exist and, with increased application, experience, and refinement they have the potential to deliver improved value for money.

Contract Design for Outsourcing Search Firms commonly outsource search for new employees, real estate, or technology to external agents. What should the contracts with these agents look like, and under which conditions should companies even hire agents (as opposed to doing the search in house)? These are the questions studied in “The when and how of delegated search” by Zorc et al. The authors find that the optimal contracts pay the agent a per-time fee as well as a bonus for finding an acceptable alternative. The size of this bonus is defined on signing of the contract and decreases over time. The decision of whether to outsource at all hinges on the firm’s trade-off between speed and quality; in-house search becomes optimal for a firm that prioritizes quality, but outsourcing offers better speed.

New medical technologies must pass several risky hurdles, such as multiple phases of clinical trials, before market access and reimbursement. A portfolio of technologies pools these risks, reducing the collective financial risk of such development while also improving the chances of identifying a successful technology. We propose a stylized model of a portfolio of technologies, each of which must pass one or two phases of clinical trials before market access is possible. Using ideas from Bayesian sequential optimization, we study the value of running response-adaptive clinical trials to flexibly allocate resources across clinical trials for technologies in a portfolio. We suggest heuristics for the response-adaptive policy and find evidence for their value relative to non-adaptive policies.

Background Intensive Care Unit (ICU) capacity management is essential to provide high-quality healthcare for critically ill patients. Yet, consensus on the most favorable ICU design is lacking, especially whether ICUs should deliver dedicated or non-dedicated care. The decision for dedicated or non-dedicated ICU design considers a trade-off in the degree of specialization for individual patient care and efficient use of resources for society. We aim to share insights of a model simulating capacity effects for different ICU designs. Upon request, this simulation model is available for other ICUs.Methods A discrete event simulation model was developed and used, to study the hypothetical performance of a large University Hospital ICU on occupancy, rejection, and rescheduling rates for a dedicated and non-dedicated ICU design in four different scenarios. These scenarios either simulate the base-case situation of the local ICU, varying bed capacity levels, potential effects of reduced length of stay for a dedicated design and unexpected increased inflow of unplanned patients.Results The simulation model provided insights to foresee effects of capacity choices that should be made. The non-dedicated ICU design outperformed the dedicated ICU design in terms of efficient use of scarce resources.Conclusions The choice to use dedicated ICUs does not only affect the clinical outcome, but also rejection- rescheduling and occupancy rates. Our analysis of a large university hospital demonstrates how such a model can support decision making on ICU design, in conjunction with other operation characteristics such as staffing and quality management.

Direct stochastic simulations of medium to large scale Markovian processes with population dynamics may have runtimes that are proportional to the population size, if they account for each state transition of each individual in the population. Several approaches to speed up such simulations have been proposed. We use a discrete-time, Euler-forward type approximation for state transition functions that simulates all transitions within a given time step in an effort to improve run times, at the expense of some (potentially correctable) bias. We illustrate this with a stylized model of COVID-19 social distancing interventions in the United Arab Emirates. We also adapt the next generation matrix method of Hill and Longini (2003) to a continuous time, discrete state model. The approach accelerates simulation run times from a linear scaling of run times in population size to a constant that depends on the number of possible state transitions.

Health technology assessments often inform decisions made by public payers, such as the UK's National Health Service, as they negotiate the pricing of companies' new health technologies. A common assessment mechanism compares the incremental costeffectiveness ratio (ICER) of the new health technology, relative to a standard of care, to maximum threshold on the cost per quality-adjusted life year. In much research and practice, these assessments may not distinguish between cost-per-patient and negotiated price, effectively ignoring the value-based-pricing principle that better health outcomes merit higher prices. Other research makes this distinction, but it does not account for uncertainty in the ICER associated with clinical trial data that are limited in size and scope. This paper models the strategic behavior of a payer and a company as they price a new health technology, and it considers the use of conditional approval (CA) schemes whose post-marketing trials reduce ICER uncertainty before final pricing decisions are made. Analytical results suggest a very different view of the value-based pricing negotiations underlying these schemes: interim prices used during CA post-marketing trials should reflect cost-sharing for the CA scheme, not just cost-effectiveness goals for a treatment. Moreover, the types caps on interim prices used by entities such as the UK Cancer Drugs Fund may hinder development of new technologies and lead to suboptimal CA designs. We propose a new risk-sharing mechanism to remedy this. Numerical results, calibrated to approval data an oncology drug, illustrate the issues in a practical setting.

Contextual ranking & selection is attracting increasing attention in simulation and other fields. A successful approach to addressing related challenges uses arm allocation indices that compute the Bayesian expected value of information of one-step look-ahead policies. We recall recent work on such indices for linear contextual bandits that take advantage of structural information about the nature of the covariates that describe contexts. Such indices can be computed exactly with a finite number of contexts and no delay in observing outcomes, but may require Monte Carlo simulation otherwise. Our contribution is to describe and quantify the benefits of two variance reduction techniques (conditional Monte Carlo and common random numbers) to estimate such allocation indices for contextual ranking & selection problems when some covariates are continuous or outcomes are observed with delay. We find that both techniques significantly improve estimates and the speed of inference, but conditioning is particularly useful.

Objectives: Value-based trials aim to maximize the expected net benefit by balancing technology adoption decisions and clinical trial costs. Adaptive trials offer additional efficiency. This article provides guidance on determining whether a value-based sequential design is the best option for an adaptive 2-arm trial, illustrated through a case study. Methods: We outlined 4 steps for the value-based sequential approach. The case study re-evaluates the Big CACTUS trial design using pilot trial data and a model-based health economic analysis. Expected net benefit is computed for (1) original fixed design, (2) value-based design with fixed sample size, and (3) optimal value-based sequential design with adaptive stopping. We compare pretrial modeling with the actual Big CACTUS trial results. Results: Over 10 years, the adoption decision would affect approximately 215 378 patients. Pretrial modeling shows that the expected net benefit minus costs are (1) 102 pound million for the original fixed design, (2) 107 pound million (+5.3% higher) for the value-based design with optimal fixed sample size, and (3) 109 pound million (+6.7% higher) for the optimal value-based sequential design with maximum sample size of 435 per arm. Post hoc analysis using actual Big CACTUS trial data indicates that the value-adaptive trial with a maximum sample size of 95 participant pairs would not have stopped early. Bootstrap simulations reveal a 9.76% probability of early completion with n = 95 pairs compared with 31.50% with n = 435 pairs. Conclusions: The 4-step approach to value-based sequential 2-arm design with adaptive stopping was successfully implemented. Further application of value-based adaptive approaches could be useful to assess the efficiency of alternative study designs.

Health systems are placing increasing emphasis on improving the design and operation of clinical trials with the aim of making the health technology adoption process more value-based . We present a model of a value-based, two-armed clinical trial in which both the recruitment rate and trial length are optimized. The model is value-based because it balances the cost of the trial with the expected benefit it generates for patients, valued by the relative health benefits and costs of the technologies. We consider a wide range of regulatory and practical contexts that address how patient health is valued (discount rate, time horizon, pragmatic trials). We present comparative statics and asymptotic analysis together with a retrospective application to a recent health technology assessment and an extension for adaptive trials. Results challenge traditional perceptions concerning the efficiency, length, and knowledge that may be gained from clinical research for trial managers or funders charged with delivering value efficiently: we highlight trade-offs between trial costs and population health benefits influenced by trial outcomes and the importance of optimizing both recruitment rate and trial duration rather than sample size alone. This paper was accepted by Stefan Scholtes, health. Funding: Alban and Chick acknowledge the support of the European Union through the Marie Skłodowska-Curie Actions European Sepsis Academy Initial Training Network (MSCA-ESA-ITN) project [Grant 676129]. Forster acknowledges funding from the Research Infrastructure Support Fund of the Department of Economics and Related Studies, University of York. Supplemental Material: The online companion and data are available at https://doi.org/10.1287/mnsc.2022.4540 .

This paper investigates the dynamics of communication on social media, related to the spread of rumours, by studying the impact of micro-level agent interactions within social media discussions, on macro-level outcomes related to the diffusion of rumours. An agent-based framework is used to model social media discussions, modularly describing heterogeneous agents with differentiating characteristics, their interaction dynamics, rumour state transitions, and the evolution of networks on which these agents interact. Studying the effect of population, agent, interaction, and network characteristics, we find that some unobservable characteristics, like the initial distribution of opinions, play a significant role in rumour outcomes, particularly the homogeneity and polarisation of opinions. We report our findings on the mechanism and interactions and suggest heuristics for managers to counter the spread of unfavourable rumours.

We propose and analyze the first model for clinical trial design that integrates each of three important trends intending to improve the effectiveness of clinical trials that inform health-technology adoption decisions: adaptive design, which dynamically adjusts the sample size and allocation of interventions to different patients; multiarm trial design, which compares multiple interventions simultaneously; and value-based design, which focuses on cost-benefit improvements of health interventions over a current standard of care. Example applications are to seamless phase II/III dose-finding trials and to trials that test multiple combinations of therapies. Our objective is to maximize the expected population health-economic benefit of health-technology adoption decisions less clinical trial costs. We show that unifying the adaptive, multiarm, and value-based approaches to trial design can reduce the cost and duration of multiarm trials with efficient adaptive look ahead policies that focus on value to patients and account for correlated rewards across arms. Features that differentiate our approach from much other work on stochastic optimization include stopping times that balance sampling costs and the expected value of information of those samples, performance guarantees offered by new asymptotic convergence proofs, and the modeling of arms’ potentially different sampling costs. Our proposed solution can be computed feasibly and can randomize patients. The class of trials for the base model assumes that health-economic data are collected and observed quickly. Related work from Bayesian optimization can enable the further inclusion of trials with intermediate duration delays between the time of treatment initiation and observation of outcomes. This paper was accepted by Stefan Scholtes, healthcare management.

We consider the problem of sequentially allocating sample observations to learn personalized treatment strategies, motivated by the design of adaptive clinical trials that aim to learn the best treatment as a function of patient covariates. In such settings there may be clinical knowledge of which covariates are predictive (they may interact with the treatment choice) and which are prognostic (they may influence the outcome independent of treatment choice). We extend the expected value of information (EVI)/knowledge gradient framework to develop useful heuristics for a context with predictive and prognostic covariates and a delay in observing outcomes. We also propose and analyze closely related Monte Carlo-based allocation policies to enhance our proposal's computational efficiency and applicability for adaptive contextual learning. We show that several of our proposed allocation policies are asymptotically optimal in learning treatment strategies. We run simulation experiments motivated by an application for clinical trial design to assess potential treatments of sepsis. We illustrate that the proposed EVI-based allocation policies, with knowledge about which covariates are predictive and prognostic, can improve the rate of inference relative to some existing approaches to adaptive contextual learning.

We review epidemiological models for the propagation of the COVID-19 pandemic during the early months of the outbreak: from February to May 2020. The aim is to propose a methodological review that highlights the following characteristics: (i) the epidemic propagation models, (ii) the modeling of intervention strategies, (iii) the models and estimation procedures of the epidemic parameters and (iv) the characteristics of the data used. We finally selected 80 articles from open access databases based on criteria such as the theoretical background, the reproducibility, the incorporation of interventions strategies, etc. It mainly resulted to phenomenological, compartmental and individual-level models. A digital companion including an online sheet, a Kibana interface and a markdown document is proposed. Finally, this work provides an opportunity to witness how the scientific community reacted to this unique situation.

We consider the contextual ranking and selection problem that aims to learn the best treatment as a function of covariates when expected outcomes are unknown but can be learned from noisy observations. We develop a sequential allocation policy based on Bayesian expected value of information methods, called fEVI, to learn the best treatment for a finite set of covariates. We observe good performance of the $f$ EVI allocation policy in simulation experiments and find that prior distributions which accurately reflect correlations across treatments and patient types can improve sampling effectiveness with limited sample sizes. We compare the performance between the case when covariates are random arrivals from a population, and the case when the allocation policy chooses covariates. In experiments, the benefit of $f$ EVI over allocation policies that sample randomly is much larger than the benefit from being able to choose covariates, or from using a prior that accurately reflects correlations.

Background/Aims: There is growing interest in the use of adaptive designs to improve the efficiency of clinical trials. We apply a Bayesian decision-theoretic model of a sequential experiment using cost and outcome data from the ProFHER pragmatic trial. We assess the model’s potential for delivering value-based research. Methods: Using parameter values estimated from the ProFHER pragmatic trial, including the costs of carrying out the trial, we establish when the trial could have stopped, had the model’s value-based stopping rule been used. We use a bootstrap analysis and simulation study to assess a range of operating characteristics, which we compare with a fixed sample size design which does not allow for early stopping. Results: We estimate that application of the model could have stopped the ProFHER trial early, reducing the sample size by about 14%, saving about 5% of the research budget and resulting in a technology recommendation which was the same as that of the trial. The bootstrap analysis suggests that the expected sample size would have been 38% lower, saving around 13% of the research budget, with a probability of 0.92 of making the same technology recommendation decision. It also shows a large degree of variability in the trial’s sample size. Conclusions: Benefits to trial cost stewardship may be achieved by monitoring trial data as they accumulate and using a stopping rule which balances the benefit of obtaining more information through continued recruitment with the cost of obtaining that information. We present recommendations for further research investigating the application of value-based sequential designs.

Introduction: The current COVID-19 pandemic leads to a massive influx of patients to the ICU. Epidemic models focus on the demand for care capacity. Here, we present a model for the number of COVID-19 and non-COVID-19 patients that can be served for a given ICU capacity. The results may give direction to expansion decisions during the pandemic. Methods: We adapted a stochastic patient flow simulation model, originally designed to assess performance metrics for ICU design decisions, to support capacity management decisions for hospitals that are trying to assess the impact of a sustained increase in ICU demand for COVID-19 patients. We also account for the impact on such decisions for the ability to provide acceptable service levels to other ICU patients. Results: We report potential COVID-19 patient flow rates for a range of potential COVID-19 patient arrival rates and ICU capacity and estimate non-COVID-19 unplanned patient ICU capacity, based on data from the Amsterdam University Medical Centres, location AMC. We provide a link to web-based code for other hospitals to use.

Intensive care units are complicated hospital departments in that they have both urgent and elective patients with a variety of specialty needs that cannot be easily treated elsewhere. Their design involves important operations strategy decisions, such as whether there are general facilities serving all patients or several specialized units for certain patient needs, or something in between. They also involve bed capacity decisions for the aggregate and potentially specialized units. This paper presents a simulation model which is used to assess trade-offs in these operational design issues with respect to three performance measures (rejection rate, rescheduling rate, and bed occupancy rate), using data and design options for the Academic Medical Center (AMC), one of two locations forming the Amsterdam University Medical Centers (UMC).

Background Creutzfeldt–Jakob disease is a fatal neurological disease caused by abnormal infectious proteins called prions. Prions that are present on surgical instruments cannot be completely deactivated; therefore, patients who are subsequently operated on using these instruments may become infected. This can result in surgically transmitted Creutzfeldt–Jakob disease. Objective To update literature reviews, consultation with experts and economic modelling published in 2006, and to provide the cost-effectiveness of strategies to reduce the risk of surgically transmitted Creutzfeldt–Jakob disease. Methods Eight systematic reviews were undertaken for clinical parameters. One review of cost-effectiveness was undertaken. Electronic databases including MEDLINE and EMBASE were searched from 2005 to 2017. Expert elicitation sessions were undertaken. An advisory committee, convened by the National Institute for Health and Care Excellence to produce guidance, provided an additional source of information. A mathematical model was updated focusing on brain and posterior eye surgery and neuroendoscopy. The model simulated both patients and instrument sets. Assuming that there were potentially 15 cases of surgically transmitted Creutzfeldt–Jakob disease between 2005 and 2018, approximate Bayesian computation was used to obtain samples from the posterior distribution of the model parameters to generate results. Heuristics were used to improve computational efficiency. The modelling conformed to the National Institute for Health and Care Excellence reference case. The strategies evaluated included neither keeping instruments moist nor prohibiting set migration; ensuring that instruments were kept moist; prohibiting instrument migration between sets; and employing single-use instruments. Threshold analyses were undertaken to establish prices at which single-use sets or completely effective decontamination solutions would be cost-effective. Results A total of 169 papers were identified for the clinical review. The evidence from published literature was not deemed sufficiently strong to take precedence over the distributions obtained from expert elicitation. Forty-eight papers were identified in the review of cost-effectiveness. The previous modelling structure was revised to add the possibility of misclassifying surgically transmitted Creutzfeldt–Jakob disease as another neurodegenerative disease, and assuming that all patients were susceptible to infection. Keeping instruments moist was estimated to reduce the risk of surgically transmitted Creutzfeldt–Jakob disease cases and associated costs. Based on probabilistic sensitivity analyses, keeping instruments moist was estimated to on average result in 2.36 (range 0–47) surgically transmitted Creutzfeldt–Jakob disease cases (across England) caused by infection occurring between 2019 and 2023. Prohibiting set migration or employing single-use instruments reduced the estimated risk of surgically transmitted Creutzfeldt–Jakob disease cases further, but at considerable cost. The estimated costs per quality-adjusted life-year gained of these strategies in addition to keeping instruments moist were in excess of £1M. It was estimated that single-use instrument sets (currently £350–500) or completely effective cleaning solutions would need to cost approximately £12 per patient to be cost-effective using a £30,000 per quality-adjusted life-year gained value. Limitations As no direct published evidence to implicate surgery as a cause of Creutzfeldt–Jakob disease has been found since 2005, the estimations of potential cases from elicitation are still speculative. A particular source of uncertainty was in the number of potential surgically transmitted Creutzfeldt–Jakob disease cases that may have occurred between 2005 and 2018. Conclusions Keeping instruments moist is estimated to reduce the risk of surgically transmitted Creutzfeldt–Jakob disease cases and associated costs. Further surgical management strategies can reduce the risks of surgically transmitted Creutzfeldt–Jakob disease but have considerable associated costs. Study registration This study is registered as PROSPERO CRD42017071807. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment ; Vol. 24, No. 11. See the NIHR Journals Library website for further project information.

How are the populations of the world likely to shift? Which countries will be impacted by sea-level rise? This paper uses a country-level agent-based dynamic network model to examine shifts in population given network relations among countries, which influences overall population change. Some of the networks considered include: alliance networks, shared language networks, economic influence networks, and proximity networks. Validation of model is done for migration probabilities between countries, as well as for country populations and distributions. The proposed framework provides a way to explore the interaction between climate change and policy factors at a global scale. Bureau 2016). The age distribution is then shifted throughout the simulation through an aging process, as well as actual births and deaths in population. We then validate our model against data for migration probabilities, and country-level observations (population and age distributions). The results are promising, as we illustrate through performance measures such as average of prediction error.