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We introduce a new fairness measure for designing organ allocation rules for transplantation. In particular, we apply the proportional fairness measure for organ transplantation whose solution can be interpreted as the solution to the Nash bargaining problem for sharing limited donor organs among patients who seek to maximize their Quality-Adjusted Life-Years (QALYs). The motivation arises from several observations that current measures of fairness induce significant inefficiencies in terms of total QALYs of the patient population. We use the asymptotic results for the fluid approximation of a transplant queuing system to estimate the expected utility of patients and formulate an optimization problem where the decision maker partitions the set of organ types to achieve a proportionally fair objective. We use an achievable region approach to transform our formulation to an alternative optimization problem and show that the optimal allocation policy under the proportional fairness measure is assortative, which has the following insight: higher quality organs are allocated to patients who will have higher expected QALYs. We compare the performance of allocation rules developed for fairness purposes in organ allocation along with our proposed policy via a validated simulation model for heart transplantation in the US. [ABSTRACT FROM AUTHOR]

Identifying the right dose is one of the most important decisions in drug development. Adaptive designs are promoted to conduct dose-finding clinical trials as they are more efficient and ethical compared with static designs. However, current techniques in response-adaptive designs for dose allocation are complex and need significant computational effort, which is a major impediment for implementation in practice. This study proposes a Bayesian nonparametric framework for estimating the dose-response curve, which uses a piecewise linear approximation to the curve by consecutively connecting the expected mean response at each dose. Our extensive numerical results reveal that a first-order Bayesian nonparametric model with a known correlation structure in prior for the expected mean response performs competitively when compared with the standard approach and other more complex models in terms of several relevant metrics and enjoys computational efficiency. Furthermore, structural properties for the optimal learning problem, which seeks to minimize the variance of the target dose, are established under this simple model. Summary of Contribution: In this work, we propose a methodology to derive efficient patient allocation rules in response-adaptive dose-finding clinical trials, where computational issues are the main concern. We show that our methodologies are competitive with the state-of-the-art methodology in terms of solution quality, are significantly more computationally efficient, and are more robust in terms of the shape of the dose-response curve, among other parameter changes. This research fits in "the intersection of computing and operations research" as it adapts operations research techniques to produce computationally attractive solutions to patient allocation problems in dose-finding clinical trials. [ABSTRACT FROM AUTHOR]

In this work, we study the optimal design of two-armed clinical trials to maximize the accuracy of parameter estimation in a statistical model, where the interaction between patient covariates and treatment are explicitly incorporated to enable precision medication decisions. Such a modeling extension leads to significant complexities for the produced optimization problems because they include optimization over design and covariates concurrently. We take a min-max optimization model and minimize (over design) the maximum (over population) variance of the estimated interaction effect between treatment and patient covariates. This results in a min-max bilevel mixed integer nonlinear programming problem, which is notably challenging to solve. To address this challenge, we introduce a surrogate optimization model by approximating the objective function, for which we propose two solution approaches. The first approach provides an exact solution based on reformulation and decomposition techniques. In the second approach, we provide a lower bound for the inner optimization problem and solve the outer optimization problem over the lower bound. We test our proposed algorithms with synthetic and real-world data sets and compare them with standard (re)randomization methods. Our numerical analysis suggests that the proposed approaches provide higher-quality solutions in terms of the variance of estimators and probability of correct selection. We also show the value of covariate information in precision medicine clinical trials by comparing our proposed approaches to an alternative optimal design approach that does not consider the interaction terms between covariates and treatment. Summary of Contribution: Precision medicine is the future of healthcare where treatment is prescribed based on each patient information. Designing precision medicine clinical trials, which are the cornerstone of precision medicine, is extremely challenging because sample size is limited and patient information may be multidimensional. This work proposes a novel approach to optimally estimate the treatment effect for each patient type in a two-armed clinical trial by reducing the largest variance of personalized treatment effect. We use several statistical and optimization techniques to produce efficient solution methodologies. Results have the potential to save countless lives by transforming the design and implementation of future clinical trials to ensure the right treatments for the right patients. Doing so will reduce patient risks and reduce costs in the healthcare system. [ABSTRACT FROM AUTHOR]

Several studies have shown that biomass cofiring is a viable short-term option for coal-fired power plants to reduce their emissions if supported by appropriate tax incentives. These results suggest a unique opportunity for governments to design monetary incentives such as tax credits that lead to reduction in greenhouse gas emissions in biomass-rich regions. Therefore, a natural question is: What is an optimal tax credit strategy in these regions? To this end, we propose a Stackelberg/Nash game, and solve it algorithmically via reformulating the model as a mixed-integer bilinear program and using a piecewise linear relaxation of bilinear terms. The structure of the optimal solution of special cases is exploited, which helps design efficient heuristics. This study develops a case study using real data about power plants and biomass availability in Mississippi and Arkansas. The results compare the optimal tax credit schemes and plants' cofiring strategies to provide insights on optimal tax credit mechanisms. Results show that a flexible tax credit scheme, which allows a plant-specific tax credit rate, is more efficient than the currently-used flat tax credit rate. This proposed approach uses a smaller budget and targets the plants that need funding support to comply with emissions regulations. [ABSTRACT FROM AUTHOR]

We consider the problem of dynamic allocation of organs to patients in a transplant system. The system is modeled as a multiclass bipartite matching system, in which patients may die/delist or move between classes due to changes of their health status. We study a stochastic queueing control problem (QCP) with the control process governing the allocation of each arriving organ, and the objective of maximizing the expected total life years, which consists of both pre- and posttransplant years, of the entire population in the system during a finite time horizon. We first construct a deterministic control problem, referred to as the fluid control problem (FCP), and show that it serves as a performance upper bound for the QCP. We next develop an asymptotic framework, in which large scaled overloaded transplant systems are considered, and show that the fluid scaled QCP attains the FCP upper bound asymptotically. We then propose a simple priority type policy for the QCP based on the optimal solution of the FCP, and establish its asymptotic optimality through a scaling limit theorem. Last, we conduct sensitivity analysis of the FCP with respect to the input parameters and functions to demonstrate the robustness of the proposed policy. [ABSTRACT FROM AUTHOR]

Let X be a completely regular topological space. For each closed non-vanishing ideal H of CB(X), the normed algebra of all bounded continuous scalar-valued mappings on X equipped with pointwise addition and multiplication and the supremum norm, we study its spectrum, denoted by 𝔰𝔭(H). We make a correspondence between algebraic properties of H and topological properties of 𝔰𝔭(H). This continues some previous studies, in which topological properties of 𝔰𝔭(H) such as the Lindelöf property, paracompactness, σ-compactness and countable compactness have been made into correspondence with algebraic properties of H. We study here other compactness properties of 𝔰𝔭(H) such as weak paracompactness, sequential compactness and pseudocompactness. We also study the ideal isomorphisms between two non-vanishing closed ideals of CB(X). [ABSTRACT FROM AUTHOR]

We consider the problem of optimally maintaining an offshore wind farm in which major components progressively degrade over time due to normal usage and exposure to a randomly varying environment. The turbines exhibit both economic and stochastic dependence due to shared maintenance setup costs and their common environment. Our aim is to identify optimal replacement policies that minimize the expected total discounted setup, replacement, and lost power production costs over an infinite horizon. The problem is formulated using a Markov decision process (MDP) model from which we establish monotonicity of the cost function jointly in the degradation level and environment state and characterize the structure of the optimal replacement policy. For the special case of a two-turbine farm, we prove that the replacement threshold of one turbine depends not only on its own state of degradation but also on the state of degradation of the other turbine in the farm. This result yields a complete characterization of the replacement policy of both turbines by a monotone curve. The policies characterized herein can be used to optimally prescribe timely replacements of major components and suggest when it is most beneficial to share costly maintenance resources. [ABSTRACT FROM AUTHOR]

Motivated to apply sustainable supply chain principles to air-pollution control systems, this paper presents a dynamic inventory-management approach where substitution is possible to maintain these systems' equipment. An air-pollution control system's subsequent reliability depends on the replacement equipment selected. The corresponding problem is formulated as a stochastic dynamic program. Because the state and action space are prohibitively large, the approximate policy iteration algorithm is adapted to generate high-quality solutions. Therefore, this work replaces the value function with an affine combination of nonlinear basis functions and shows that a relaxation of the policy improvement step requires the solving of a mixed integer linear program. This approach helps in the designing of an algorithm that improves the quality of the approximation by solving a convex optimization problem. To assess the quality of resulting solutions, a lower bound is developed by considering a relaxation of the problem. In addition, two classes of heuristics are proposed based on a rolling-horizon two-stage stochastic programming formulation of the problem and a standard base-stock ordering policy. The performance of proposed policies is tested on a variety of settings, and results show that the approximate dynamic programming policies are near-optimal in the settings of interest and significantly outperformavailable benchmarks. The following analysis reveals that the proposed inventory replenishment policies resemble a base-stock policy with occasional deviations, and assignment and substitution decisions are determined by balancing the reliability with ordering, holding, and shortage costs. [ABSTRACT FROM AUTHOR]

Background: The effects of antiretroviral treatment on the HIV epidemic are complex. HIV-infected individuals survive longer with treatment, but are less likely to transmit the disease. The standard coverage measure improves with the deaths of untreated individuals and does not consider the fact that some individuals may acquire the disease and die before receiving treatment, making it susceptible to overestimating the long-run performance of antiretroviral treatment programs.Objective: The objective was to propose an alternative coverage definition to better measure the long-run performance of HIV treatment programs.Methods: We introduced cumulative incidence-based coverage as an alternative to measure an HIV treatment program's success. To numerically compare the definitions, we extended a simulation model of HIV disease and treatment to represent a dynamic population that includes uninfected and HIV-infected individuals. Also, we estimated the additional resources required to implement various treatment policies in a resource-limited setting.Results: In a synthetic population of 600,000 people of which 44,000 (7.6%) are infected, and eligible for treatment with a CD4 count of less than 500 cells/mm(3), assuming a World Health Organization (WHO)-defined coverage rate of 50% of eligible people, and treating these individuals with a single treatment regimen, the gap between the current WHO coverage definition and our proposed one is as much as 16% over a 10-year planning horizon.Conclusions: Cumulative incidence-based definition of coverage yields a more accurate representation of the long-run treatment success and along with the WHO and other definitions of coverage provides a better understanding of the HIV treatment progress. [ABSTRACT FROM AUTHOR]

Background: Many analyses of HIV treatment decisions assume a fixed formulary of HIV drugs. However, new drugs are approved nearly twice a year, and the rate of availability of new drugs may affect treatment decisions, particularly when to initiate antiretroviral therapy (ART). Objectives: To determine the impact of considering the availability of new drugs on the optimal initiation criteria for ART and outcomes in patients with HIV/AIDS. Methods: We enhanced a previously described simulation model of the optimal time to initiate ART to incorporate the rate of availability of new antiviral drugs. We assumed that the future rate of availability of new drugs would be similar to the past rate of availability of new drugs, and we estimated the past rate by fitting a statistical model to actual HIV drug approval data from 1982–2010. We then tested whether or not the future availability of new drugs affected the model-predicted optimal time to initiate ART based on clinical outcomes, considering treatment initiation thresholds of 200, 350, and 500 cells/mm3. We also quantified the impact of the future availability of new drugs on life expectancy (LE) and quality-adjusted life expectancy (QALE). Results: In base case analysis, considering the availability of new drugs raised the optimal starting CD4 threshold for most patients to 500 cells/mm3. The predicted gains in outcomes due to availability of pipeline drugs were generally small (less than 1%), but for young patients with a high viral load could add as much as a 4.9% (1.73 years) increase in LE and a 8% (2.43 QALY) increase in QALE, because these patients were particularly likely to exhaust currently available ART regimens before they died. In sensitivity analysis, increasing the rate of availability of new drugs did not substantially alter the results. Lowering the toxicity of future ART drugs had greater potential to increase benefit for many patient groups, increasing QALE by as much as 10%. Conclusions: The future availability of new ART drugs without lower toxicity raises optimal treatment initiation for most patients, and improves clinical outcomes, especially for younger patients with higher viral loads. Reductions in toxicity of future ART drugs could impact optimal treatment initiation and improve clinical outcomes for all HIV patients. [ABSTRACT FROM AUTHOR]

We developed a model to compare the impacts of different lifestyle interventions among prediabetes individuals and to identify the optimal age groups for such interventions. A stochastic simulation was developed to replicate the prediabetes and diabetes trends (1997–2010) in the U.S. adult population. We then simulated the population-wide impacts of three lifestyle diabetes prevention programs, i.e., the Diabetes Prevention Program (DPP), DPP-YMCA, and the Healthy Living Partnerships to Prevent Diabetes (HELP-PD), over a course of 10, 15 and 30 years. Our model replicated the temporal trends of diabetes in the U.S. adult population. Compared to no intervention, the diabetes incidence declined 0.3 per 1,000 by DPP, 0.2 by DPP-YMCA, and 0.4 by HELP-PD over the 15-year period. Our simulations identified HELP-PD as the most cost-effective intervention, which achieved the highest 10-year savings of $38 billion for those aged 25–65, assuming all eligible individuals participate in the intervention and considering intervention achievement rates. Our model simulates the diabetes trends in the U.S. population based on individual-level longitudinal data. However, it may be used to identify the optimal intervention for different subgroups in defined populations. [ABSTRACT FROM AUTHOR]

African American adults, in the United States (US), bear the greatest burden of high blood pressure (HBP)--a primary risk factor for cardiovascular disease and premature death. Current research does not adequately inform the design of multi-level interventions that work to control HBP in this at-risk population. The purpose of this study was to uncover information about priority strategies recommended for HBP control in African American adults. Research questions are 1) what are factors that reduce or manage HBP in at-risk African Americans and 2) what factors are important to the design and implementation of successful HBP interventions in at-risk African Americans? A qualitative interpretive descriptive design and in-depth interview were used with a purposive sample (n=10) of doctorate-educated healthcare clinicians (i.e., physicians) and academicians (i.e., researchers) based on eligibility criteria: a) experience in treating or conducting research about HBP in African American adults, b) located within the Southern US, and c) consent to participate. Thematic analysis of audio-taped interview transcripts yielded a theoretical framework that consists of three multi-level elements believed to be critical components of interventions that can successfully manage HBP in at-risk African Americans: 1) social support, 2) lifestyle coaching, and 3) personalized medical management. Unique domains within the each element were revealed that ranged from stress management and holism to emotional resonant patient-caregiver partnership and experiential learning. Our findings were consistent with the social ecological model and have the potential to help address racial/ethnic-based health disparities through the design of patient-centered interventions. Findings will also be used to identify parameters available to simulation modelers in the design of models for optimal population level HBP control policy. [ABSTRACT FROM AUTHOR]

Background: New York City (NYC) remains an epicenter of the HIV epidemic in the United States. Given the variety of evidence-based HIV prevention strategies available and the significant resources required to implement each of them, comparative studies are needed to identify how to maximize the number of HIV cases prevented most economically. Methods: A new model of HIV disease transmission was developed integrating information from a previously validated micro-simulation HIV disease progression model. Specification and parameterization of the model and its inputs, including the intervention portfolio, intervention effects and costs were conducted through a collaborative process between the academic modeling team and the NYC Department of Health and Mental Hygiene. The model projects the impact of different prevention strategies, or portfolios of prevention strategies, on the HIV epidemic in NYC. Results: Ten unique interventions were able to provide a prevention benefit at an annual program cost of less than $360,000, the threshold for consideration as a cost-saving intervention (because of offsets by future HIV treatment costs averted). An optimized portfolio of these specific interventions could result in up to a 34% reduction in new HIV infections over the next 20 years. The cost-per-infection averted of the portfolio was estimated to be $106,378; the total cost was in excess of $2 billion (over the 20 year period, or approximately $100 million per year, on average). The cost-savings of prevented infections was estimated at more than $5 billion (or approximately $250 million per year, on average). Conclusions: Optimal implementation of a portfolio of evidence-based interventions can have a substantial, favorable impact on the ongoing HIV epidemic in NYC and provide future cost-saving despite significant initial costs. [ABSTRACT FROM AUTHOR]