A Value of Information (VOI) analysis can play a key role in decision-making for adopting new approach methodologies (NAMs). We applied EPA's recently developed VOI framework to the Threshold of ...Toxicological Concern (TTC). Obtaining/deriving a TTC value for use as a toxicity reference value (TRV) for substances with limited toxicity data was shown to provide equivalent or greater health protection, immense return on investment (ROI), greater net benefit, and substantially lower costs of delay (CoD) compared with TRVs derived from either traditional human health assessment (THHA) chronic toxicity testing in lab animals or the 5-day in vivo EPA Transcriptomic Assessment Product (ETAP). For all nine exposure scenarios examined, the TTC was more economical terms of CoD and ROI than the ETAP or the THHA; expected net benefit was similar for the TTC and ETAP with both of these more economical than the THHA The TTC ROI was immensely greater (5,000,000-fold on average) than the ROI for THHA and the ETAP ROI (100,000-fold on average). These results support the use of the TTC for substances within its domain of applicability to waive requiring certain in vivo tests, or at a minimum, as an initial screening step before conducting either the ETAP or THHA in vivo studies.
•EPA's recently developed VOI framework was applied to the Threshold of Toxicological Concern (TTC).•The return on investment for the TTC was immensely greater than either animal method considered by EPA.•The TTC is consistent with the TSCA statute and is suggested as a valuable screening step in chemical hazard assessment.
Healthcare resource allocation decisions made under conditions of uncertainty may turn out to be suboptimal. In a resource constrained system in which there is a fixed budget, these suboptimal ...decisions will result in health loss. Consequently, there may be value in reducing uncertainty, through the collection of new evidence, to make better resource allocation decisions. This value can be quantified using a value of information (VOI) analysis. This report, from the ISPOR VOI Task Force, introduces VOI analysis, defines key concepts and terminology, and outlines the role of VOI for supporting decision making, including the steps involved in undertaking and interpreting VOI analyses. The report is specifically aimed at those tasked with making decisions about the adoption of healthcare or the funding of healthcare research. The report provides a number of recommendations for good practice when planning, undertaking, or reviewing the results of VOI analyses.
•Decision uncertainty, although not relevant to a risk-neutral decision maker identifying the optimal choice in the current circumstances, is of interest for addressing the question of whether to collect additional information to better inform future decisions. As such, probability distributions should be assigned to parameters to characterize uncertainty in the current evidence base, with probabilistic analysis (PA) used to assess the uncertainty. Parameters excluded from the PA will be excluded from the analysis of uncertainty.•A value of information (VOI) analysis provides a formal assessment of the value of research, based on the extent to which the information generated through research would improve the expected payoffs associated with a decision by reducing the uncertainty surrounding it. This value can then be compared with the cost of acquiring the information to determine whether the research is potentially worthwhile and of value to undertake.•This report was written to provide decision makers who have been tasked with making decisions about the adoption of healthcare or the funding of healthcare research with an introduction to the concept of VOI analysis and to the decisions that can be supported by this type of analysis, including: (1) research prioritization, (2) efficient research design, (3) reimbursement, and (4) efficient decision making over the life cycle.•The report describes the process of VOI analysis, providing a top-level description of the methods and steps involved in undertaking and interpreting the results of such an analysis, from conceptualizing the decision problem to developing the decision model, parameterizing the model, running the probabilistic analysis, calculating the value of information (perfect, partial perfect, and sample), and determining the worth of research (expected net benefit of sampling).•This report provides 9 recommendations for good practice when planning, undertaking, or reviewing the results of VOI analyses with the aim to improve accessibility of VOI analysis for all stakeholders.
This paper proposes an expected value and chance constrained stochastic optimization approach for the unit commitment problem with uncertain wind power output. In the model, the utilization of wind ...power can be adjusted by changing the utilization rate in the proposed expected value constraint. Meanwhile, the chance constraint is used to restrict the probability of load imbalance. Then a Sample Average Approximation (SAA) method is used to transform the objective function, the expected value constraint, and the chance constraint into sample average reformulations. Furthermore, a combined SAA framework that considers both the expected value and the chance constraints is proposed to construct statistical upper and lower bounds for the optimization problem. Finally, the performance of the proposed algorithm with different utilization rates and different risk levels is tested for a six-bus system. A revised IEEE 118-bus system is also studied to show the scalability of the proposed model and algorithm.
This article is devoted to establish the distributions for Sombor indices in a general random chain, in which their explicit analytical expressions of the expected values and variances are obtained. ...As applications, these results for random hexagonal, random phenylene, random polyphenyl and random spiro chains are given. Finally, the distributions for the Sombor indices in these four random chains are asymptotic to normal distributions is shown.
Respiratory syncytial virus (RSV) imposes a substantial burden on pediatric hospital capacity in Europe. Promising prophylactic interventions against RSV including monoclonal antibodies (mAb) and ...maternal immunizations (MI) are close to licensure. Therefore, we aimed to evaluate the cost-effectiveness of potential mAb and MI interventions against RSV in infants, for six European countries.
We used a static cohort model to compare costs and health effects of four intervention programs to no program and to each other: year-round MI, year-round mAb, seasonal mAb (October to April), and seasonal mAb plus a catch-up program in October. Input parameters were obtained from national registries and literature. Influential input parameters were identified with the expected value of partial perfect information and extensive scenario analyses (including the impact of interventions on wheezing and asthma).
From the health care payer perspective, and at a price of €50 per dose (mAb and MI), seasonal mAb plus catch-up was cost-saving in Scotland, and cost-effective for willingness-to-pay (WTP) values ≥€20,000 (England, Finland) or €30,000 (Denmark) per quality adjusted life-year (QALY) gained for all scenarios considered, except when using ICD-10 based hospitalization data. For the Netherlands, seasonal mAb was preferred (WTP value: €30,000-€90,000) for most scenarios. For Veneto region (Italy), either seasonal mAb with or without catch-up or MI was preferred, depending on the scenario and WTP value. From a full societal perspective (including leisure time lost), the seasonal mAb plus catch-up program was cost-saving for all countries except the Netherlands.
The choice between a MI or mAb program depends on the level and duration of protection, price, availability, and feasibility of such programs, which should be based on the latest available evidence. Future research should focus on measuring accurately age-specific RSV-attributable hospitalizations in very young children.
Impulse control disorders (ICD) in Parkinson’s disease (PD) are associated with a heavy burden on patients and caretakers. While recovery can occur, ICD persists in many patients despite optimal ...management. The basis for this inter-individual variability in recovery is unclear and poses a major challenge to personalized health care.
We adopt a computational psychiatry approach and leverage the longitudinal, prospective Personalized Parkinson Project (N=136 persons with PD, within 5 years of diagnosis) to combine dopaminergic learning theory-informed fMRI with machine learning (at baseline) to predict ICD symptom recovery after two years of follow-up. We focused on a change in QUIP-rs across the entire cohort, regardless of an ICD diagnosis.
Greater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum, medial prefrontal cortex and the behavioral accuracy score measured while ON medication were associated with greater recovery from impulse control symptoms two years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use.
Our results provide a proof of principle for combining generative model-based inference of latent learning processes with machine learning-based predictive modeling of variability in clinical symptom recovery trajectories. Hence, we showed that RL modelling parameters predict recovery from ICD symptoms in PD
Early adverse experiences are assumed to affect fundamental processes of reward learning and decision making. However, computational neuroimaging studies investigating these circuits in the context ...of adversity are sparse and limited to studies conducted in adolescent samples, leaving the long-term effects unexplored.
Using data from a longitudinal birth cohort study (n = 156; 87 female), we investigated associations between adversities and computational markers of reward learning (i.e., expected value, prediction errors). At age 33 years, all participants completed a functional magnetic resonance imaging–based passive avoidance task. Psychopathology measures were collected at the time of functional magnetic resonance imaging investigation and during the COVID-19 pandemic. We applied a principal component analysis to capture common variations across 7 adversity measures. The resulting adversity factors (factor 1: postnatal psychosocial adversities and prenatal maternal smoking; factor 2: prenatal maternal stress and obstetric adversity; factor 3: lower maternal stimulation) were linked with psychopathology and neural responses in the core reward network using multiple regression analysis.
We found that the adversity dimension primarily informed by lower maternal stimulation was linked to lower expected value representation in the right putamen, right nucleus accumbens, and anterior cingulate cortex. Expected value encoding in the right nucleus accumbens further mediated the relationship between this adversity dimension and psychopathology and predicted higher withdrawn symptoms during the COVID-19 pandemic.
Our results suggested that early adverse experiences in caregiver context might have a long-term disruptive effect on reward learning in reward-related brain regions, which can be associated with suboptimal decision making and thereby may increase the vulnerability of developing psychopathology.
The subtree number index STN(G) of a simple graph G is the number of nonempty subtrees of G. It is a structural and counting topological index that has received more and more attention in recent ...years. In this paper we first obtain exact formulas for the expected values of subtree number index of random polyphenylene and spiro chains, which are molecular graphs of a class of unbranched multispiro molecules and polycyclic aromatic hydrocarbons. Moreover, we establish a relation between the expected values of the subtree number indices of a random polyphenylene and its corresponding hexagonal squeeze. We also present the average values for subtree number indices with respect to the set of all polyphenylene and spiro chains with n hexagons.
The highly influential Allingham and Sandmo model of income tax evasion assumes that taxpayers are driven by utility maximization, choosing evasion over compliance if it yields a higher expected ...profit. We test the main assumptions of this so‐called deterrence approach considering both compliance decisions and the process of information acquisition using MouselabWEB. In an incentivized experiment, 109 participants made 24 compliance decisions with varying information presented for four within‐subject factors (the four central model parameters: income, tax rate, audit probability, and fine level). Additionally, explicit expected value information was indicated in one of two conditions. The results reveal that participants attended to all relevant information, a prerequisite for expected value‐like calculations. As predicted by the deterrence model, choices were clearly influenced by audit probability and fine level. Against the model assumptions, the presented parameters were not integrated adequately, indicated by a non‐monotonic increase of evasion with rising expected rate of return from evasion. Additionally, more transitions between information necessary for calculating expected values did not result in higher model conformity, just as presenting explicit information on expected values. We conclude that deterrence information clearly influences tax compliance decisions in our setting but observed deviations from the deterrence model can be attributed to failures to properly integrate all relevant parameters.
Uncertain fractional differential equation (UFDE) is of importance tool for the description of uncertain dynamic systems. Generally we may not obtain its analytic solutions in most cases. This paper ...focuses on proposing a numerical method for solving UFDE involving Caputo derivative. First, the concept of α-path to an UFDE with initial value conditions is introduced, which is a solution of the corresponding fractional differential equation (FDE) involving with the same initial value conditions. Then the relations between its solution and associate α-path are investigated. Besides, a formula is derived for calculating expected value of a monotonic function with respect to solutions of UFDEs. Based on the established relations, numerical algorithms are designed. Finally, some numerical experiments of nonlinear UFDEs are given to demonstrate the effectiveness of the numerical algorithms.
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