Uncertainty In Values Research Articles

Valuing the Societal Impact of Medicines and Other Health Technologies: A User Guide to Current Best Practices

Abstract This study argues that value assessment conducted from a societal perspective should rely on the Generalized Cost-Effectiveness Analysis (GCEA) framework proposed herein. Recently developed value assessment inventories – such as the Second Panel on Cost-Effectiveness’s “impact inventory” and International Society of Pharmacoeconomics Outcomes Research (ISPOR) “value flower” – aimed to more comprehensively capture the benefits and costs of new health technologies from a societal perspective. Nevertheless, application of broader value elements in practice has been limited in part because quantifying these elements can be complex, but also because there have been numerous methodological advances since these value inventories have been released (e.g. generalized and risk-adjusted cost effectiveness). To facilitate estimation of treatment value from a societal perspective, this paper provides an updated value inventory – called the GCEA value flower – and a user guide for implementing GCEA for health economics researchers and practitioners. GCEA considers 15 broader value elements across four categories: (i) uncertainty, (ii) dynamics, (iii) beneficiary, and (iv) additional value components. The uncertainty category incorporates patient risk preferences into value assessment. The dynamics category petals account for the evolution of real-world treatment value (e.g. option value) and includes drug pricing trends (e.g. future genericization). The beneficiary category accounts for the fact health technologies can benefit others (e.g. caregivers) and also that society may care to whom health benefits accrue (e.g. equity). Finally, GCEA incorporates additional broader sources of value (e.g. community spillovers, productivity losses). This GCEA user guide aims to facilitate both the estimation of each of these value elements and the incorporation of these values into health technology assessment when conducted from a societal perspective.

Uncertainty Quantification in the Prediction of Remaining Useful Life Considering Multiple Failure Modes

Abstract Despite the substantive literature on remaining useful life (RUL) prediction, less attention is paid to the influence of epistemic uncertainty and aleatory uncertainty in multiple failure behaviors in the accuracy of RUL. The research question in this study was: can uncertainties be quantified in predicting the RUL of systems with multiple failure modes? The first objective was to quantify the uncertainties in the prediction of RUL, considering known multiple failure modes. This objective used vibration data from accelerated degradation experiments of rolling element bearings. The second objective was to calculate the uncertainties in the prediction of RUL, considering the multiple failure modes as unknown. The experimental data used in this objective were from run-to-failure tests of Li-ion batteries. An analysis was performed on how the uncertainties affect the RUL prediction in systems with known multiple failure modes and systems where the multiple failure modes were unknown. A Bayesian neural network (BNN) was used to quantify epistemic and aleatory uncertainty while predicting RUL. The results of the qualitative uncertainties on RUL in systems with multiple failure modes were presented and discussed. Also, the study yielded an RUL uncertainty quantification model for multiple failure modes. The proposed framework's performance in the RUL prediction was demonstrated. Finally, the epistemic and aleatory uncertainties were quantified in the system's RUL. It was shown that systems that fail due to the same failure mode tend to have similar uncertainty values over time. The results in this paper may lead to the design of more reliable systems that exhibit multiple failure modes.

Online Retailing Operations: Is It Wise to Offer Free Sample Trial and Money-Back Guarantee Together?

In retail operations, free sample trial (FST) and product return with money-back guarantee (MBG) both serve as important strategies to reduce consumers’ worries and valuation uncertainty. This paper is the first study which analytically examines when and whether an online retailer should provide both strategies simultaneously, and when one strategy outperforms the other. We consider an online retailer selling to consumers who are uncertain about the product values when making purchasing decisions. The retailer decides whether to offer MBG or FST. We develop analytical models to examine four strategies: strategy NN (both FST and MBG are not provided), strategy NS (FST is provided and MBG is not offered), strategy MN (FST is not provided and MBG is provided), and strategy MS (both FST and MBG are offered). Comparing among these four strategies, we find that when only FST is offered, the retailer surprisingly should decrease the price if the product cost is low. If the retailer only provides MBG, he will charge a higher price compared with the situation when MBG is not provided. When the size of free sample is very small, strategy NS (MS) is the optimal strategy if the product's cost is low (high). When the size of free sample is large, strategy MN is the optimal strategy. Then, we extend our model to consider three situations, namely: (a) the retailer offers coupons to consumers to experience the free sample, (b) consumer returns incur a cost, and (c) the size of free sample is endogenous. We find the main conclusion remains robust. Our findings may help explain some real-world practices, such as why some retailers (e.g., Amazon and Walmart) provide consumers with low volume toners to decrease the free sample cost; skincare product retailers such as Lancôme, Estee Lauder, and Guerlain implement both MBG and FST. We also interestingly show that a bigger size of free sample may not necessarily benefit consumers.

Choosing between additive and conventional manufacturing of spare parts: On the impact of failure rate uncertainties and the tools to reduce them

Recently, there has been great interest in using additive manufacturing (AM) to produce spare parts: allowing to produce spare parts with short lead times and close to the point of use, AM reduces the need for large inventories otherwise required by conventional manufacturing (CM) techniques to deal with intermittent spare parts demand. However, using AM to produce spare parts is limited by two main drawbacks: high production costs and uncertain failure rates arising from AM still being a relatively new production technique. While the former can be counterbalanced by inventory cost reductions, it is unclear how the latter impacts the sourcing option decision (i.e. AM or CM). We aim to fill this gap by studying a periodic review model in which spare parts demands follow a Poisson process. To make our analysis accurate and reliable, we leverage a material science approach to obtain realistic values for the failure rate uncertainties. From the results, it emerges that AM is heavily penalized by failure rate uncertainties much higher than those of CM. Consequently, we then focus on some recent tools developed to reduce AM failure rate uncertainties: porosity assessment and in-situ monitoring. We find that the failure rate uncertainty should be reduced by five and six percentage points to make it worthwhile to invest in porosity assessment and in-situ monitoring, respectively. Finally, we determine under which circumstances each tool is preferred over the other and found that porosity assessment is typically the most competitive uncertainty reduction tool.

A game-based power system planning approach considering real options and coordination of all types of participants

With the ongoing advancement of global power marketization, the increasing diversity of stakeholders in the power market and their interactions have a significant impact on power system planning and operation. Traditional methods employ game theory to describe the interactions among market participants. However, these approaches fail to include all relevant participant types and overlook the long-term uncertainty value of planning schemes. Therefore, this paper proposes a game theory-based power system planning approach considering real options (RO) and coordination of all types of participants. First, the dynamic gaming interaction among supply-transmission-demand is studied, and the independent system operator is integrated into the game planning framework based on the overall perspective of power market operation. Moreover, the new power supply, grid planning, and operation of distributed generation (DG) are taken as decision variables based on the RO theory, and a game decision model of all participants in the power system planning considering the investment uncertainty is constructed. Finally, the iterative search algorithm is employed to solve the model, and the framework is validated based on the IEEE 30-bus system. The results indicate that through collaborative planning involving various market participants, Generation Companies and Transmission Companies expand their investments to maximize profits, while large power consumers reduce their investment in DG and instead increase their direct power purchases to lower costs. Moreover, these participants are inclined to pursue projects with higher asset value volatility, which further enhances their profit. By integrating RO theory with game theory, the proposed approach establishes a comprehensive planning framework that effectively addresses investment uncertainty. This integration enables market participants to make more optimal decisions, ultimately leading to an enhancement in the long-term economic performance of the power system.

OTHR-14 THE AFRICAN NEURO-ONCOLOGY ACCESS DISPARITY (ANAD) INDEX: A PUBLIC DATA-DRIVEN APPROACH TO MAPPING NATIONAL VARIATIONS IN NEURO-ONCOLOGY CARE BARRIERS

Abstract INTRODUCTION The burden of central nervous system tumors is unevenly distributed globally. In Africa, this burden is further compounded by a lack of cancer-focused health system strengthening. The authors sought to quantify these system-level challenges with a novel index. METHODS This prototype index was developed from publicly available data provided by the World Health Organization’s Global Health Observatory, the World Bank Group’s Country Database, and the Institute For Health Metrics and Evaluation’s Global Burden of Disease database. Metrics were selected based on their ability to represent health system components including workforce, service delivery, infrastructure, information management, health financing, and governance. The metrics were normalized either using standardization or min-max scaling. Next, they were weighted iteratively based on importance, relative impact, and expert judgment. Also, the weighted metrics were grouped into facilitators or obstructors. The difference between obstructors and facilitators for each country was percentile ranked to obtain the index score between 0 and 100. Finally, sensitivity analysis was conducted to test best- and worst-case scenarios based on uncertainty values. The t-test and ANOVA were used to evaluate association between country subregion/income and the index. RESULTS Data was found for 51 African countries. Ethiopia (98), Madagascar (96), and Mali (94) had the highest ANAD Index score whereas Gabon (0), Seychelles (2), and Mauritius (4) had the lowest scores. North African countries had lower ANAD Index scores than sub-Saharan African countries (mean (SD) 19 (4) vs. 54 (4), p=0.005). Also, low-income countries had higher ANAD Index scores than lower-middle- and upper-middle-income countries (72 (19) vs. 37 (27) vs. 9 (12), p<0.001). CONCLUSION This prototype will be further tested for face, construct, content, and criterion validity. The authors anticipate that expanding this metric globally will improve its validity while facilitating advocacy and allowing for impact assessment at the system level.

GRACE Satellite Observations of Antarctic Bottom Water Transport Variability

AbstractAntarctic Bottom Water (AABW) formation and transport constitute a key component of the global ocean circulation. Direct observations suggest that AABW volumes and transport rates may be decreasing, but these observations are too temporally or spatially sparse to determine the cause. To address this problem, we develop a new method to reconstruct AABW transport variability using data from the GRACE (Gravity Recovery and Climate Experiment) satellite mission. We use an ocean general circulation model to investigate the relationship between ocean bottom pressure and AABW: we calculate both of these quantities in the model, and link them using a regularized linear regression. Our reconstruction from modeled ocean bottom pressure can capture 65%–90% of modeled AABW transport variability, depending on the ocean basin. When realistic observational uncertainty values are added to the modeled ocean bottom pressure, the reconstruction can still capture 30%–80% of AABW transport variability. Using the same regression values, the reconstruction skill is within the same range in a second, independent, general circulation model. We conclude that our reconstruction method is not unique to the model in which it was developed and can be applied to GRACE satellite observations of ocean bottom pressure. These advances allow us to create the first global reconstruction of AABW transport variability over the satellite era. Our reconstruction provides information on the interannual variability of AABW transport, but more accurate observations are needed to discern AABW transport trends.

Valuation of Ethanol Grade on the Robustness of Acid Number Determination in Fish Oil

The robustness evaluation of the acid number analysis method in fish oil was carried out to see the effect of the type of ethanol used as a solvent in the titration process. The types of ethanol used in this study were pro analysis grade, pharma and food grade, and technical grade. The acid number values (mgKOH/g) in fish oil with various solvents obtained were 2.3955±0.3211 for pro analysis grade; 2.7932±0.2983 for pharma and food grade; 2.7812±0.3362 for technical grade 1; 2.7031±0.3405 for technical grade 2. The acid number for all types of ethanol has a value that is in accordance with the requirements of SNI 8467: 2018 where the acid number value must be less than 3 mgKOH/g. However, if the measurement uncertainty value is included in the calculation, only the pro analysis grade ethanol solvent has an acid number value that is in accordance with the requirements of SNI. The results of the ANOVA test also showed that there was a significant difference in the variation of ethanol types for determining acid numbers because the calculated F value (13.9004) was greater than the F table (3.0984). Therefore, the solvent that must be used in the method of determining acid numbers in fish oil is pro analysis grade ethanol.

Uncertainty guided test-time training for face forgery detection

The rapid development of generative image modeling poses security risks of spreading unreal visual information, even though those techniques make a lot of applications possible in positive aspects. To provide alerts and maintain a secure social environment, forgery detection has been an urgent and crucial solution to deal with this situation and try to avoid any negative effects, especially for human faces, owing to potential severe results when malicious creators spread disinformation widely. In spite of the success of recent works w.r.t. model design and feature engineering, detecting face forgery from novel image creation methods or data distributions remains unresolved, because well-trained models are typically not robust to the distribution shift during test-time. In this work, we aim to alleviate the sensitivity of an existing face forgery detector to new domains, and then boost real-world detection under unknown test situations. In specific, we leverage test examples, selected by uncertainty values, to fine-tune the model before making a final prediction. Therefore, it leads to a test-time training based approach for face forgery detection, that our framework incorporates an uncertainty-driven test sample selection with self-training to adapt a classifier onto target domains. To demonstrate the effectiveness of our framework and compare with previous methods, we conduct extensive experiments on public datasets, including FaceForensics++, Celeb-DF-v2, ForgeryNet and DFDC. Our results clearly show that the proposed framework successfully improves many state-of-the-art methods in terms of better overall performance as well as stronger robustness to novel data distributions.

The interaction between manufacturer’s showroom deployment and online selling formats

Many manufacturers selling through online retail platforms are deploying physical showrooms to facilitate consumers’ learning about their products. To uncover the effects of the manufacturer-deployed showrooms on participants in a platform-based distribution channel, we establish a game-theoretic model wherein a manufacturer sells through an online retailer under either agency selling or wholesale selling, and consumers decide sequentially on showrooming and purchasing. We find that, firstly, despite the merits of showrooms in resolving consumers’ valuation uncertainty, the deployment of showrooms may unexpectedly engender more product returns (i.e., return exacerbation effect), which hurts the retailer under wholesale selling when the consumers’ travel cost is moderate. Second, the manufacturer under each selling format tends to benefit (suffer) from showrooms in the case of low (high) travel cost; in addition, the decrease in commission rate boosts the gain from showroom deployment, making the manufacturer under agency selling more likely to deploy showrooms, especially when the travel cost is moderate. Third, in anticipation of the manufacturer’s (not) deploying showrooms and the corresponding profit impacts, the channel members may adopt varying preferences for the selling format. Notably, they have aligned preferences for agency selling with medium low commission rate, while neither prefers agency selling with relatively high commission rate and travel cost. This study sheds light on the flourishing practice of manufacturer-deployed showrooms, clarifying their impacts on the platform-based distribution channel.

Green and sensitive method combining ultrasonic solvent extraction, stir bar sorptive extraction and thermal desorption-gas chromatography–tandem mass spectrometry for determination of organotin compounds in sediment

A method integrating ultrasonic solvent extraction (USE) and stir bar sorptive extraction (SBSE) with thermal desorption-gas chromatography–tandem mass spectrometry (TD-GC–MS/MS) was developed and validated for determining critical organotin compounds (OTCs)—tributyltin (TBT) and triphenyltin (TPhT)—in sediment. In preparing a 0.5 g sediment sample, two 1-min cycles of US bath solvent extraction (using equal parts CH3COOH and MeOH), were followed by derivatization with NaBEt4 in the presence of a methanolic-aqueous solution of CH3COONa and SBSE for 1 h, achieving analyte recoveries of 64–94 %. Matrix-matched internal standard calibration and the use of an isotopically labelled recovery standard corrected for analyte losses and matrix effect (ME), ensuring accurate results for triorganotins. However, the method was not suitable for mono- and diorganotins due to low and anomalous recoveries caused by ME. Miniaturizing and optimizing the sample preparation reduced chemicals use and waste, contributing to the method being deemed ’an excellent green analysis’ by the Analytical Eco-Scale assessment. The method achieved a limit of quantification (LOQ) of 0.081 ng g−1 and an expanded uncertainty (U) value of 15 % for TBT, meeting the environmental quality standards (EQS) criteria of the European Union Water Framework Directive. Its applicability was demonstrated through the analysis of actual sediment samples and natural matrix certified reference materials.

A novel method for measuring electrolytic conductivity with a polarization impedance control function

The article presents an idealized model of the differential method utilized in conductometry to measure electrolytic conductivity (EC). It identifies the main drawback of the widely used differential method: the inability to control the influence of polarization impedance on EC measurement results. A criterion for the absence of error caused by polarization effects is proposed, involving equality between the ratios of resistance differences and cell constant differences. A novel method for EC measurement, termed the double differential method, is introduced. This method relies on repeated differential impedance measurements and incorporates an additional third impedance measurement of a virtual liquid column. The new approach enables the identification of polarization impedance influence on EC measurements and the calculation of measurement errors for correction. A 3 times lower type A measurement uncertainty value can be achieved by averaging the corrected results.

The Impact of Corporate Social Responsibility on the Relationship between Economic Policy Uncertainty and Corporate Value：Evidence from Chinese Listed Companies

The Impact of Corporate Social Responsibility on the Relationship between Economic Policy Uncertainty and Corporate Value：Evidence from Chinese Listed Companies

Adaptive Fuzzy Filter-based L 1 Adaptive Controller Design for Electromechanical Actuator Containing Uncertainties and Disturbances

Conventional L 1 adaptive controller utilizes high adaptation gain to quickly adapt time varying uncertainties and disturbances. However, it contaminates the control signal with high frequency components. To eliminate this high frequency signal, it uses a low pass filter with fixed structure. Yet, the main problem with this fixed structured filter is that it may cancel the estimated values of uncertainties and disturbances along with the high frequencies. To overcome this problem, in this research work, an adaptive fuzzy low pass filter is proposed in place of the fixed structured filter of the L 1 adaptive controller. In this proposed research work, the filter changes its structure according to the time varying uncertainties and disturbances. Hence, the contributory part of this work is that the proposed adaptive fuzzy filter efficiently cancels out high frequency components without nullifying the estimated values of uncertainties and disturbances from the control signal. The stability of the proposed adaptive fuzzy filter-based L 1 adaptive controller is guaranteed analytically. The proposed method is employed on an electromechanical actuator, viz., DC motor experimental setup to validate its effectiveness. Adequate cancellation of high frequency signal incurs smooth control signal which in turn provides lower control energy.

Simultaneous quantification of 2-pyrrolidone-5-carboxylic acid (PCA), urocanic acid (UCA), and histidine (His) in the stratum corneum by HPLC-PDA

Located in the epidermis, the stratum corneum is the most superficial layer of the skin, acting as a barrier against aggression from the external environment, preventing dehydration, and maintaining the water balance of the skin. The stratum corneum contains the Natural Hydration Factor (NMF), a mixture of hygroscopic molecules derived from filaggrin. The NMF includes 2-pyrrolidone-5-carboxylic acid (PCA), urocanic acid (UCA), and histidine (His), target biomarkers that were extracted by tape-stripping from the stratum corneum of participants for quantification in HPLC-PDA. By extracting the stratum corneum, we developed a protocol to optimize, through audience definition, the quantification of NMF biomarkers. Chromatographic analysis was performed using a YMC-Triart C18 chromatographic column, with a gradient elution of mobile phase composed of triethylammonium phosphate and acetonitrile mixture, and a photodiode array detector. HPLC-PDA procedure was selective, linear (in the range from 0.2 to 5.0 µg/mL), accurate (recovery from 92.7 to 115.1 %), precise (RSD from 0.3 to 12.1 %), and with proper detection and quantification limits. The measurement uncertainty was evaluated from validation data, with combined standard uncertainty values of 0.025–0.12 µg/mL (2.1–5.6 %), 0.004–0.28 µg/mL (2.4–12.6 %), and 0.016–0.16 µg/mL (3.2–7.9 %) for His, PCA, and UCA, respectively. Statistical analyses were performed using Monte Carlo simulation and the Mann-Whitney test, as our results were not homoscedastic and deviated from normality. The results indicate that the best audience for quantifying biomarkers were participants up to 35 years old, with all phototypes, and, preferably, female.

A Method for Estimating the Resultant Expanded Uncertainty Value Based on Interval Arithmetic

The article describes a method for determining the resultant expanded uncertainty value in the case of analyzing an uncertainty budget with many components. It employs interval arithmetic, known for its low calculation complexity compared to other methods in the literature. The article includes a detailed explanation of the proposed method and showcases its applications. Results are compared with those from the Monte Carlo method and assumptions based on the central limit theorem. The relative error in estimating the resulting expanded uncertainty value typically stays within ±5% of the Monte Carlo method’s result.

Similarity recognition approach to identify zero-added MSG soy sauce using stable isotopes and amino acid profiles

Seasonings such as naturally fermented soy sauce without added monosodium glutamate (MSG), are currently a growth market in China. However, fraudulent and mislabeled zero-added MSG soy sauce may cause a risk of excessive MSG intake, increasing food safety issues for consumers. This study investigates stable carbon and nitrogen isotopes and 16 amino acids in typical Chinese in-market soy sauces and uses a similarity method to establish criteria to authenticate MSG addition claims. Results reveal most zero-added MSG soy sauces had lower δ13C values (−25.2 ‰ to −17.7 ‰) and glutamic acid concentrations (8.97 mg mL−1 to 34.76 mg mL−1), and higher δ15N values (−0.27 ‰ +0.95 ‰) and other amino acid concentrations than added-MSG labeled samples. A combined approach, using isotopes, amino acids, similarity coefficients and uncertainty values, was evaluated to rapidly and accurately identify zero-added MSG soy sauces from MSG containing counterparts.

Distributed state constraint containment control of multi-AUV formation with unknown control direction

The control problem of multi-autonomous underwater vehicle (multi-AUV) formation systems is a hot issue in current research. The research goal is to make the formation have a more comprehensive ability to deal with emergencies under higher control accuracy. In this paper, input saturation, disturbances, and system uncertainties are considered. To achieve the convergence of system containment errors, a state constraint containment control method is proposed for multi-AUV systems with unknown control direction. Combined with unknown control direction, new auxiliary variables are constructed to compensate the input saturation. On this basis, the follower reference trajectories and errors are defined. To constrain some system states, the tan-type barrier Lyapunov function (TBLF) is proposed. The estimated values of system uncertainties and disturbances can be obtained by neural network. Compared to other works, the amount of data generated by neural network is further reduced by the optimized adaptive law in this study. The unknown control direction problem is handled based on Nussbaum function, and its limitation range is expanded. The designed control method ensures the stable operation of formation systems. The containment errors can converge to a small neighborhood of zero. By comparing the numerical simulation results with other work, the advantages of the control algorithm proposed in this paper has been verified.

Valuation of Medical Innovation Handling with Uncertainty and Risk.

Background: The purpose of this paper is to address how to handle uncertainty when performing an economic valuation of a medical innovation R&D project in orphan diseases from the perspective of the investor. Methods: We describe the specific uncertainty related to cash flows and the cost of capital for innovation in orphan diseases. The uncertainty in cash flows relates to sales, manufacturing and R&D costs, and probabilities of failure for each phase in the clinical trial program. We consider different net present values (NPVs) and higher standard deviations for orphan drugs compared to non-orphan drugs. Results: Numerical case base examples showed the differences in trade-off by an investor for R&D projects with differences in NPV and level of uncertainty. The investor will transfer the additional uncertainty in cash flows in a higher cost of capital. An alternative approach is the application of an "acceptability curve" based on a probabilistic sensitivity analysis, which displays the cumulative probabilities at a range of different values for the NPV. Finally, we consider uncertainty in the cost of capital itself by applying the Capital Asset Pricing Model (CAPM). Conclusions: In this paper, we described various types of uncertainty and explored various approaches to how to handle uncertainty in the economic valuation of medical innovation in orphan diseases. The bridging of health economics with economic valuation theory in the healthcare market is to our knowledge a novel approach for the valuation of medical innovation by investors.

Heterogenous Patent Portfolios, Market Uncertainty, and Firm Value

Heterogenous Patent Portfolios, Market Uncertainty, and Firm Value