Indifference Zone Research Articles

Novel machine learning technique further clarifies unrelated donor selection to optimize transplantation outcomes

AbstractWe investigated the impact of donor characteristics on outcomes in allogeneic hematopoietic cell transplantation (HCT) recipients using a novel machine learning approach, the Nonparametric Failure Time Bayesian Additive Regression Trees (NFT BART). NFT BART models were trained on data from 10 016 patients who underwent a first HLA-A, B, C, and DRB1 matched unrelated donor HCT between 2016 and 2019, reported to the Center for International Blood and Marrow Transplant Research, then validated on an independent cohort of 1802 patients. The NFT BART models were adjusted based on recipient, disease, and transplant variables. We defined a clinically meaningful impact on overall survival (OS) or event-free survival (EFS; survival without relapse, graft failure, or moderate to severe chronic graft-versus-host disease) as >1% difference in predicted outcome at 3 years. Characteristics with <1% impact (within a zone of indifference) were not considered to be clinically relevant. Donor cytomegalovirus, parity, HLA-DQB1, and HLA-DPB1 T-cell epitope matching fell within the zone of indifference. The only significant donor factor that associated with OS was age, in which, compared with 18-year-old donors, donors aged ≥31 years old were associated with lower OS. Both donor age (≤32 years) and use of a male donor, regardless of recipient sex, improved EFS. We, therefore, recommend selecting the earliest available donor within the 18 to 30 years age range for HCT to optimize OS. If several donors in the 18 to 30 years age range are available, a male donor may be chosen to optimize EFS.

Gap analysis of strategies for promoting interprofessional teams in healthcare units

PurposeA healthcare unit's effectiveness largely depends on how well its interprofessional teams work together. Unfortunately, the strategies used to improve these teams often lack substance. This study analyzed these strategies and found a performance gap.Design/methodology/approachThis study took a unique mixed-method approach, systematically reviewing both qualitative and quantitative studies that identified strategies to enhance interprofessional teams in healthcare units. To gauge the effectiveness of these strategies, the researcher utilized an Importance-Performance Analysis (IPA) in four specialized clinical training centers in Hamadan province, Iran. The analysis of the IPA involved 35 experts from these centers as the statistical population.FindingsBased on a systematic review, there are seven categories: contextual, strategic, communication, organizational, individual, Human Resources Management (HRM), and environmental for promoting interprofessional teams with a total of 36 sub-indicator. Based on the IPA, the HRM aspect shows the most extensive performance gap. The individual and organizational aspects fall under resource wastage, and the environmental aspect is within the indifferent zone. Also, some critical sub-indicators, such as incentives/rewards, roles and responsibilities, financial resources, team-initiated innovation, the culture of respect, partner resources, humility, data availability, set expectations, and team availability, are in the weak areas.Practical implicationsThis research has identified critical areas for improvement in promoting teamwork in clinical training centers through a comprehensive gap analysis. It also presents practical policy solutions to address these weak points, providing a clear roadmap for enhancing interprofessional teams in healthcare units.Originality/valueImproving teamwork in healthcare can be challenging, but it is possible with proper strategies and tools. One of the highlights of the recent study was the combination of systematic review studies with IPA to identify areas for improving interprofessional teamwork in clinical training centers.

Sequential Monitoring Using the Second Generation P-Value with Type I Error Controlled by Monitoring Frequency

The Second Generation P-Value (SGPV) measures the overlap between an estimated interval and a composite hypothesis of parameter values. We develop a sequential monitoring scheme of the SGPV (SeqSGPV) to connect study design intentions with end-of-study inference anchored on scientific relevance. We build upon Freedman’s “Region of Equivalence” (ROE) in specifying scientifically meaningful hypotheses called Pre-specified Regions Indicating Scientific Merit (PRISM). We compare PRISM monitoring versus monitoring alternative ROE specifications. Error rates are controlled through the PRISM’s indifference zone around the point null and monitoring frequency strategies. Because the former is fixed due to scientific relevance, the latter is a targettable means for designing studies with desirable operating characters. An affirmation step to stopping rules improves frequency properties including the error rate, the risk of reversing conclusions under delayed outcomes, and bias.

A Non-Parametric Sequential Procedure for the Generalized Partition Problem

In selection and ranking, the partitioning of treatments by comparing them to a control treatment is an important statistical problem. For over eighty years, this problem has been investigated by a number of researchers via various statistical designs to specify the partitioning criteria and optimal strategies for data collection. Many researchers have proposed designs in order to generalize formulations known at that time. One such generalization adopted the indifference-zone formulation to designate the region between the boundaries for “good” and “bad” treatments as the indifference zone. Since then, this formulation has been adopted by a number of researchers to study various aspects of the partition problem. In this paper, a non-parametric purely sequential procedure is formulated for the partition problem. The “first-order” asymptotic properties of the proposed non-parametric procedure are derived. The performance of the proposed non-parametric procedure for small and moderate sample sizes is studied via Monte Carlo simulations. An example is provided to illustrate the proposed procedure.

Evaluative Deflation, Social Expectations, and the Zone of Moral Indifference.

Acts that are considered undesirable standardly violate our expectations. In contrast, acts that count as morally desirable can either meet our expectations or exceed them. The zone in which an act can be morally desirable yet not exceed our expectations is what we call the zone of moral indifference, and it has so far been neglected. In this paper, we show that people can use positive terms in a deflated manner to refer to actions in the zone of moral indifference, whereas negative terms cannot be so interpreted.

Classification Under Partial Reject Options

In many applications there is ambiguity about which (if any) of a finite number N of hypotheses that best fits an observation. It is of interest then to possibly output a whole set of categories, that is, a scenario where the size of the classified set of categories ranges from 0 to N. Empty sets correspond to an outlier, sets of size 1 represent a firm decision that singles out one hypothesis, sets of size N correspond to a rejection to classify, whereas sets of sizes 2,…,N-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2,\\ldots ,N-1$$\\end{document} represent a partial rejection to classify, where some hypotheses are excluded from further analysis. In this paper, we review and unify several proposed methods of Bayesian set-valued classification, where the objective is to find the optimal Bayesian classifier that maximizes the expected reward. We study a large class of reward functions with rewards for sets that include the true category, whereas additive or multiplicative penalties are incurred for sets depending on their size. For models with one homogeneous block of hypotheses, we provide general expressions for the accompanying Bayesian classifier, several of which extend previous results in the literature. Then, we derive novel results for the more general setting when hypotheses are partitioned into blocks, where ambiguity within and between blocks are of different severity. We also discuss how well-known methods of classification, such as conformal prediction, indifference zones, and hierarchical classification, fit into our framework. Finally, set-valued classification is illustrated using an ornithological data set, with taxa partitioned into blocks and parameters estimated using MCMC. The associated reward function’s tuning parameters are chosen through cross-validation.

Models of body weight and fatness regulation.

Body weight and fatness appear to be regulated phenomena. Several different theoretical models are available to capture the essence of this idea. These include the set-point, dynamic equilibrium, adiposity force, control theory-settling point, Hall-Guo, operation point and dual intervention point (DIP) models. The set-point model posits a single reference point around which levels of fat are regulated. The dynamic equilibrium model suggests that the apparent regulation of body fat around a reference point is an illusion owing to the necessary impacts of weight change on energy expenditure. Control theory focuses on the importance of feedback gain and suggests set-point and dynamic equilibrium are ends of a continuum of feedback gain. Control theory models have also been called 'settling point' models. The Hall-Guo, operation point and DIP models also bring together the set-point and dynamic equilibrium ideas into a single framework. The DIP proposes a zone of indifference where dynamic equilibrium 'regulation' predominates, bounded by upper and lower intervention points beyond which physiological mechanisms are activated. The drifty gene hypothesis is an idea explaining where this individual variation in the upper intervention point might come from. We conclude that further experiments to test between the models are sorely required. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

Морфологические изменения роговицы у мелких домашних животных под влиянием кросслинкинга

The article presents morphological changes of the cornea in small domestic animals during crosslinking. The research was carried out on the basis of the Department of Anatomy and Histology of Animals named after Professor A. F. Klimov, as well as the Center for Veterinary Ophthalmology of Dr. Shilkin A.G. Morphological studies were performed in the Department of Pathomorphology of the FSAU «MNTC «Eye Microsurgery» named after Academician S. N. Fedorov». The objects of the study were selected cats (n=10) aged 4 to 6 years and dogs (n=10) aged 4 to 6 years of the Yorkshire Terrier breed (n=5) and Chihuahua (n=5), admitted to the «Center of Veterinary Ophthalmology of Dr. A. Shilkin.G.», euthanized as a result of received polytrauma incompatible with life. Analysis of the data obtained allowed us to conclude that corneal collagen crosslinking affects changes in the morpho-functional features of the corneal stroma. The data on changes in the anterior layers of the stroma, which are expressed by a compact packing of fibers without signs of edema, as well as a demarcation line separating the impact area from the indifferent zone, are scientifically substantiated. The results obtained are basic in the development of new methods of therapeutic strategy and tactics in the treat-ment of animals with keratopathies of various genesis.

Data-Driven Ranking and Selection Under Input Uncertainty

In many applications, input data are collected frequently to update the simulation model of the system, whereas simulation is run to compare different designs/strategies to identify the best one with a high confidence. In “Data-Driven Ranking and Selection Under Input Uncertainty,” Wu, Wang, and Zhou consider such a simulation-based ranking and selection (R&amp;S) problem, in which the input distribution is estimated and updated with input data arriving in batches over time. Unlike most existing works of R&amp;S that conduct simulation under a fixed distribution, in this data-driven setting, simulation outputs are generated under different input distributions over time. A moving average estimator is introduced to aggregate simulation outputs generated under heterogenous distributions. Then, two sequential elimination procedures are devised by establishing exact and asymptotic confidence bands for the estimator. The efficiency of the procedures can be further boosted by incorporating the “indifference zone” idea and optimizing the “drop rate” parameter of the moving average estimator.

Study of the Interactive Effect between Fosfomycin and Tylosin for Use in the Therapy and Prevention of Disease in Veterinary Medicine

The pharmacological interaction of the combination of Fosfomycin and tylosin was studied. An in vitro challenge method was used for this purpose. The results were used to calculate their interaction using the graphical representation known as ISOBOLOGRAM. The agar well bioassay method was used. The following criteria were used to quantify the results: A synergistic effect was defined when the actual/theoretical effect (SMEF) was greater than 1. The additive effect was defined when the SMEF was equal to 1. The area between the antagonism and additive effect was defined as the zone of indifference. When studying the interaction between Fosfomycin and Tylosin, it was observed that the best SMF of this antibiotic combination was 2 : 3. This result allows postulating that the use of this combination of Fosfomycin and Tylosin establishes a well recognised and documented synergistic effect between them and can be recommended in pigs and poultry for the treatment of infections by gram + and gram - germs and other infectious agents sensitive to this combination.

Study of the Interactive Effect between Gentamicin and Sulfa-Trimethoprim for Use in the Therapy and Prevention of Disease in Veterinary Medicine

The pharmacological interaction of the combination of Gentamicin sulphate and sulfamethoxazole-Trimethoprim was studied. An in vitro challenge method was used for this purpose. The results were used to calculate their interaction using the graphical representation known as ISOBOLOGRAM. The pure salts of Gentamicin sulphate and Sulfamethoxazole-Trimethoprim were used and were obtained from the manufacturer DIPROFA'Q Productos Farmaceuticos Veterinarios, Mexico. The agar bioassay method was used. The following criteria were used to quantify the results: A synergistic effect was defined when the actual/theoretical effect (SMEF) was greater than 1. The additive effect was defined when the SMEF was equal to 1. The area between the antagonism and additive effect was defined as the zone of indifference. When studying the interaction between Gentamicin and Sulfamethoxazole-Trimetroprim it was observed that the best SMF of this antibiotic combination was 2 : 3,. This result allows postulating that the use of this combination of Gentamicin and Sulfamethoxazole-Trimethoprim establishes a well recognised and documented synergistic effect between them and can be recommended in Canids and Felids for the treatment of enteric infections by gram + and gram- germs and other infectious agents sensitive to this combination.

Adaptive fully sequential selection procedures with linear and nonlinear control variates

A decision-making process often involves selecting the best solution from a finite set of possible alternatives regarding some performance measure, which is known as Ranking-and-Selection (R&S) when the performance is not explicitly available and can only be estimated by taking samples. Many R&S procedures have been proposed considering different problem formulations. In this article, we adopt the classic fully sequential Indifference-Zone (IZ) formulation developed in the statistical literature, and take advantage of the control variates, a well-known variance reduction technique in the simulation literature, to investigate the potential benefits as well as the statistical guarantee by designing a new type of R&S procedure in an adaptive fashion. In particular, we propose a generic adaptive fully sequential procedure that can employ both linear and nonlinear control variates, in which both the control coefficient and sample variance can be sequentially updated as the sampling process progresses. We demonstrate that the proposed procedures provide the desired probability of correct selection in the asymptotic regime as the IZ parameter goes to zero. We then compare the proposed procedures with various existing procedures through the simulation experiments on practical illustrative examples, in which we observe several interesting findings and demonstrate the advantage of our proposed procedures.

Interactive Effect between Florfenicol and Tylosin Tartrate for Use in the Therapy and Prevention of Diseases in Veterinary Medicine

An in vitro test on the pharmacological interaction of the antibiotic combination between florfenicol and tylosin tartrate was performed. An in vitro challenge method was used for this purpose. The results were used to calculate their interaction using the graphical representation known as ISOBOLOGRAM. The pure salts of florfenicol and tylosin tartrate were used. The agar bioassay method was used. The following criteria were used to quantify the results: A synergistic effect was defined when the actual/theoretical effect (SMEF) was greater than 1. The additive effect was defined when the SMEF was equal to 1. The area between antagonism and additive effect was defined as the zone of indifference. When studying the interaction between florfenicol and tylosin tartrate it was observed that the best SMF of this antibiotic combination was 1.45 when the concentration ratio between the antibiotics was 1 : 4 (florfenicol tylosin tartrate respectively). Consequently, it can be postulated that the combination of florfenicol with tylosin tartrate showed a synergistic effect and can be recommended in poultry and swine for the treatment of mycoplasma infections, most gram + and gram- germs and other infectious agents sensitive to the formulation.

The Interactive Effect between Kitasamycin Tartrate and Doxycycline Hydrate for Use in the Therapy and Prevention of Diseases in Veterinary Medicine

An in vitro test on the pharmacological interaction of the antibiotic combination between kitasamycin tartrate and Doxycycline hydrate was carried out. An in vitro challenge method was used for this purpose. The results were used to calculate their interaction using the graphical representation known as ISOBOLOGRAM. The pure salts of Kitasamycin tartrap and Doxycycline Hydrate were used and were obtained from the manufacturer of the product, Lapi - Doxi. The agar bioassay method was used. The following criteria were used to quantify the results: A synergistic effect was defined when the actual/theoretical effect (SMEF) was greater than 1. The additive effect was defined when the SMEF was equal to 1. The area between the antagonism and additive effect was defined as the zone of indifference. When studying the interaction between florfenicol and tylosin tartrate it was observed that the best SMF of this antibiotic combination was 1.45 when the concentration ratio between the antibiotics was 1 : 4 (Kitasamycin tartrate and doxycycline hydrate respectively). Consequently it can be postulated that the combination of kitasamycin tartrate with doxycycline hydrate showed a synergistic effect and can be recommended in poultry and swine for the treatment of mycoplasma infections, most gram + and gram- germs and other infectious agents sensitive to the formulation.

Study of the Interactive Effect between Tylosin Tartrate and Colistin for its Use in the Therapy and Prevention of Poultry Diseases in Veterinary Medicine

The pharmacological interaction of antibiotic combinations of tylosin and colistin was studied. For this purpose, an in vitro challenge method was used. The results were used to calculate their interaction using the graphic representation known as ISOBOLOGRAM. The pure salts of tylosin tartrate and colistin sulfate were obtained from the manufacturer of the product, Laboratorios Ecozoo SA de CV Mexico. The bioassay method was used in agar wells and to quantify the results, the following criteria were used: A synergistic effect was defined when the real/theoretical effect (SMF) was greater than 1. The additive effect was defined when the SMF was equal to 1. The area between the antagonism and additive effect was defined as the zone of indifference. When studying the interaction between tylosin and colistin it was observed that the best SMF of this antibiotic combination was 3 to 2 and 2 : 4 (tylosin colistin respectively). It was cocluded that the combination of tylosin with colistin exerts a synergistic effect and can be recommended for oral administration in poultry for the treatment of infections by disease producing germs sensitive to the formula.

Bayesian-based indifference-zone multi-objective ranking and selection procedures

Three indifference-zone (IZ) multi-objective ranking and selection (MORS) procedures are presented in this paper. The procedures define the minimum number of simulation replications required for each alternative system which will guarantee that the probability of correct selection exceeds a desired minimum. The statistical validity of the procedures are shown by using a Bayesian inference model, which contributes to the efficiency of the procedures. The proposed procedures were compared to multi-objective budget allocation procedures and performed well in numerical experiments. The concept of relaxed Pareto optimality is also proposed, which incorporates the indifference-zone concept into the multi-objective optimization domain.

Unbiased decision making in location-routing problems with uncertain customer demands

In this paper, we focus on a location-routing problem (LRP) in the dairy industry. This problem consists of locating a cold storage warehouse, from which vehicles of limited capacity are dispatched to serve a given number of supermarkets with uncertain service requirements, and determining the order of supermarkets served by each vehicle. First, the LRP is solved by using a classical approach based on a deterministic model where the service requirements, i.e. customer demands, are defined through sample means. Second, we propose an indifference zone approach to the LRP. The indifference zone procedures are specific ranking and selection methods aimed at selecting the best option from a set of alternative configurations. In particular, they attempt to guarantee the probability of correct choice, while minimising the computational effort. The numerical results presented in the paper highlight the risk of biased decision making when mere sample means are used in a deterministic model. In addition, they show the effectiveness of indifference zone approaches to the dairy products distribution activity.

Evaluative Deflation, Social Expectations and the Zone of Moral Indifference

Evaluative Deflation, Social Expectations and the Zone of Moral Indifference

Fixed-Confidence, Fixed-Tolerance Guarantees for Ranking-and-Selection Procedures

Ever since the conception of the statistical ranking-and-selection (R8S) problem, a predominant approach has been the indifference-zone (IZ) formulation. Under the IZ formulation, R8S procedures are designed to provide a guarantee on the probability of correct selection (PCS) whenever the performance of the best system exceeds that of the second-best system by a specified amount. We discuss the shortcomings of this guarantee and argue that providing a guarantee on the probability of good selection (PGS)—selecting a system whose performance is within a specified tolerance of the best—is a more justifiable goal. Unfortunately, this form of fixed-confidence, fixed-tolerance guarantee has received far less attention within the simulation community. We present an overview of the PGS guarantee with the aim of reorienting the simulation community toward this goal. We examine numerous techniques for proving the PGS guarantee, including sufficient conditions under which selection and subset-selection procedures that deliver the IZ-inspired PCS guarantee also deliver the PGS guarantee.

Selecting the Best Alternative Based on Its Quantile

A value-at-risk, or quantile, is widely used as an appropriate investment selection measure for risk-conscious decision makers. We present two quantile-based sequential procedures—with and without consideration of equivalency between alternatives—for selecting the best alternative from a set of simulated alternatives. These procedures asymptotically guarantee a user-defined target probability of correct selection within a prespecified indifference zone. Experimental results demonstrate the trade-off between the indifference-zone size and the number of simulation iterations needed to render a correct selection while satisfying a desired probability of correct selection.