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Опубліковано: 14 березня 2023

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Статті в журналах з теми "Data Subgroup"

1

Kavšek, Branko, and Nada Lavrač. "Using subgroup discovery to analyze the UK traffic data." Advances in Methodology and Statistics 1, no. 1 (January 1, 2004): 249–64. http://dx.doi.org/10.51936/zewh2294.

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Rule learning is typically used in solving classification and prediction tasks. However, learning of classification rules can be adapted also to subgroup discovery. Such an adaptation has already been done for the CN2 rule learning algorithm. In previous work this new algorithm, called CN2-SD, has been described in detail and applied to the well known UCI data sets. This paper summarizes the modifications needed for the adaptation of the CN2 rule learner to subgroup discovery and presents its application to a real-life data set - the UK traffic data - confirming its appropriateness for subgroup discovery in real-life applications through experimental comparison with the CN2 rule learning algorithm as well as through the evaluation of an expert. Furthermore we make the first step towards the comparison of the new CN2-SD algorithm to another state-of-the-art subgroup discovery algorithm SubgroupMiner by applying both algorithms to a slightly different data set - the UK traffic challenge data set. The results of this application are presented in the form of ROC curves, showing CN2-SD’s potential in finding descriptions (subgroups) for minority classes, while SubgroupMiner found ‘better’ subgroups when trying to describe the majority class given the problem at hand.
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2

Huang, Xifen, Chaosong Xiong, Jinfeng Xu, Jianhua Shi, and Jinhong Huang. "Mixture Modeling of Time-to-Event Data in the Proportional Odds Model." Mathematics 10, no. 18 (September 16, 2022): 3375. http://dx.doi.org/10.3390/math10183375.

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Subgroup analysis with survival data are most essential for detailed assessment of the risks of medical products in heterogeneous population subgroups. In this paper, we developed a semiparametric mixture modeling strategy in the proportional odds model for simultaneous subgroup identification and regression analysis of survival data that flexibly allows the covariate effects to differ among several subgroups. Neither the membership or the subgroup-specific covariate effects are known a priori. The nonparametric maximum likelihood method together with a pair of MM algorithms with monotone ascent property are proposed to carry out the estimation procedures. Then, we conducted two series of simulation studies to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of German breast cancer data is further provided for illustrating the proposed methodology.
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3

Mukherjee, Shubhabrata, Jesse Mez, Emily H. Trittschuh, Andrew J. Saykin, Laura E. Gibbons, David W. Fardo, Madeline Wessels, et al. "Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups." Molecular Psychiatry 25, no. 11 (December 4, 2018): 2942–51. http://dx.doi.org/10.1038/s41380-018-0298-8.

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Abstract Categorizing people with late-onset Alzheimer’s disease into biologically coherent subgroups is important for personalized medicine. We evaluated data from five studies (total n = 4050, of whom 2431 had genome-wide single-nucleotide polymorphism (SNP) data). We assigned people to cognitively defined subgroups on the basis of relative performance in memory, executive functioning, visuospatial functioning, and language at the time of Alzheimer’s disease diagnosis. We compared genotype frequencies for each subgroup to those from cognitively normal elderly controls. We focused on APOE and on SNPs with p < 10−5 and odds ratios more extreme than those previously reported for Alzheimer’s disease (<0.77 or >1.30). There was substantial variation across studies in the proportions of people in each subgroup. In each study, higher proportions of people with isolated substantial relative memory impairment had ≥1 APOE ε4 allele than any other subgroup (overall p = 1.5 × 10−27). Across subgroups, there were 33 novel suggestive loci across the genome with p < 10−5 and an extreme OR compared to controls, of which none had statistical evidence of heterogeneity and 30 had ORs in the same direction across all datasets. These data support the biological coherence of cognitively defined subgroups and nominate novel genetic loci.
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4

Lötsch, Jörn, and Alfred Ultsch. "Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data." International Journal of Molecular Sciences 21, no. 1 (December 20, 2019): 79. http://dx.doi.org/10.3390/ijms21010079.

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Advances in flow cytometry enable the acquisition of large and high-dimensional data sets per patient. Novel computational techniques allow the visualization of structures in these data and, finally, the identification of relevant subgroups. Correct data visualizations and projections from the high-dimensional space to the visualization plane require the correct representation of the structures in the data. This work shows that frequently used techniques are unreliable in this respect. One of the most important methods for data projection in this area is the t-distributed stochastic neighbor embedding (t-SNE). We analyzed its performance on artificial and real biomedical data sets. t-SNE introduced a cluster structure for homogeneously distributed data that did not contain any subgroup structure. In other data sets, t-SNE occasionally suggested the wrong number of subgroups or projected data points belonging to different subgroups, as if belonging to the same subgroup. As an alternative approach, emergent self-organizing maps (ESOM) were used in combination with U-matrix methods. This approach allowed the correct identification of homogeneous data while in sets containing distance or density-based subgroups structures; the number of subgroups and data point assignments were correctly displayed. The results highlight possible pitfalls in the use of a currently widely applied algorithmic technique for the detection of subgroups in high dimensional cytometric data and suggest a robust alternative.
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5

Emery, William. "WOCE/TOGA Historical Oceanographic Data Subgroup." Eos, Transactions American Geophysical Union 67, no. 22 (1986): 500. http://dx.doi.org/10.1029/eo067i022p00500-03.

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6

Pan, Yunzhi, Weidan Pu, Xudong Chen, Xiaojun Huang, Yan Cai, Haojuan Tao, Zhiming Xue, et al. "Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data." Schizophrenia Bulletin 46, no. 3 (January 4, 2020): 623–32. http://dx.doi.org/10.1093/schbul/sbz112.

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Abstract The diagnosis of schizophrenia is thought to embrace several distinct subgroups. The manifold entities in a single clinical patient group increase the variance of biological measures, deflate the group-level estimates of causal factors, and mask the presence of treatment effects. However, reliable neurobiological boundaries to differentiate these subgroups remain elusive. Since cortical thinning is a well-established feature in schizophrenia, we investigated if individuals (patients and healthy controls) with similar patterns of regional cortical thickness form naturally occurring morphological subtypes. K-means algorithm clustering was applied to regional cortical thickness values obtained from 256 structural MRI scans (179 patients with schizophrenia and 77 healthy controls [HCs]). GAP statistics revealed three clusters with distinct regional thickness patterns. The specific patterns of cortical thinning, clinical characteristics, and cognitive function of each clustered subgroup were assessed. The three clusters based on thickness patterns comprised of a morphologically impoverished subgroup (25% patients, 1% HCs), an intermediate subgroup (47% patients, 46% HCs), and an intact subgroup (28% patients, 53% HCs). The differences of clinical features among three clusters pertained to age-of-onset, N-back performance, duration exposure to treatment, total burden of positive symptoms, and severity of delusions. Particularly, the morphologically impoverished group had deficits in N-back performance and less severe positive symptom burden. The data-driven neuroimaging approach illustrates the occurrence of morphologically separable subgroups in schizophrenia, with distinct clinical characteristics. We infer that the anatomical heterogeneity of schizophrenia arises from both pathological deviance and physiological variance. We advocate using MRI-guided stratification for clinical trials as well as case–control investigations in schizophrenia.
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7

Shirrell, Matthew. "The Effects of Subgroup-Specific Accountability on Teacher Turnover and Attrition." Education Finance and Policy 13, no. 3 (July 2018): 333–68. http://dx.doi.org/10.1162/edfp_a_00227.

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The No Child Left Behind Act of 2001 required states to set cutoffs to determine which schools were subject to accountability for their racial/ethnic subgroups. Using a regression discontinuity design and data from North Carolina, this study examines the effects of this policy on teacher turnover and attrition. Subgroup-specific accountability had no overall effects on teacher turnover or attrition, but the policy caused black teachers who taught in schools that were held accountable for the black student subgroup to leave teaching at significantly lower rates, compared with black teachers who taught in schools not accountable for the black subgroup's performance. The policy also caused shifts in the students assigned to black teachers, with schools that were held accountable for the black subgroup less likely to assign black students to black teachers the following year. These findings demonstrate that subgroup-focused policies—particularly those that use cutoffs to determine subgroup accountability—can shape the composition of the teacher labor force in unintended ways, and have implications for the design of future accountability systems that aim to close racial/ethnic gaps in achievement.
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8

Koopman, Laura, Geert J. M. G. van der Heijden, Arno W. Hoes, Diederick E. Grobbee, and Maroeska M. Rovers. "Empirical comparison of subgroup effects in conventional and individual patient data meta-analyses." International Journal of Technology Assessment in Health Care 24, no. 03 (July 2008): 358–61. http://dx.doi.org/10.1017/s0266462308080471.

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Objectives:Individual patient data (IPD) meta-analyses have been proposed as a major improvement in meta-analytic methods to study subgroup effects. Subgroup effects of conventional and IPD meta-analyses using identical data have not been compared. Our objective is to compare such subgroup effects using the data of six trials (n= 1,643) on the effectiveness of antibiotics in children with acute otitis media (AOM).Methods:Effects (relative risks, risk differences [RD], and their confidence intervals [CI]) of antibiotics in subgroups of children with AOM resulting from (i) conventional meta-analysis using summary statistics derived from published data (CMA), (ii) two-stage approach to IPD meta-analysis using summary statistics derived from IPD (IPDMA-2), and (iii) one-stage approach to IPD meta-analysis where IPD is pooled into a single data set (IPDMA-1) were compared.Results:In the conventional meta-analysis, only two of the six studies were included, because only these reported on relevant subgroup effects. The conventional meta-analysis showed larger (age &lt; 2 years) or smaller (age ≥ 2 years) subgroup effects and wider CIs than both IPD meta-analyses (age &lt; 2 years: RDCMA-21 percent, RDIPDMA-1-16 percent, RDIPDMA-2-15 percent; age ≥2 years: RDCMA-5 percent, RDIPDMA-1-11 percent, RDIPDMA-2-11 percent). The most important reason for these discrepant results is that the two studies included in the conventional meta-analysis reported outcomes that were different both from each other and from the IPD meta-analyses.Conclusions:This empirical example shows that conventional meta-analyses do not allow proper subgroup analyses, whereas IPD meta-analyses produce more accurate subgroup effects. We also found no differences between the one- and two-stage meta-analytic approaches.
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9

Nanlin Jin, Peter Flach, Tom Wilcox, Royston Sellman, Joshua Thumim, and Arno Knobbe. "Subgroup Discovery in Smart Electricity Meter Data." IEEE Transactions on Industrial Informatics 10, no. 2 (May 2014): 1327–36. http://dx.doi.org/10.1109/tii.2014.2311968.

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10

Tsai, Kao-Tai, and Karl Peace. "Analysis of Subgroup Data of Clinical Trials." Journal of Causal Inference 1, no. 2 (September 10, 2013): 193–207. http://dx.doi.org/10.1515/jci-2012-0008.

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AbstractLarge randomized controlled clinical trials are the gold standard to evaluate and compare the effects of treatments. It is common practice for investigators to explore and even attempt to compare treatments, beyond the first round of primary analyses, for various subsets of the study populations based on scientific or clinical interests to take advantage of the potentially rich information contained in the clinical database. Although subjects are randomized to treatment groups in clinical trials, this does not imply the same degree of randomization among sub-populations of the original trials. Therefore, comparisons of treatments in sub-populations may not produce fair and unbiased results without properly addressing this issue. Covariate adjustments in regression analysis and propensity score matching are commonly used to address the non-randomized nature of the sub-populations issue with various degrees of success. However, further improvements to these methods are still possible. In this article, we propose an analysis strategy that shows improvement to conventional methods. Treatment effects and their differences are estimated after adjustment for background imbalances. Treatment groups are then compared using confidence intervals whose limits are determined using the Robbins–Monro stochastic approximation. Data from a recent clinical trial are used to illustrate the methodology.
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Дисертації з теми "Data Subgroup"

1

Atzmüller, Martin. "Knowledge-intensive subgroup mining : techniques for automatic and interactive discovery /." Berlin : Aka, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=2928288&prov=M&dok_var=1&dok_ext=htm.

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2

Atzmüller, Martin. "Knowledge-intensive subgroup mining techniques for automatic and interactive discovery." Berlin Aka, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2928288&prov=M&dok_var=1&dok_ext=htm.

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3

Belfodil, Aimene. "An order theoretic point-of-view on subgroup discovery." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI078.

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Comme le titre pourrait le suggérer, l’objectif principal de cette thèse est de fournir une meilleure compréhension de la tâche de la découverte de sous-groupes à travers la théorie de l’ordre. La découverte de sous-groupes (Subgroup Discovery - SD) est la tâche automatique dont le but est la découverte d’hypothèses intéressantes dans les bases de données. Autrement dit, étant donnée une base de donnée, l’espace de recherche de toutes les hypothèses que l’analyste voudra tester ainsi qu’un moyen formel pour évaluer la qualité de ces hypothèses ; la tâche automatique de la découverte de sous-groupe s’efforce de trouver les meilleurs hypothèses quant à ces trois paramètres. Afin d’élaborer des algorithmes efficaces et efficients pour cette tâche, il est important de comprendre les propriétés des espaces de recherche d’une part et les propriétés de la mesure de qualité d’autre part. Dans cette thèse, nous étendons l’état de l’art par: (i) fournir une vue unifiée sur les espaces d’hypothèses derrière la tâche de découverte de sous-groupes en utilisant la théorie de l’ordre, (ii) proposer l’espace d’hypothèses de conjonctions d’inégalités linéaires dans les bases de données numériques ainsi que différents algorithmes permettant de les énumérer et (iii) proposer un algorithme anytime - fournit progressivement des résultats - pour la tâche particulière de fouille de sous-groupe discriminants dans les bases de données numériques. Ce dernier fournit des garanties sur la qualité des sous-groupes extraits même si l’algorithme est interrompu
As the title of this dissertation may suggest, the aim of this thesis is to provide an order-theoretic point of view on the task of subgroup discovery. Subgroup discovery is the automatic task of discovering interesting hypotheses in databases. That is, given a database, the hypothesis space the analyst wants to explore and a formal way of how the analyst gauges the quality of the hypotheses (e.g. a quality measure); the automated task of subgroup discovery aims to extract the interesting hypothesis w.r.t. these parameters. In order to elaborate fast and efficient algorithms for subgroup discovery, one should understand the underlying properties of the hypothesis space on the one hand and the properties of its quality measure on the other. In this thesis, we extend the state-of-the-art by: (i) providing a unified view of the hypotheses space behind subgroup discovery using the well-founded mathematical tool of order theory, (ii) proposing the new hypothesis space of conjunction of linear inequalities in numerical databases and the algorithms enumerating its elements and (iii) proposing an anytime algorithm for discriminative subgroup discovery on numerical datasets providing guarantees upon interruption
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4

Mistry, Dipesh. "Recursive partitioning based approaches for low back pain subgroup identification in individual patient data meta-analyses." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/64032/.

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This thesis presents two novel approaches for performing subgroup analyses or identifying subgroups in an individual patient data (IPD) meta-analyses setting. The work contained in this thesis originated from an important research priority in the area of low back pain (LBP); identifying subgroups that most (or least) benefit from treatment. Typically, a subgroup is evaluated by applying a statistical test for interaction between a baseline characteristic and treatment. A systematic review found that subgroup analyses in the area of LBP are severely underpowered and are of a rather poor quality (Chapter 4). IPD meta-analyses provide an ideal framework with improved statistical power to investigate and identify subgroups. However, conventional approaches to subgroup analyses applied in both a single trial setting and an IPD setting have a number of issues, one of them being that subgroups are typically investigated one at a time. As individuals have multiple characteristics that may be related to response to treatment, alternative statistical methods are required to overcome the associated issues. Tree based methods are a promising alternative that systematically search the entire covariate space to identify subgroups defined by multiple characteristics. In this work, a number of relevant tree methods, namely the Interaction Tree (IT), Simultaneous Threshold Interaction Modelling Algorithm (STIMA) and Subpopulation Identification based on a Differential Effect Search (SIDES), were identified and evaluated in a single trial setting in a simulation study. The most promising methods (IT and SIDES) were extended for application in an IPD meta-analyses setting by incorporating fixed-effect and mixed-effect models to account for the within trial clustering in the hierarchical data structure, and again assessed in a simulation study. Thus, this work proposes two statistical approaches to subgroup analyses or subgroup identification in an IPD meta-analysis framework. Though the application is based in a LBP setting, the extensions are applicable in any research discipline where subgroup analyses in an IPD meta-analysis setting is of interest.
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Doubleday, Kevin. "Generation of Individualized Treatment Decision Tree Algorithm with Application to Randomized Control Trials and Electronic Medical Record Data." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613559.

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With new treatments and novel technology available, personalized medicine has become a key topic in the new era of healthcare. Traditional statistical methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials (RCTs). With restricted inclusion and exclusion criteria, data from RCTs may not reflect real world treatment effectiveness. However, electronic medical records (EMR) offers an alternative venue. In this paper, we propose a general framework to identify individualized treatment rule (ITR), which connects the subgroup identification methods and ITR. It is applicable to both RCT and EMR data. Given the large scale of EMR datasets, we develop a recursive partitioning algorithm to solve the problem (ITR-Tree). A variable importance measure is also developed for personalized medicine using random forest. We demonstrate our method through simulations, and apply ITR-Tree to datasets from diabetes studies using both RCT and EMR data. Software package is available at https://github.com/jinjinzhou/ITR.Tree.
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6

Mueller, Marianne Larissa [Verfasser], Stefan [Akademischer Betreuer] Kramer, and Frank [Akademischer Betreuer] Puppe. "Data Mining Methods for Medical Diagnosis : Test Selection, Subgroup Discovery, and Contrained Clustering / Marianne Larissa Mueller. Gutachter: Stefan Kramer ; Frank Puppe. Betreuer: Stefan Kramer." München : Universitätsbibliothek der TU München, 2012. http://d-nb.info/1024964264/34.

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7

Li, Rui [Verfasser], Burkhard [Akademischer Betreuer] [Gutachter] Rost, and Stefan [Gutachter] Kramer. "Data Mining and Machine Learning Methods for High-dimensional Patient Data in Dementia Research: Voxel Features Mining, Subgroup Discovery and Multi-view Learning / Rui Li ; Gutachter: Burkhard Rost, Stefan Kramer ; Betreuer: Burkhard Rost." München : Universitätsbibliothek der TU München, 2017. http://d-nb.info/1125018224/34.

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8

Domingue, Jean-Laurent. "Nurses’ Knowledge, Attitudes and Documentation Practices in a Context of HIV Criminalization: A Secondary Subgroup Analysis of Data from California, Florida, New York, and Texas Nurses." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35570.

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Under international legal norms, HIV criminalization is considered to be an overly broad use of criminal law. In the United States, at least 33 states have HIV-specific criminal laws. Data from California, Florida, New York, and Texas nurses provided exemplars from different HIV-related criminal law approaches and the impact of those laws on nurses’ practices. Nurses who cared for patients who expressed fears or concerns about HIV criminalization or patients who had been arrested for HIV-related crimes were more likely to correctly identify the presence or absence of HIV-specific laws in the states where they practised, when compared to nurses who did not care for such patients. Lack of knowledge about HIV-related criminal laws may erode the nurse-patient relationship. Jurisdiction specific education should be created and offered to nurses in order to address this knowledge gap and protect the dignity of people living with HIV.
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9

Belfodil, Adnene. "Exceptional model mining for behavioral data analysis." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI086.

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Avec la prolifération rapide des plateformes de données qui récoltent des données relatives à plusieurs domaines tels que les données de gouvernements, d’éducation, d’environnement ou les données de notations de produits, plus de données sont disponibles en ligne. Ceci représente une opportunité sans égal pour étudier le comportement des individus et les interactions entre eux. Sur le plan politique, le fait de pouvoir interroger des ensembles de données de votes peut fournir des informations intéressantes pour les journalistes et les analystes politiques. En particulier, ce type de données peut être exploité pour l’investigation des sujet exceptionnellement conflictuels ou consensuels. Considérons des données décrivant les sessions de votes dans le parlement Européen (PE). Un tel ensemble de données enregistre les votes de chaque député (MPE) dans l’hémicycle en plus des informations relatives aux parlementaires (e.g., genre, parti national, parti européen) et des sessions (e.g., sujet, date). Ces données offrent la possibilité d’étudier les accords et désaccords de sous-groupes cohérents, en particulier pour mettre en évidence des comportements inattendus. Par exemple, il est attendu que sur la majorité des sessions, les députés votent selon la ligne politique de leurs partis politiques respectifs. Cependant, lorsque les sujets sont plutôt d’intérêt d’un pays particulier dans l’Europe, des coalitions peuvent se former ou se dissoudre. À titre d’exemple, quand une procédure législative concernant la pêche est proposée devant les MPE dans l’hémicycle, les MPE des nations insulaires du Royaume-Uni peuvent voter en accord sans être influencés par la différence entre les lignes politiques de leurs alliances respectives, cela peut suggérer un accord exceptionnel comparé à la polarisation observée habituellement. Dans cette thèse, nous nous intéressons à ce type de motifs décrivant des (dés)accords exceptionnels, pas uniquement sur les données de votes mais également sur des données similaires appelées données comportementales. Nous élaborons deux méthodes complémentaires appelées Debunk et Deviant. La première permet la découverte de (dés)accords exceptionnels entre groupes tandis que la seconde permet de mettre en évidence les comportements exceptionnels qui peuvent au sein d’un même groupe. Idéalement, ces deux méthodes ont pour objective de donner un aperçu complet et concis des comportements exceptionnels dans les données comportementales. Dans l’esprit d’évaluer la capacité des deux méthodes à réaliser cet objectif, nous évaluons les performances quantitatives et qualitatives sur plusieurs jeux de données réelles. De plus, nous motivons l’utilisation des méthodes proposées dans le contexte du journalisme computationnel
With the rapid proliferation of data platforms collecting and curating data related to various domains such as governments data, education data, environment data or product ratings, more and more data are available online. This offers an unparalleled opportunity to study the behavior of individuals and the interactions between them. In the political sphere, being able to query datasets of voting records provides interesting insights for data journalists and political analysts. In particular, such data can be leveraged for the investigation of exceptionally consensual/controversial topics. Consider data describing the voting behavior in the European Parliament (EP). Such a dataset records the votes of each member (MEP) in voting sessions held in the parliament, as well as information on the parliamentarians (e.g., gender, national party, European party alliance) and the sessions (e.g., topic, date). This dataset offers opportunities to study the agreement or disagreement of coherent subgroups, especially to highlight unexpected behavior. It is to be expected that on the majority of voting sessions, MEPs will vote along the lines of their European party alliance. However, when matters are of interest to a specific nation within Europe, alignments may change and agreements can be formed or dissolved. For instance, when a legislative procedure on fishing rights is put before the MEPs, the island nation of the UK can be expected to agree on a specific course of action regardless of their party alliance, fostering an exceptional agreement where strong polarization exists otherwise. In this thesis, we aim to discover such exceptional (dis)agreement patterns not only in voting data but also in more generic data, called behavioral data, which involves individuals performing observable actions on entities. We devise two novel methods which offer complementary angles of exceptional (dis)agreement in behavioral data: within and between groups. These two approaches called Debunk and Deviant, ideally, enables the implementation of a sufficiently comprehensive tool to highlight, summarize and analyze exceptional comportments in behavioral data. We thoroughly investigate the qualitative and quantitative performances of the devised methods. Furthermore, we motivate their usage in the context of computational journalism
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10

Wesley, S. Scott. "Background data subgroups and career outcomes : some developmental influences on person job-matching." Diss., Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/31065.

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Книги з теми "Data Subgroup"

1

Atzmüller, Martin. Knowledge-intensive subgroup mining: Techniques for automatic and interactive discovery. Berlin: Aka, Akademische Verlagsgsellschaft, 2007.

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2

Office, General Accounting. Decennial census: Methods for collecting and reporting Hispanic subgroup data need refinement : report to Congressional Requesters. [Washington, D.C.]: GAO, 2003.

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3

Siek-Toon, Khoo, Goff Ginger Nelson, and Educational Resources Information Center (U.S.), eds. Multidimensional description of subgroup differences in mathematics achievement data from the 1992 National Assessment of Educational Progress: Draft. Los Angeles CA: National Center for Research on Evaluation, Standards, and Student Testing, 1994.

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4

Wright, Thomas L. Chemical data for flows and feeder dikes of the Yakima Basalt Subgroup, Columbia River Basalt Group, Washington, Oregon, and Idaho, and their bearing on a petrogenetic model. Washington: U.S. G.P.O., 1989.

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5

1932-, Kameny Iris, United States. Defense Modeling and Simulation Office. Data and Repositories Technology Working Group., National Defense Research Institute (U.S.), and United States. Dept. of Defense., eds. Defense Modeling and Simulation Office Data and Repositories Technology Working Group (DRTWG) meetings held February 7-10, 1995, and additional task force and subgroup meetings held between July 1994 and February 1995. Santa Monica, CA: Rand, 1995.

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6

Hajat, Anjum. Health outcomes among Hispanic subgroups: Data from the National Health Interview Survey, 1992-95. [Hyattsville, Md.] (6525 Belcrest Rd., Hyattsville 20782-2003): [U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2000.

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7

United States. Substance Abuse and Mental Health Services Administration. Office of Applied Studies., ed. Prevalence of substance use among racial and ethnic subgroups in the United States, 1991-1993. Rockville, Md. (5600 Fishers Lane, Rm. 16-105, Rockville 20857): Dept. of Health and Human Services, Substance Abuse and Mental Health Services Administration, Office of Applied Studies, 1998.

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8

Atzmuller, Martin. Knowledge-Intensive Subgroup Mining: Techniques for Automatic and Interactive Discovery - Volume 307 Dissertations in Artificial Intelligence - Infix ... in Artificial Intelligence). IOS Press, 2007.

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9

Tran, Thanh V., and Keith T. Chan. Applied Cross-Cultural Data Analysis for Social Work. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190888510.001.0001.

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Applied Cross-Cultural Data Analysis for Social Work is a research guide which provides a hands-on approach for learning and understanding data analysis techniques for examining and interpreting data for the purpose of cultural group comparisons. This book aims to provide practical applications in statistical approaches of data analyses that are commonly used in cross-cultural research and evaluation. Readers are presented with step-by-step illustrations in the use of descriptive, bivariate, and multivariate statistics to compare cross-cultural populations using large-scale, population-based survey data. These techniques have important applications in health, mental health, and social science research relevant to social work and other helping professions, especially in providing a framework of evidence to examine health disparities using population-health data. For each statistical approach discussed in this book, we explain the underlying purpose, basic assumptions, types of variables, application of the Stata statistical package, the presentation of statistical findings, and the interpretation of results. Unlike previous guides on statistical approaches and data analysis in social work, this book explains and demonstrates the strategies of cross-cultural data analysis using descriptive and bivariate analysis, multiple regression, additive and multiplicative interaction, mediation, and SEM and HLM for subgroup analysis and cross-cultural comparisons. This book also includes sample syntax from Stata for social work researchers to conduct cross-cultural analysis with their own research.
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10

Proctor, Kim. Measuring Group Consciousness. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.33.

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Although group consciousness is an important concept in explaining political behavior, both theoretical guidance on how to measure group consciousness and empirical consensus regarding its operationalization are lacking. This has the potential to lead to both diverging results and inaccurate empirical conclusions, which greatly limits the ability to understand the role that group consciousness plays in politics. Using data from Pew’s 2013 “Survey of LGBT Americans,” this analysis provides a foundation for measuring group consciousness using item response theory (IRT). Through an examination of dimensionality, monotonicity, model fit, and differential item functioning, the results demonstrate that many assumptions about measuring group consciousness have been incorrect. Further, the findings suggest that previous conclusions about subgroup differences may be the result of survey bias, rather than actual between-group differences. Moving forward, scholars of political behavior should use IRT to measure latent constructs.
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Частини книг з теми "Data Subgroup"

1

Cleophas, Ton J., and Aeilko H. Zwinderman. "Subgroup Analysis." In Understanding Clinical Data Analysis, 141–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39586-9_7.

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Klösgen, W. "Subgroup Mining." In Computational Intelligence in Data Mining, 39–49. Vienna: Springer Vienna, 2000. http://dx.doi.org/10.1007/978-3-7091-2588-5_2.

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Kim, Ju Han. "Gene Set Approaches and Prognostic Subgroup Prediction." In Genome Data Analysis, 135–57. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_8.

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Lavrač, Nada. "Subgroup Discovery Techniques and Applications." In Advances in Knowledge Discovery and Data Mining, 2–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11430919_2.

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5

Atzmueller, Martin, Juergen Mueller, and Martin Becker. "Exploratory Subgroup Analytics on Ubiquitous Data." In Lecture Notes in Computer Science, 1–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14723-9_1.

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Gähler, Franz. "Computer checking of the subgroup data." In International Tables for Crystallography, 27–28. Chester, England: International Union of Crystallography, 2006. http://dx.doi.org/10.1107/97809553602060000540.

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Gähler, Franz. "Computer checking of the subgroup data." In International Tables for Crystallography, 25–26. Chester, England: International Union of Crystallography, 2011. http://dx.doi.org/10.1107/97809553602060000792.

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8

Dzyuba, Vladimir, and Matthijs van Leeuwen. "Interactive Discovery of Interesting Subgroup Sets." In Advances in Intelligent Data Analysis XII, 150–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41398-8_14.

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9

Millot, Alexandre, Rémy Cazabet, and Jean-François Boulicaut. "Optimal Subgroup Discovery in Purely Numerical Data." In Advances in Knowledge Discovery and Data Mining, 112–24. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47436-2_9.

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10

Cleophas, Ton J., and Aeilko H. Zwinderman. "Subgroup Characteristics Assessed as Dependent Adverse Effects." In Analysis of Safety Data of Drug Trials, 183–93. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05804-3_14.

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Тези доповідей конференцій з теми "Data Subgroup"

1

Lemmerich, Florian, and Frank Puppe. "Local Models for Expectation-Driven Subgroup Discovery." In 2011 IEEE 11th International Conference on Data Mining (ICDM). IEEE, 2011. http://dx.doi.org/10.1109/icdm.2011.94.

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2

Yang, Xi, Yuan Zhang, and Min Chi. "Time-aware Subgroup Matrix Decomposition: Imputing Missing Data Using Forecasting Events." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622436.

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Liu, Jing, Yu Jiang, Zechao Li, Xi Zhang, and Hanqing Lu. "Domain-sensitive Recommendation with user-item subgroup analysis." In 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 2016. http://dx.doi.org/10.1109/icde.2016.7498377.

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Lijffijt, Jefrey, Bo Kang, Wouter Duivesteijn, Kai Puolamaki, Emilia Oikarinen, and Tijl De Bie. "Subjectively Interesting Subgroup Discovery on Real-Valued Targets." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00148.

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Mathonat, Romain, Diana Nurbakova, Jean-Francois Boulicaut, and Mehdi Kaytoue. "Anytime Subgroup Discovery in High Dimensional Numerical Data." In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2021. http://dx.doi.org/10.1109/dsaa53316.2021.9564223.

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Trabold, Daniel, and Henrik Grosskreutz. "Parallel subgroup discovery on computing clusters — First results." In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691625.

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Purucker, Lennart, Felix Stamm, Florian Lemmerich, and Joeran Beel. "Estimating the Pruned Search Space Size of Subgroup Discovery." In 2022 IEEE International Conference on Data Mining (ICDM). IEEE, 2022. http://dx.doi.org/10.1109/icdm54844.2022.00147.

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Padillo, F., J. M. Luna, and S. Ventura. "Subgroup discovery on big data: Pruning the search space on exhaustive search algorithms." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840799.

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9

Meeng, Marvin, Wouter Duivesteijn, and Arno Knobbe. "ROCsearch — An ROC-guided Search Strategy for Subgroup Discovery." In Proceedings of the 2014 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973440.81.

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10

Shukla, Piyush Kumar, Pradeep Rusiya, Deepak Agrawal, Lata Chhablani, and Balwant Singh Raghuwanshi. "Multiple Subgroup Data Compression Technique Based on Huffman Coding." In 2009 First International Conference on Computational Intelligence, Communication Systems and Networks (CICSYN). IEEE, 2009. http://dx.doi.org/10.1109/cicsyn.2009.86.

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Звіти організацій з теми "Data Subgroup"

1

Kim, Kang Seog. SUBGR: A Program to Generate Subgroup Data for the Subgroup Resonance Self-Shielding Calculation. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1261346.

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2

Wu, Ling, Tao Zhang, Yao Wang, Xiao Ke Wu, Tin Chiu Li, Pui Wah Chung, and Chi Chiu Wang. Polymorphisms and premature ovarian insufficiency and failure: A comprehensive meta-analysis update, subgroup, ranking, and network analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2022. http://dx.doi.org/10.37766/inplasy2022.1.0052.

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Review question / Objective: Early identification of women potentially who develop POI and POF is essential for early screening and treatment to improve clinical outcomes. We aim to conduct a comprehensive meta-analysis update, subgroup, ranking and network analysis for all available genetic polymorphism and associated with the POI and POF risk. Information sources: Six electronic databases will be included such as PubMed, Web of Science, Embase, MEDLINE, WANFANG DATA, CNKI. Will contact with authors by emails when necessary.
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Wells, Aaron, Tracy Christopherson, Gerald Frost, Matthew Macander, Susan Ives, Robert McNown, and Erin Johnson. Ecological land survey and soils inventory for Katmai National Park and Preserve, 2016–2017. National Park Service, September 2021. http://dx.doi.org/10.36967/nrr-2287466.

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This study was conducted to inventory, classify, and map soils and vegetation within the ecosystems of Katmai National Park and Preserve (KATM) using an ecological land survey (ELS) approach. The ecosystem classes identified in the ELS effort were mapped across the park, using an archive of Geo-graphic Information System (GIS) and Remote Sensing (RS) datasets pertaining to land cover, topography, surficial geology, and glacial history. The description and mapping of the landform-vegetation-soil relationships identified in the ELS work provides tools to support the design and implementation of future field- and RS-based studies, facilitates further analysis and contextualization of existing data, and will help inform natural resource management decisions. We collected information on the geomorphic, topographic, hydrologic, pedologic, and vegetation characteristics of ecosystems using a dataset of 724 field plots, of which 407 were sampled by ABR, Inc.—Environmental Research and Services (ABR) staff in 2016–2017, and 317 were from existing, ancillary datasets. ABR field plots were located along transects that were selected using a gradient-direct sampling scheme (Austin and Heligers 1989) to collect data for the range of ecological conditions present within KATM, and to provide the data needed to interpret ecosystem and soils development. The field plot dataset encompassed all of the major environmental gradients and landscape histories present in KATM. Individual state-factors (e.g., soil pH, slope aspect) and other ecosystem components (e.g., geomorphic unit, vegetation species composition and structure) were measured or categorized using standard classification systems developed for Alaska. We described and analyzed the hierarchical relationships among the ecosystem components to classify 92 Plot Ecotypes (local-scale ecosystems) that best partitioned the variation in soils, vegetation, and disturbance properties observed at the field plots. From the 92 Plot Ecotypes, we developed classifications of Map Ecotypes and Disturbance Landscapes that could be mapped across the park. Additionally, using an existing surficial geology map for KATM, we developed a map of Generalized Soil Texture by aggregating similar surficial geology classes into a reduced set of classes representing the predominant soil textures in each. We then intersected the Ecotype map with the General-ized Soil Texture Map in a GIS and aggregated combinations of Map Ecotypes with similar soils to derive and map Soil Landscapes and Soil Great Groups. The classification of Great Groups captures information on the soil as a whole, as opposed to the subgroup classification which focuses on the properties of specific horizons (Soil Survey Staff 1999). Of the 724 plots included in the Ecotype analysis, sufficient soils data for classifying soil subgroups was available for 467 plots. Soils from 8 orders of soil taxonomy were encountered during the field sampling: Alfisols (<1% of the mapped area), Andisols (3%), Entisols (45%), Gelisols (<1%), Histosols (12%), Inceptisols (22%), Mollisols (<1%), and Spodosols (16%). Within these 8 Soil Orders, field plots corresponded to a total of 74 Soil Subgroups, the most common of which were Typic Cryaquents, Typic Cryorthents, Histic Cryaquepts, Vitrandic Cryorthents, and Typic Cryofluvents.
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4

Caulfield, Laura E., Wendy L. Bennett, Susan M. Gross, Kristen M. Hurley, S. Michelle Ogunwole, Maya Venkataramani, Jennifer L. Lerman, Allen Zhang, Ritu Sharma, and Eric B. Bass. Maternal and Child Outcomes Associated With the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Agency for Healthcare Research and Quality (AHRQ), April 2022. http://dx.doi.org/10.23970/ahrqepccer253.

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Objectives. The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) aims to safeguard the health of low-income, nutritionally at-risk pregnant and postpartum women and children less than 5 years old. This systematic review evaluates whether participation in WIC is associated with nutrition and health outcomes for women, infants, and children, and whether the associations vary by duration of participation or across subgroups. Because of major revisions to the WIC food package in 2009, we prioritized studies published since 2009 and included studies comparing outcomes before and after the 2009 food package change. Data sources. Using electronic publication databases, we conducted a literature search from January 2009 to September 2021 and a targeted search for selected outcomes from January 2000 to September 2021. Review methods. Paired team members independently screened search results, serially abstracted data, assessed risk of bias, and graded strength of evidence (SOE) using standard methods for observational studies. Results. We included 82 quantitative observational studies and 16 qualitative studies, with 49 studies comparing outcomes of WIC participants with WIC-eligible non-participants. WIC prenatal participation was associated with lower risk of three outcomes: preterm delivery (moderate SOE), low birth weight (moderate SOE), and infant mortality (moderate SOE). Prenatal WIC participation was associated with better maternal diet quality (low SOE), lower risk of inadequate gestational weight gain (low SOE), lower alcohol use in pregnancy (low SOE), and no difference in smoking (low SOE). Maternal WIC participation was associated with increased child preventive care and immunizations (each low SOE), and higher cognitive scores for children (low SOE). Child WIC participation was associated with better diet quality (moderate SOE), and greater intakes of 100 percent fruit juice, whole grain cereals, and age-appropriate milk (moderate SOE). Household WIC participation was associated with greater purchasing of healthy food groups (moderate SOE). Maternal WIC participation was not associated with breastfeeding initiation (moderate SOE). The evidence was insufficient for other outcomes related to maternal health and child growth. The evidence generally was insufficient on how WIC participation affects outcomes across subgroups. Conclusions. Maternal WIC participation was associated with improved birth outcomes, lower infant mortality, and better child cognitive development. WIC participation was associated with purchasing healthier foods and with improved diets for pregnant women and children. More research is needed on maternal health outcomes; food security; child growth, development, and academic achievement; and effectiveness of WIC in all segments of the eligible population.
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Chou, Roger, Rongwei Fu, Tracy Dana, Miranda Pappas, Erica Hart, and Kimberly M. Mauer. Interventional Treatments for Acute and Chronic Pain: Systematic Review. Agency for Healthcare Research and Quality (AHRQ), September 2021. http://dx.doi.org/10.23970/ahrqepccer247.

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Objective. To evaluate the benefits and harms of selected interventional procedures for acute and chronic pain that are not currently covered by the Centers for Medicare & Medicaid Services (CMS) but are relevant for and have potential utility for use in the Medicare population, or that are covered by CMS but for which there is important uncertainty or controversy regarding use. Data sources. Electronic databases (Ovid® MEDLINE®, PsycINFO®, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews) to April 12, 2021, reference lists, and submissions in response to a Federal Register notice. Review methods. Using predefined criteria and dual review, we selected randomized controlled trials (RCTs) for 10 interventional procedures and conditions that evaluated pain, function, health status, quality of life, medication use, and harms. Random effects meta-analysis was conducted for vertebral compression fracture; otherwise, outcomes were synthesized qualitatively. Effects were classified as small, moderate, or large using previously defined criteria. Results. Thirty-seven randomized trials (in 48 publications) were included. Vertebroplasty (13 trials) is probably more effective at reducing pain and improving function in older (>65 years of age) patients, but benefits are small (less than 1 point on a 10-point pain scale). Benefits appear smaller (but still present) in sham-controlled (5 trials) compared with usual care controlled trials (8 trials) and larger in trials of patients with more acute symptoms; however, testing for subgroup effects was limited by imprecision. Vertebroplasty is probably not associated with increased risk of incident vertebral fracture (10 trials). Kyphoplasty (2 trials) is probably more effective than usual care for pain and function in older patients with vertebral compression fracture at up to 1 month (moderate to large benefits) and may be more effective at >1 month to ≥1 year (small to moderate benefits) but has not been compared against sham therapy. Evidence on kyphoplasty and risk of incident fracture was conflicting. In younger (below age for Medicare eligibility) populations, cooled radiofrequency denervation for sacroiliac pain (2 trials) is probably more effective for pain and function versus sham at 1 and 3 months (moderate to large benefits). Cooled radiofrequency for presumed facet joint pain may be similarly effective versus conventional radiofrequency, and piriformis injection with corticosteroid for piriformis syndrome may be more effective than sham injection for pain. For the other interventional procedures and conditions addressed, evidence was too limited to determine benefits and harms. Conclusions. Vertebroplasty is probably effective at reducing pain and improving function in older patients with vertebral compression fractures; benefits are small but similar to other therapies recommended for pain. Evidence was too limited to separate effects of control type and symptom acuity on effectiveness of vertebroplasty. Kyphoplasty has not been compared against sham but is probably more effective than usual care for vertebral compression fractures in older patients. In younger populations, cooled radiofrequency denervation is probably more effective than sham for sacroiliac pain. Research is needed to determine the benefits and harms of the other interventional procedures and conditions addressed in this review.
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Spitzer, Sonja, Vanessa di Lego, Angela Greulich, and Raya Muttarak. A demographic perspective on human wellbeing: Concepts, measurement and population heterogeneity. Verlag der Österreichischen Akademie der Wissenschaften, September 2021. http://dx.doi.org/10.1553/populationyearbook2021.int01.

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Анотація:
This introduction to the 2021 special issue of the Vienna Yearbook of Population Research explores demographic perspectives on human wellbeing across time and space. While the idea of relating demographic parameters to wellbeing has been around for a while, a more concrete research agenda on this topic has only recently gained momentum. Reviewing the research presented in this volume, we show how existing theoretical concepts and methodological tools in demography can be used to make substantial advances in the study of wellbeing. We also touch upon the many challenges researchers face in defining and measuring wellbeing, with the most important debate being about whether the focus should be on objective or subjective measures. The studies discussed here define wellbeing as health and mortality; as income, education or other resources; as happiness or life satisfaction; or as a combination thereof. They cover wellbeing in historical and contemporary populations in high- and low-income countries, and also point out important barriers to research on wellbeing, including the lack of good quality data in many regions. Finally, we highlight the value of considering population heterogeneities when studying wellbeing in order to identify population subgroups who are likely to fall behind, which can have important policy implications.
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7

Chou, Roger, Jesse Wagner, Azrah Y. Ahmed, Ian Blazina, Erika Brodt, David I. Buckley, Tamara P. Cheney, et al. Treatments for Acute Pain: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), December 2020. http://dx.doi.org/10.23970/ahrqepccer240.

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Анотація:
Objectives. To evaluate the effectiveness and comparative effectiveness of opioid, nonopioid pharmacologic, and nonpharmacologic therapy in patients with specific types of acute pain, including effects on pain, function, quality of life, adverse events, and long-term use of opioids. Data sources. Electronic databases (Ovid® MEDLINE®, PsycINFO®, Embase®, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews) to August 2020, reference lists, and a Federal Register notice. Review methods. Using predefined criteria and dual review, we selected randomized controlled trials (RCTs) of outpatient therapies for eight acute pain conditions: low back pain, neck pain, other musculoskeletal pain, neuropathic pain, postoperative pain following discharge, dental pain (surgical or nonsurgical), pain due to kidney stones, and pain due to sickle cell disease. Meta-analyses were conducted on pharmacologic therapy for dental pain and kidney stone pain, and likelihood of repeat or rescue medication use and adverse events. The magnitude of effects was classified as small, moderate, or large using previously defined criteria, and strength of evidence was assessed. Results. One hundred eighty-three RCTs on the comparative effectiveness of therapies for acute pain were included. Opioid therapy was probably less effective than nonsteroidal anti-inflammatory drugs (NSAIDs) for surgical dental pain and kidney stones, and might be similarly effective as NSAIDs for low back pain. Opioids and NSAIDs were more effective than acetaminophen for surgical dental pain, but opioids were less effective than acetaminophen for kidney stone pain. For postoperative pain, opioids were associated with increased likelihood of repeat or rescue analgesic use, but effects on pain intensity were inconsistent. Being prescribed an opioid for acute low back pain or postoperative pain was associated with increased likelihood of use of opioids at long-term followup versus not being prescribed, based on observational studies. Heat therapy was probably effective for acute low back pain, spinal manipulation might be effective for acute back pain with radiculopathy, acupressure might be effective for acute musculoskeletal pain, an opioid might be effective for acute neuropathic pain, massage might be effective for some types of postoperative pain, and a cervical collar or exercise might be effective for acute neck pain with radiculopathy. Most studies had methodological limitations. Effect sizes were primarily small to moderate for pain, the most commonly evaluated outcome. Opioids were associated with increased risk of short-term adverse events versus NSAIDs or acetaminophen, including any adverse event, nausea, dizziness, and somnolence. Serious adverse events were uncommon for all interventions, but studies were not designed to assess risk of overdose, opioid use disorder, or long-term harms. Evidence on how benefits or harms varied in subgroups was lacking. Conclusions. Opioid therapy was associated with decreased or similar effectiveness as an NSAID for some acute pain conditions, but with increased risk of short-term adverse events. Evidence on nonpharmacological therapies was limited, but heat therapy, spinal manipulation, massage, acupuncture, acupressure, a cervical collar, and exercise were effective for specific acute pain conditions. Research is needed to determine the comparative effectiveness of therapies for sickle cell pain, acute neuropathic pain, neck pain, and management of postoperative pain following discharge; effects of therapies for acute pain on non-pain outcomes; effects of therapies on long-term outcomes, including long-term opioid use; and how benefits and harms of therapies vary in subgroups.
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8

Wu, Bin, Lixia Guo, Kaikai Zhen, and Chao Sun. Diagnostic and prognostic value of miRNAs in hepatoblastoma: A systematic review with meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2021. http://dx.doi.org/10.37766/inplasy2021.11.0045.

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Анотація:
Review question / Objective: Background and aim: Increasing evidence has revealed the valuable diagnostic and prognostic applications of dysregulated microRNAs (miRNAs) in hepatoblastoma (HB), the most common hepatic malignancy during childhood. However, these results are inconsistent and remain to be elucidated. In the present study, we aimed to systematically compile up-to-date information regarding the clinical value of miRNAs in HB. Methods: Articles concerning the diagnostic and prognostic value of single miRNAs for HB were searched from databases. The sensitivity (SEN), specificity (SPE), positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), area under the curve (AUC), and hazard ratios (HRs) were separately pooled to explore the diagnostic and prognostic performance of miRNA. Subgroup and meta-regression analyses were further carried out only in the event of heterogeneity. Results: In all, 20 studies, involving 264 HB patients and 206 healthy individuals, met the inclusion criteria in the six included literature articles. For the diagnostic analysis of miRNAs in HB, the pooled SEN and SPE were 0.76 (95% CI: 0.72–0.80) and 0.75 (95% CI: 0.70–0.80), respectively. Moreover, the pooled PLR was 2.79 (95% CI: 2.12–3.66), NLR was 0.34 (95% CI: 0.26–0.45), DOR was 10.24 (95% CI: 6.55–16.00), and AUC was 0.83, indicating that miRNAs had moderate diagnostic value in HB. For the prognostic analysis of miRNAs in HB, the abnormal expressions of miR-21, miR-34a, miR-34b, miR-34c, miR-492, miR-193, miR-222, and miR-224 in patients were confirmed to be associated with a worse prognosis. The pooled HR was 1.74 (95% CI: 1.20–2.29) for overall survival (OS) and 1.74 (95% CI: 1.31–2.18) for event-free survival (EFS), suggesting its potential as a prognostic indicator for HB. Conclusion: To the best of our knowledge, this is the first comprehensive systematic review and meta-analysis that examines the diagnostic and prognostic role of dysregulated miRNAs in HB patients. The combined meta-analysis results supported the previous individual finds that miRNAs might provide a new, noninvasive method for the diagnostic and prognostic analyses ofHB.
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Shumway, Dean A., Kimberly S. Corbin, Magdoleen H. Farah, Kelly E. Viola, Tarek Nayfeh, Samer Saadi, Vishal Shah, et al. Partial Breast Irradiation for Breast Cancer. Agency for Healthcare Research and Quality (AHRQ), January 2023. http://dx.doi.org/10.23970/ahrqepccer259.

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Анотація:
Objectives. To evaluate the comparative effectiveness and harms of partial breast irradiation (PBI) compared with whole breast irradiation (WBI) for early-stage breast cancer, and how differences in effectiveness and harms may be influenced by patient, tumor, and treatment factors, including treatment modality, target volume, dose, and fractionation. We also evaluated the relative financial toxicity of PBI versus WBI. Data sources. MEDLINE®, Embase®, Cochrane Central Registrar of Controlled Trials, Cochrane Database of Systematic Reviews, Scopus, and various grey literature sources from database inception to June 30, 2022. Review methods. We included randomized clinical trials (RCTs) and observational studies that enrolled adult women with early-stage breast cancer who received one of six PBI modalities: multi-catheter interstitial brachytherapy, single-entry catheter brachytherapy (also known as intracavitary brachytherapy), 3-dimensional conformal external beam radiation therapy (3DCRT), intensity-modulated radiation therapy (IMRT), proton radiation therapy, intraoperative radiotherapy (IORT). Pairs of independent reviewers screened and appraised studies. Results. Twenty-three original studies with 17,510 patients evaluated the comparative effectiveness of PBI, including 14 RCTs, 6 comparative observational studies, and 3 single-arm observational studies. PBI was not significantly different from WBI in terms of ipsilateral breast recurrence (IBR), overall survival, or cancer-free survival at 5 and 10 years (high strength of evidence [SOE]). Evidence for cosmetic outcomes was insufficient. Results were generally consistent when PBI modalities were compared with WBI, whether compared individually or combined. These PBI approaches included 3DCRT, IMRT, and multi-catheter interstitial brachytherapy. Compared with WBI, 3DCRT showed no difference in IBR, overall survival, or cancer-free survival at 5 and 10 years (moderate to high SOE); IMRT showed no difference in IBR or overall survival at 5 and 10 years (low SOE); multi-catheter interstitial brachytherapy showed no difference in IBR, overall survival, or cancer-free survival at 5 years (low SOE). Compared with WBI, IORT was associated with a higher IBR rate at 5, 10, and over 10 years (high SOE), with no difference in overall survival, cancer-free survival, or mastectomy-free survival (low to high SOE). There were significantly fewer acute adverse events (AEs) with PBI compared with WBI, with no apparent difference in late AEs (moderate SOE). Data about quality of life were limited. Head-to-head comparisons between the different PBI modalities showed insufficient evidence to estimate an effect on main outcomes. There were no significant differences in IBR or other outcomes according to patient, tumor, and treatment characteristics; however, data for subgroups were insufficient to draw conclusions. Eight studies addressed concepts closely related to financial toxicity. Compared with conventionally fractionated WBI, accelerated PBI was associated with lower transportation costs and days away from work. PBI was also associated with less subjective financial difficulty at various time points after radiotherapy. Conclusions. Clinical trials that compared PBI with WBI demonstrate no significant difference in the risk of IBR. PBI is associated with fewer acute AEs and may be associated with less financial toxicity. The current evidence supports the use of PBI in appropriately selected patients with early-stage breast cancer. Further investigation is needed to evaluate the outcomes of PBI in patients with various clinical and tumor characteristics, and to define optimal radiation treatment dose and technique for PBI.
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Mobley, Erin M., Diana J. Moke, Joel Milam, Carol Y. Ochoa, Julia Stal, Nosa Osazuwa, Maria Bolshakova, et al. Disparities and Barriers to Pediatric Cancer Survivorship Care. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepctb39.

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Objectives. Survival rates for pediatric cancer have dramatically increased since the 1970s, and the population of childhood cancer survivors (CCS) exceeds 500,000 in the United States. Cancer during childhood and related treatments lead to long-term health problems, many of which are poorly understood. These problems can be amplified by suboptimal survivorship care. This report provides an overview of the existing evidence and forthcoming research relevant to disparities and barriers for pediatric cancer survivorship care, outlines pending questions, and offers guidance for future research. Data sources. This Technical Brief reviews published peer-reviewed literature, grey literature, and Key Informant interviews to answer five Guiding Questions regarding disparities in the care of pediatric survivors, barriers to cancer survivorship care, proposed strategies, evaluated interventions, and future directions. Review methods. We searched research databases, research registries, and published reviews for ongoing and published studies in CCS to October 2020. We used the authors’ definition of CCS; where not specified, CCS included those diagnosed with any cancer prior to age 21. The grey literature search included relevant professional and nonprofit organizational websites and guideline clearinghouses. Key Informants provided content expertise regarding published and ongoing research, and recommended approaches to fill identified gaps. Results. In total, 110 studies met inclusion criteria. We identified 26 studies that assessed disparities in survivorship care for CCS. Key Informants discussed subgroups of CCS by race or ethnicity, sex, socioeconomic status, and insurance coverage that may experience disparities in survivorship care, and these were supported in the published literature. Key Informants indicated that major barriers to care are providers (e.g., insufficient knowledge), the health system (e.g., availability of services), and payers (e.g., network adequacy); we identified 47 studies that assessed a large range of barriers to survivorship care. Sixteen organizations have outlined strategies to address pediatric survivorship care. Our searches identified only 27 published studies that evaluated interventions to alleviate disparities and reduce barriers to care. These predominantly assessed approaches that targeted patients. We found only eight ongoing studies that evaluated strategies to address disparities and barriers. Conclusions. While research has addressed disparities and barriers to survivorship care for childhood cancer survivors, evidence-based interventions to address these disparities and barriers to care are sparse. Additional research is also needed to examine less frequently studied disparities and barriers and to evaluate ameliorative strategies in order to improve the survivorship care for CCS.
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