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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.
Повний текст джерелаАнотація:
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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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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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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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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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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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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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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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 < 2 years) or smaller (age ≥ 2 years) subgroup effects and wider CIs than both IPD meta-analyses (age < 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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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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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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Groenwold, Rolf H. H. "Confounding of Subgroup Analyses in Randomized Data." Archives of Internal Medicine 169, no. 16 (September 12, 2009): 1532. http://dx.doi.org/10.1001/archinternmed.2009.250.
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Foster, Jared C., Jeremy M. G. Taylor, and Stephen J. Ruberg. "Subgroup identification from randomized clinical trial data." Statistics in Medicine 30, no. 24 (August 4, 2011): 2867–80. http://dx.doi.org/10.1002/sim.4322.
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Benson, Scott J., Brian L. Ruis, Aly M. Fadly, and Kathleen F. Conklin. "The Unique Envelope Gene of the Subgroup J Avian Leukosis Virus Derives from ev/J Proviruses, a Novel Family of Avian Endogenous Viruses." Journal of Virology 72, no. 12 (December 1, 1998): 10157–64. http://dx.doi.org/10.1128/jvi.72.12.10157-10164.1998.
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ABSTRACT A new subgroup of avian leukosis virus (ALV), designated subgroup J, was identified recently. Viruses of this subgroup do not cross-interfere with viruses of the avian A, B, C, D, and E subgroups, are not neutralized by antisera raised against the other virus subgroups, and have a broader host range than the A to E subgroups. Sequence comparisons reveal that while the subgroup J envelope gene includes some regions that are related to those found inenv genes of the A to E subgroups, the majority of the subgroup J gene is composed of sequences either that are more similar to those of a member (E51) of the ancient endogenous avian virus (EAV) family of proviruses or that appear unique to subgroup J viruses. These data led to the suggestion that the ALV-Jenv gene might have arisen by multiple recombination events between one or more endogenous and exogenous viruses. We initiated studies to investigate the origin of the subgroup J envelope gene and in particular to determine the identity of endogenous sequences that may have contributed to its generation. Here we report the identification of a novel family of avian endogenous viruses that include env coding sequences that are over 95% identical to both the gp85 and gp37 coding regions of subgroup J viruses. We call these viruses the ev/J family. We also report the isolation of ev/J-encoded cDNAs, indicating that at least some members of this family are expressed. These data support the hypothesis that the subgroup J envelope gene was acquired by recombination with expressed endogenous sequences and are consistent with acquisition of this gene by only one recombination event.
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Robinson, Giles W., Matthew Parker, Tanya Kranenburg, Charles Lu, Xiang Chen, Li Ding, Timothy Phoenix, et al. "Use of whole genome sequencing to identify novel mutations in distinct subgroups of medulloblastoma." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 9518. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.9518.
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9518 Background: Medulloblastoma is a malignant childhood brain tumor comprising four discrete subgroups (SHH-subgroup, WNT-subgroup, subgroup-3 and subgroup-4). The genetic alterations that drive these subgroups and that might serve as treatment targets are largely unknown. Methods: We sequenced entire genomes of 37 tumors and matched normal blood. 136 somatically mutated genes identified in this discovery cohort were sequenced in an additional 56 medulloblastomas. All tumors were classified into the 4 subgroups by expression profiling and immunohistochemistry. All mutations were validated by custom capture, 454, or Sanger sequencing. Results: Recurrent mutations were detected in 49 genes: 41 are not yet implicated in medulloblastoma. Several target distinct components of the epigenetic machinery in different disease subgroups, e.g., regulators of H3K27 and H3K4 trimethylation in subgroup-3 and 4 (e.g., KDM6A and ZMYM3), and CTNNB1-associated chromatin remodellers in WNT-subgroup tumors (e.g., SMARCA4 and CREBBP). Modelling of mutations in mouse lower rhombic lip progenitors that generate WNT-subgroup tumours, identified genes that maintain this cell lineage (DDX3X) as well as mutated genes that initiate (CDH1) or cooperate (PIK3CA) in tumourigenesis. Conclusions: We have identified several new recurrent somatic mutations that are enriched in specific subgroups of medulloblastoma. Alterations affecting subgroup-3 and 4 tumors appear to disrupt chromatin marking, most notably H3K27me3, potentially preserving a stem cell-like state in tumor cells. Mutations in WNT subgroup tumors affect binding partners of CTNNB1 that regulate WNT-response gene transcription. These data provide important new insights into the pathogenesis of medulloblastoma subgroups and highlight targets for therapeutic development.
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Zhang, Sheng, Fei Liang, Wenfeng Li, and Xichun Hu. "Subgroup Analyses in Reporting of Phase III Clinical Trials in Solid Tumors." Journal of Clinical Oncology 33, no. 15 (May 20, 2015): 1697–702. http://dx.doi.org/10.1200/jco.2014.59.8862.
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Purpose Treatment decisions in clinical oncology are guided by results from phase III randomized clinical trials (RCTs). The results of subgroup analyses may be potentially important in individualizing patient care. We investigated the appropriateness of the use and interpretation of subgroup analyses in oncology RCTs on the basis of the CONSORT statement requirements. Methods Phase III RCTs published between January 1, 2011, and December 31, 2013, were reviewed to identify eligible studies of solid tumor treatments. Information related to the subgroup analyses included prespecification, number, subgroup factors, interaction test use, and claim of subgroup difference. Results A total of 221 publications reporting data on 184,500 patients were analyzed. One hundred eighty-eight (85%) RCTs were reported with subgroup analyses. Of those, 146 (78%) trials were reported with at least six subgroups. For the majority of trials with subgroup analyses (173; 92%), the actual number of subgroup analyses conducted cannot be determined. Only 59 (31%) RCTs were reported with fully prespecified subgroups and only 64 (34%) trials were reported with interaction tests. In addition, 102 (54%) RCTs were reported with claims of subgroup differences. Of those, only 18 claims of RCTs (18%) were based on significant interaction test results. Conclusion The reporting of subgroup analyses in contemporary oncology RCTs is neither uniform nor complete; it requires improvement to ensure consistency and to provide critical information for guiding patient care. Major problems include testing of a large number of subgroups, subgroups without prespecifications, and inadequate use of interaction tests.
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Aronson, Doron. "Subgroup analyses with special reference to the effect of antiplatelet agents in acute coronary syndromes." Thrombosis and Haemostasis 112, no. 07 (2014): 16–25. http://dx.doi.org/10.1160/th13-09-0801.
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SummaryControlled trials estimate treatment effects averaged over the reference population of subjects. However, physicians are interested in whether the treatment effect varies across subgroups (effect heterogeneity) in order to target specific subgroups to maximise the benefit of treatment and minimise harm. Therefore, large clinical trials of antiplatelet agents include subgroup analyses that examine whether treatment effects differ between subgroups of subjects identified by baseline characteristics. Reporting subgroup is pervasive and often accompanied by claims of difference of treatment effects between subgroups with potential important implications for clinical practice. However, subgroup-specific analyses of clinical trial data have inherent limitations that reduce their reliability. These include reduced statistical power, failure to specify the subgroups of interest a priori, failure to account for examining large numbers of subgroups, lack of strong rationale for biological response modification, and performing analyses based on variables measured post randomisation or in trials showing no overall difference between treatments. Rules for interpretation of subgroup findings in subgroups have been suggested but are frequently not applied. In this article we draw attention to the pitfalls of subgroup analyses in the context of recent trials of antiplatelet agents.
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Harris, E. K., and J. C. Boyd. "On dividing reference data into subgroups to produce separate reference ranges." Clinical Chemistry 36, no. 2 (February 1, 1990): 265–70. http://dx.doi.org/10.1093/clinchem/36.2.265.
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Abstract We consider statistical criteria for partitioning a reference database to obtain separate reference ranges for different subpopulations. Using general formulas relating population variances, sample sizes, and the normal deviate test for the significance of the difference between two subgroup means, we show that partitioning into separate ranges produces little reduction in between-person variability, even when the differences between means are highly significant statistically. However, when there is a clear physiological basis for distinguishing between certain subgroups, simulation studies show that partitioning may be necessary to obtain reference limits that cut off the desired proportions of low and high values in each subgroup. Guidelines based on these results are provided to help decide whether separate ranges should be obtained for a given analyte.
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Lyons-Warren, Ariel M., Michael F. Wangler, and Ying-Wooi Wan. "Cluster Analysis of Short Sensory Profile Data Reveals Sensory-Based Subgroups in Autism Spectrum Disorder." International Journal of Molecular Sciences 23, no. 21 (October 27, 2022): 13030. http://dx.doi.org/10.3390/ijms232113030.
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Autism spectrum disorder is a common, heterogeneous neurodevelopmental disorder lacking targeted treatments. Additional features include restricted, repetitive patterns of behaviors and differences in sensory processing. We hypothesized that detailed sensory features including modality specific hyper- and hypo-sensitivity could be used to identify clinically recognizable subgroups with unique underlying gene variants. Participants included 378 individuals with a clinical diagnosis of autism spectrum disorder who contributed Short Sensory Profile data assessing the frequency of sensory behaviors and whole genome sequencing results to the Autism Speaks’ MSSNG database. Sensory phenotypes in this cohort were not randomly distributed with 10 patterns describing 43% (162/378) of participants. Cross comparison of two independent cluster analyses on sensory responses identified six distinct sensory-based subgroups. We then characterized subgroups by calculating the percent of patients in each subgroup who had variants with a Combined Annotation Dependent Depletion (CADD) score of 15 or greater in each of 24,896 genes. Each subgroup exhibited a unique pattern of genes with a high frequency of variants. These results support the use of sensory features to identify autism spectrum disorder subgroups with shared genetic variants.
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Salomon, Björn. "Interspecific hybridizations in the Elymus semicostatus group (Poaceae)." Genome 36, no. 5 (October 1, 1993): 899–905. http://dx.doi.org/10.1139/g93-118.
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Meiotic pairing in 16 interspecific hybrids in the genus Elymus is reported. The hybrids were made among seven species in the Elymus semicostatus group, viz., E. semicostatus, E. validus (subgroup I), E. abolinii (subgroup II), E. fedtschenkoi, E. nevskii, E. praeruptus (subgroup III), and E. panormitanus (subgroup IV). All species are tetraploid (2n = 4x = 28) and possess the SY genomes. Meiotic pairing was distinctly higher in hybrids made within subgroups than between subgroups, but the genomes in E. panormitanus have differentiated from those in the other species. These results generally support the subdivision of the E. semicostatus group based on morphological data but also indicate that the subgroups are more distantly related than previously believed, and that the group may be nonmonophyletic.Key words: meiotic pairing, interspecific hybridization, relationships, Triticeae, Poaceae.
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Dong, Jing, Junni L. Zhang, Shuxi Zeng, and Fan Li. "Subgroup balancing propensity score." Statistical Methods in Medical Research 29, no. 3 (August 28, 2019): 659–76. http://dx.doi.org/10.1177/0962280219870836.
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This paper concerns estimation of subgroup treatment effects with observational data. Existing propensity score methods are mostly developed for estimating overall treatment effect. Although the true propensity scores balance covariates in any subpopulations, the estimated propensity scores may result in severe imbalance in subgroup samples. Indeed, subgroup analysis amplifies a bias-variance tradeoff, whereby increasing complexity of the propensity score model may help to achieve covariate balance within subgroups, but it also increases variance. We propose a new method, the subgroup balancing propensity score, to ensure good subgroup balance as well as to control the variance inflation. For each subgroup, the subgroup balancing propensity score chooses to use either the overall sample or the subgroup (sub)sample to estimate the propensity scores for the units within that subgroup, in order to optimize a criterion accounting for a set of covariate-balancing moment conditions for both the overall sample and the subgroup samples. We develop two versions of subgroup balancing propensity score corresponding to matching and weighting, respectively. We devise a stochastic search algorithm to estimate the subgroup balancing propensity score when the number of subgroups is large. We demonstrate through simulations that the subgroup balancing propensity score improves the performance of propensity score methods in estimating subgroup treatment effects. We apply the subgroup balancing propensity score method to the Italy Survey of Household Income and Wealth (SHIW) to estimate the causal effects of having debit card on household consumption for different income groups.
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Shiozawa, Yusuke, Luca Malcovati, Anna Gallì, Andrea Pellagatti, Hiromichi Suzuki, Tetsuichi Yoshizato, Yusuke Sato, et al. "Combined DNA and Transcriptome Sequencing Reveals Discrete Subtypes of Myelodysplasia." Blood 128, no. 22 (December 2, 2016): 1974. http://dx.doi.org/10.1182/blood.v128.22.1974.1974.
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Abstract Introduction Although gene expression profile of myelodysplastic syndromes (MDS) had been widely studied, gene expression-based disease classification was yet to be established. We performed combined DNA and transcriptome sequencing to assess the relationship between genomic lesions, transcriptomic data, hematologic phenotype, and clinical outcome were analyzed. Methods We enrolled a total of 214 patients with myeloid neoplasms with myelodysplasia, for whom complete clinical and pathological data were available. Oncogenic variants and copy number alterations were identified by targeted-capture sequencing using RNA baits designed for 89 known or putative driver genes in myeloid neoplasms and 1,674 single nucleotide polymorphisms. RNA sequencing was performed on both bone marrow mononuclear cells (BMMNCs) and CD34+ cells (n = 51), only CD34+ cells (n = 49), or BMMNCs alone (n = 114). Consensus clustering was performed to identify robust and stable molecular subgroups. Survival analyses were performed with the Kaplan-Meier method. Survival curves were compared using the log-rank test. Multivariate survival analyses were performed by means of Cox proportional hazards regression. Included variables were age, sex, %marrow blast, cytogenetic abnormalities, hemoglobin, absolute neutrophil count, and platelet levels. Results Unsupervised clustering of gene expression data of CD34+ cells from 100 cases identified two stable subgroups. The first subgroup was characterized by a lower blast count, and the up-regulation of genes specifically detected in erythroid lineages. By contrast, the second subgroup was significantly associated with an increased blast count, and expression of the genes related to stem/progenitor cells. These differences became more conspicuous when the comparison was made with healthy adults. Up-regulated expression of many signaling pathway genes, including MAPK, PI3K, and JAK/STAT signaling, was also a conspicuous feature of the second subgroup. To investigate the genetic basis of these unique expression profiles, we compared frequencies of genetic lesions between the two subgroups. The patients in the second subgroup had a higher number of mutations (median 2 [range 0-6] vs. 4 [0-10], P = 0.016) and copy number alterations (median 0 [0-6] vs. 0 [0-9], P = 0.0053) than those in the first subgroup. Among those lesions observed in >10% in either subgroup, SF3B1 and TET2 mutations were significantly enriched in the first subgroup (q-value < 0.1). Del(7)/del(7q), NRAS, and TP53 mutations were also more frequent in the second subgroup (q-value < 0.1). Clinical outcomes also differed substantially between both subgroups. Compared to the first subgroup, the second subgroup was significantly associated with a combined endpoint of death or leukemic transformation in either univariate (hazard ratio 20.3 [95% confidence interval (CI), 4.59-89.6], P < 0.001) or multivariate analysis (hazard ratio 15.5 [95% CI, 3.05-79.2], P < 0.001) at a median follow-up of 8.5 months (range, 0-103 months). Especially no leukemic transformation occurred in the first subgroup, which was in contrast to the very high leukemic transformation rate in the second subgroup (38%). These subgroups were based on the gene expression profile of bone marrow CD34+ cells purified from BMMNCs. To enhance clinical utility, we sought to construct a classifier of the molecular subgroups using gene expression of unfractionated BMMNCs. Among the 100 patients with CD34+ cells, 51 were also analyzed by RNA sequencing for BMMNCs, which were used as a training cohort. Ten-fold cross-validation on the training set identified a logistic regression model with 25 genes, which as applied to the remaining 114 cases with only BMMNC samples. Again, SF3B1 mutations were most significantly enriched in the predicted low-risk subgroup. Predicted high-risk subgroup was significantly associated with poor prognosis in either univariate or multivariate analysis. This gene expression-based classification enabled better stratification of patients at risk of leukemic transformation by combining information on bone marrow blast count. Conclusion We showed that myeloid neoplasms with myelodysplasia can be subgrouped into two major classes with erythroid and stem/progenitor cell signature. This newly developed molecular classification might improve risk prediction and treatment stratification of MDS. Disclosures Kataoka: Yakult: Honoraria; Kyowa Hakko Kirin: Honoraria; Boehringer Ingelheim: Honoraria. Makishima:The Yasuda Medical Foundation: Research Funding. Ogawa:Sumitomo Dainippon Pharma: Research Funding; Takeda Pharmaceuticals: Consultancy, Research Funding; Kan research institute: Consultancy, Research Funding.
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Cummins, C. J. "Fundamental domains for genus-zero and genus-one congruence subgroups." LMS Journal of Computation and Mathematics 13 (July 23, 2010): 222–45. http://dx.doi.org/10.1112/s1461157008000041.
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AbstractIn this paper, we compute Ford fundamental domains for all genus-zero and genus-one congruence subgroups. This is a continuation of previous work, which found all such groups, including ones that are not subgroups ofPSL(2,ℤ). To compute these fundamental domains, an algorithm is given that takes the following as its input: a positive square-free integerf, which determines a maximal discrete subgroup Γ0(f)+ofSL(2,ℝ); a decision procedure to determine whether a given element of Γ0(f)+is in a subgroupG; and the index ofGin Γ0(f)+. The output consists of: a fundamental domain forG, a finite set of bounding isometric circles; the cycles of the vertices of this fundamental domain; and a set of generators ofG. The algorithm avoids the use of floating-point approximations. It applies, in principle, to any group commensurable with the modular group. Included as appendices are: MAGMA source code implementing the algorithm; data files, computed in a previous paper, which are used as input to compute the fundamental domains; the data computed by the algorithm for each of the congruence subgroups of genus zero and genus one; and an example, which computes the fundamental domain of a non-congruence subgroup.
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Guo, Bing, and Carmen Moga. "OP72 Added Value Of Using Individual Patient Data Meta-analysis." International Journal of Technology Assessment in Health Care 34, S1 (2018): 26. http://dx.doi.org/10.1017/s0266462318001125.
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Introduction:Although individual patient data meta-analysis (IPD MA) is considered the gold standard of systematic reviews (SRs), a recent International Network of Agencies for Health Technology Assessment survey indicates that IPD MA is not frequently included in a health technology assessment (HTA), or conducted by HTA researchers. The objective of this presentation is to describe our first experience with including an IPD MA in a HTA report, discuss the added value for an evidence-based decision-making process, and advocate for expanding work in this field.Methods:An overview of SRs on endovascular therapy for acute ischemic stroke included one IPD MA and six study-level SRs/MAs. Methodological quality was appraised by two reviewers independently using the tool recommended by the Cochrane IPD MA working group for the IPD MA, and the AMSTAR (A MeaSurement Tool to Assess systematic Reviews) for the study-level reviews. Pooled results from subgroup analyses based on access to primary patient data were compared to those reported in SRs that conducted subgroup analyses based on the published data to identify patients or clinical factors that would impact clinical outcomes.Results:The overall findings were similar between the IPD MA and other SRs/MAs. However, when compared to aggregated data used in study-level SRs/MAs, subgroup analyses based on patient data allowed for adjustment of confounders, multiple categories within a subgroup, standardization of outcomes across trials, and detailed data checking. Larger sample sizes of each pre-defined subgroup permitted for more precise estimates of treatment effects. A number of methodological issues in the IPD MA were identified; particularly, no assessment of risk of bias of included trials was conducted.Conclusions:Access to original patient data is demanding and conducting IPD MA requires extensive resources. The advantages of having an improved quality analysis, an appropriate quantification of the effects in the analyzed subgroups, and precision of results may justify additional efforts, and may increase confidence in the decision-making process.
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Bello-Chavolla, Omar Yaxmehen, Jessica Paola Bahena-López, Arsenio Vargas-Vázquez, Neftali Eduardo Antonio-Villa, Alejandro Márquez-Salinas, Carlos A. Fermín-Martínez, Rosalba Rojas, et al. "Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach." BMJ Open Diabetes Research & Care 8, no. 1 (July 2020): e001550. http://dx.doi.org/10.1136/bmjdrc-2020-001550.
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IntroductionPrevious reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methodsWe trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.ResultsSNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).ConclusionsDiabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications.
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Nikolaidis, Nikolas, and Zacharias G. Scouras. "The Drosophila montium subgroup species. Phylogenetic relationships based on mitochondrial DNA analysis." Genome 39, no. 5 (October 1, 1996): 874–83. http://dx.doi.org/10.1139/g96-110.
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Mitochondrial DNA (mtDNA) restriction site maps for three Drosophila montium subgroup species of the melanogaster species group, inhabiting Indian and Afrotropical montium subgroup territories, were established. Taking into account previous mtDNA data concerning six oriental montium species, a phylogeny was established using distance-matrix and parsimony methods. Both genetic diversity and mtDNA size variations were found to be very narrow, suggesting close phylogenetic relationships among all montium species studied. The phylogenetic trees that were constructed revealed three main lineages for the montium subgroup species studied: one consisting of the Afrotropical species Drosophila seguyi, which is placed distantly from the other species, one comprising the north-oriental (Palearctic) species, and one comprising the southwestern (south-oriental, Australasian, Indian, and Afrotropical) species. The combination of the mtDNA data presented here with data from other species belonging to the melanogaster and obscura subgroups revealed two major clusters: melanogaster and obscura. The melanogaster cluster is further divided into two compact lineages, comprising the montium subgroup species and the melanogaster complex species; the species of the other complex of the melanogaster subgroup, yakuba, disperse among the obscura species. The above grouping is in agreement with the mtDNA size variations of the species. Overall, among all subgroups studied, the species of the montium subgroup seem to be the most closely related. Key words : mtDNA restriction site maps, mtDNA size variations, Drosophila, phylogeny.
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Karsidag, S., A. Akcal, S. Sahin, S. Karsidag, F. Kabukcuoglu, and K. Ugurlu. "Neurophysiological and morphological responses to treatment with acetyl-L-carnitine in a sciatic nerve injury model: preliminary data." Journal of Hand Surgery (European Volume) 37, no. 6 (November 11, 2011): 529–36. http://dx.doi.org/10.1177/1753193411426969.
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We investigated the effects of acetyl-L-carnitine (ALCAR) on the recovery of sciatic nerve injuries in rats. Sprague Dawley rats were randomized to two groups: ALCAR treated (for 14 days) and control. Each group was divided into three subgroups: distal transection, proximal transection, and grafted. Distal latencies, amplitudes, and motor nerve conduction velocities were measured. In the third month, biopsies were taken and examined under light microscopy. Electrophysiological measurements demonstrated that regeneration occurred earlier and was better in the ALCAR group, particularly in the distal transection subgroup. Better results were obtained in the distal transection subgroup in terms of axonal regeneration compared with the proximal transection and grafted subgroups because the regenerating segment was shorter. ALCAR enhanced the quality of neural recovery at the different levels and in different types of repair, and led to a decline in nerve death.
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Li, Peng-Fei, and Wei-Liang Chen. "Are the Different Diabetes Subgroups Correlated With All-Cause, Cancer-Related, and Cardiovascular-Related Mortality?" Journal of Clinical Endocrinology & Metabolism 105, no. 12 (September 7, 2020): e4240-e4251. http://dx.doi.org/10.1210/clinem/dgaa628.
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Abstract Context Numerous studies have shown that cardiovascular disease (CVD) represents the most important cause of mortality among people with diabetes mellitus (DM). However, no studies have evaluated the risk of CVD-related mortality among different DM subgroups. Objective We aimed to examine all-cause, CVD-related, and cancer-related mortality for different DM subgroups. Design, Setting, Patients, and Interventions We included participants (age ≥ 20 years) from the National Health and Nutrition Examination Survey III (NHANES III) data set. We evaluated the risks of all-cause and cause-specific (CVD and cancer) mortality among 5 previously defined diabetes subgroups: severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). Primary Outcome Measure The hazard ratios (HRs) for all-cause and cause-specific (CVD and cancer) mortality were measured for each of the 5 DM subgroups. We also evaluated the odds ratios (ORs) for retinopathy and nephropathy in each subgroup. Results A total of 712 adults were enrolled and the median follow-up time was 12.71 years (range, 0.25-18.08 years). The number of deaths in the 5 subgroups (SAID, SIDD, SIRD, MOD, and MARD) were 50, 75, 64, 7, and 18, respectively, and the number of CVD-related deaths in the 5 subgroups was 29, 30, 26, 2, and 11, respectively. Compared to the MOD subgroup, the adjusted HRs and 95% CIs of CVD-related mortality for the SAID, SIDD, SIRD, and MARD subgroups were 3.23 (95% CI, 0.77-13.61), 2.87 (95% CI, 0.68-12.06), 2.23 (95% CI, 0.53-9.50), and 4.75 (95% CI, 1.05-21.59), respectively (the HR for the MARD subgroup had a P value of .04). In addition, compared to the MARD subgroup, the adjusted ORs and 95% CIs for retinopathy in the SAID and SIDD groups were 2.38 (95% CI, 1.13-5.01, P = .02) and 3.34 (95% CI, 1.17-6.88, P = .001), respectively. The ORs for nephropathy were nonsignificant. Conclusions Our study of patients from the NHANES III data set indicated that among the different DM subgroups, the MARD subgroup tended to have a higher CVD-related mortality than the MOD subgroup. The all-cause and cancer-related mortality rates were similar across the different diabetes subgroups. In addition, compared to the MARD subgroup, the SAID and SIDD subgroups had a higher retinopathy risk, but there was no difference in nephropathy among the subgroups.
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Andrews, Nichole, and Hyunkeun Cho. "Validating effectiveness of subgroup identification for longitudinal data." Statistics in Medicine 37, no. 1 (September 25, 2017): 98–106. http://dx.doi.org/10.1002/sim.7500.
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Roossinck, Marilyn J., Lee Zhang, and Karl-Heinz Hellwald. "Rearrangements in the 5′ Nontranslated Region and Phylogenetic Analyses of Cucumber Mosaic Virus RNA 3 Indicate Radial Evolution of Three Subgroups." Journal of Virology 73, no. 8 (August 1, 1999): 6752–58. http://dx.doi.org/10.1128/jvi.73.8.6752-6758.1999.
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ABSTRACT Cucumber mosaic virus (CMV) has been divided into two subgroups based on serological data, peptide mapping of the coat protein, nucleic acid hybridization, and nucleotide sequence similarity. Analyses of a number of recently isolated strains suggest a further division of the subgroup I strains. Alignment of the 5′ nontranslated regions of RNA 3 for 26 strains of CMV suggests the division of CMV into subgroups IA, IB, and II and suggests that rearrangements, deletions, and insertions in this region may have been the precursors of the subsequent radiation of each subgroup. Phylogeny analyses of CMV using the coat protein open reading frame of 53 strains strongly support the further division of subgroup I into IA and IB. In addition, strains within each subgroup radiate from a single point of origin, indicating that they have evolved from a single common ancestor for each subgroup.
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Park, Hyun-Jeong, Dayeon Lee, and Hyeonju Kwon. "Fitting the GLMM tree to educational data: focusing on differential effects of afterschool program and private tutoring participation." Korean Society for Educational Evaluation 35, no. 4 (December 31, 2022): 577–607. http://dx.doi.org/10.31158/jeev.2022.35.4.577.
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This study aims to introduce the GLMM tree and show its usefulness in educational research. The GLMM tree model is an extension of MOB to be applied to multilevel data, which detects subgroups with differential effects to estimate the fixed-effect in each subgroup and the random effect by the cluster to which each observation belongs. In this study, we identified the differential effects by detecting subgroups of afterschool programs and private tutoring participation in mathematics achievement of second grade high school students. Using the GLMM trees, students were divided into 16 and 15 subgroups respectively according to the participation of afterschool programs and the private tutoring. Also, cognitive and affective variables such as pre-mathematics achievement, interest in mathematics were selected as nodes. For both treatments, it was confirmed that the fixed-effect was estimated differently for each subgroup. Based on the results, we compared the differential effects of participation in afterschool programs and private tutoring and factors selected as nodes of models, and discussed the potential of the GLMM tree model in educational research.
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Wu, Mei Jiun. "School Resources and Subgroup Performance Gains: What Works for Whom?" Educational Administration Quarterly 56, no. 2 (April 9, 2019): 220–54. http://dx.doi.org/10.1177/0013161x19840400.
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Using a fixed effects model, a balanced panel data set of 6,922 schools in California from 2004 to 2011 was analyzed to see whether changes in resources would affect subgroup performance at intraschool level. Seven school resources variables previously demonstrated influential to school or subgroup achievement at interschool level were tested for their effects on Academic Performance Index (API) gains of eight subgroups. Teachers’ in-district experience had the strongest positive impacts on API gains for all subgroups, ranging from 3.367 to 8.958 points, and teachers’ total experience had the largest negative impacts on subgroup API, varying between −1.120 and −5.495 points. Increases in teachers’ in-district experience, shares of highly educated and full-time equivalent teachers all offered promising outcomes for improving APIs of disadvantaged subgroups.
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Obura, Morgan, Joline W. J. Beulens, Roderick Slieker, Anitra D. M. Koopman, Trynke Hoekstra, Giel Nijpels, Petra Elders, et al. "Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes: An IMI-DIRECT study." PLOS ONE 15, no. 11 (November 30, 2020): e0242360. http://dx.doi.org/10.1371/journal.pone.0242360.
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Aim Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D. Methods The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders. Results At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1–3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18–1.92) for subgroup 2 and 1.88 (-0.08–3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose. Conclusions Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk.
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Bancks, Michael P., Alain G. Bertoni, Mercedes Carnethon, Haiying Chen, Mary Frances Cotch, Unjali P. Gujral, David Herrington, et al. "Association of Diabetes Subgroups With Race/Ethnicity, Risk Factor Burden and Complications: The MASALA and MESA Studies." Journal of Clinical Endocrinology & Metabolism 106, no. 5 (January 27, 2021): e2106-e2115. http://dx.doi.org/10.1210/clinem/dgaa962.
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Abstract Introduction There are known disparities in diabetes complications by race and ethnicity. Although diabetes subgroups may contribute to differential risk, little is known about how subgroups vary by race/ethnicity. Methods Data were pooled from 1293 (46% female) participants of the Mediators of Atherosclerosis in South Asians Living in America (MASALA) and the Multi-Ethnic Study of Atherosclerosis (MESA) who had diabetes (determined by diabetes medication use, fasting glucose, and glycated hemoglobin [HbA1c]), including 217 South Asian, 240 non-Hispanic white, 125 Chinese, 387 African American, and 324 Hispanic patients. We applied k-means clustering using data for age at diabetes diagnosis, body mass index, HbA1c, and homeostatic model assessment measures of insulin resistance and beta cell function. We assessed whether diabetes subgroups were associated with race/ethnicity, concurrent cardiovascular disease risk factors, and incident diabetes complications. Results Five diabetes subgroups were characterized by older age at diabetes onset (43%), severe hyperglycemia (26%), severe obesity (20%), younger age at onset (1%), and requiring insulin medication use (9%). The most common subgroup assignment was older onset for all race/ethnicities with the exception of South Asians where the severe hyperglycemia subgroup was most likely. Risk for renal complications and subclinical coronary disease differed by diabetes subgroup and, separately, race/ethnicity. Conclusions Racial/ethnic differences were present across diabetes subgroups, and diabetes subgroups differed in risk for complications. Strategies to eliminate racial/ethnic disparities in complications may need to consider approaches targeted to diabetes subgroup.
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Binon, Joë lle, Elena Bonaccorsi, Heinz-Jü rgen Bernhardt, and André Mathieu Fransolet. "The mineralogical status of "cavolinite" from Vesuvius, Italy, and crystallochemical data on the davyne subgroup." European Journal of Mineralogy 16, no. 3 (June 7, 2004): 511–20. http://dx.doi.org/10.1127/0935-1221/2004/0016-0511.
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Raghavendra, Meghana, Mohammed Al-Hamadani, and Ronald S. Go. "Long-Term (10 Years or More) Survivors Of Multiple Myeloma: A Population-Based Analysis Of The US National Cancer Data Base." Blood 122, no. 21 (November 15, 2013): 760. http://dx.doi.org/10.1182/blood.v122.21.760.760.
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Abstract Introduction Long-term survivors in multiple myeloma (MM), described as those surviving >10 years since their diagnosis, are uncommon. There is paucity of data describing this subgroup of patients and how they differ clinically from the rest. Methods Patients with MM diagnosed from 1998 to 2000 were identified in the National Cancer Data Base (NCDB). We obtained data associated with socio-demographics, type and location of care facility, as well as the use high dose chemotherapy/autologous stem cell transplant (ASCT) as initial treatment option. Four cohorts were created based on overall survival (OS): subgroup 1 (OS: < median); subgroup 2 (OS: median to 2X-median), subgroup 3 (OS: 2X-median to <10 years) and subgroup 4 (OS: >10 years). Results There were 27,987 MM patients. The median OS for the whole group was 26.7 months. Among them, 2,196 (7.9%) were long-term survivors. Subgroups 1, 2, and 3 comprised 54.8%, 19.0%, and 18.3% of the remaining patients, respectively. Majority were males (54.3%) with a mean age at diagnosis of 67.2 years (range, 19-90). Compared to the other subgroups (1/2/3), the long-term survivor subgroup had a significantly higher proportion of patients with high educational level (37.8% vs 28.4%/31.6%/33.9%; P < 0.001), high annual household income (41.5% vs 31.0%/34.2%/36.4%; P < 0.001), residence in a metro area (79.2% vs. 77.8%/78.7%/78.3%).; P=0.003), initial treatment at an academic center (46.6% vs 28.1%/34.6%/39.0%; P < 0.001), and had ASCT as part of initial therapy (16.5% vs 2.5%/6.4%/10.9%; P < 0.001). Multivariable analyses showed that younger age, non-Black race, lower educational level, non-Medicare/Medicaid primary payor, treatment at academic centers, and receipt of ASCT as part of initial treatment were significant independent predictors of survival > 10 years. In contrast, sex, ethnicity, type or geographic location of residence, and median annual household income were not significant. Conclusions In the US, approximately 1 in 13 MM patients diagnosed in 1998-2000 are long-term survivors. There are disparities in long-term outcomes according to socio-demographic characteristics, type of treatment facility, and receipt of ASCT as part of initial therapy. Disclosures: No relevant conflicts of interest to declare.
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Lahti, Ari, Per Hyltoft Petersen, James C. Boyd, Pål Rustad, Petter Laake, and Helge Erik Solberg. "Partitioning of Nongaussian-Distributed Biochemical Reference Data into Subgroups." Clinical Chemistry 50, no. 5 (May 1, 2004): 891–900. http://dx.doi.org/10.1373/clinchem.2003.027953.
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Abstract Background: The aim of this study was to develop new methods for partitioning biochemical reference data, covering in particular nongaussian distributions. Methods: We recently proposed partitioning criteria for gaussian distributions. These criteria relate to proportions of the subgroups outside each of the reference limits of the combined distribution (proportion criteria) and to distances between the subgroup distributions as correlates of these proportions (distance criteria). However, distance criteria do not seem to be ideal for nongaussian distributions because a generally valid relationship between proportions and distances cannot be established for these. Results: Proportion criteria appear preferable to distance criteria for two additional reasons: (a) The prevalences of the subgroup populations may have a considerable effect on stratification, but these are hard to account for by using distance criteria. Two methods to handle prevalences are described, the root method and the multiplication method. (b) Tied reference values, another complication of the partitioning problem, could also be hard to take care of using distance criteria. Some solutions to the problems caused by tied reference values are suggested. Conclusions: Partitioning of biochemical reference data should preferably be based on proportion criteria; this is particularly true for nongaussian distributions. Both of the described complications of the partitioning problem, the prevalences of the subgroups and tied reference values, are hard to deal with using distance criteria, but the proposed methods make it possible to account for them when proportion criteria are applied.
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Barrett, Deirdre. "Fantasizers and Dissociaters: Data on Two Distinct Subgroups of Deep Trance Subjects." Psychological Reports 71, no. 3 (December 1992): 1011–14. http://dx.doi.org/10.2466/pr0.1992.71.3.1011.
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This study delineated two subgroups of highly hypnotizable subjects. The first ( n = 19) entered trance rapidly, scored high on absorption, and described hypnosis as much like their rich and vivid waking fantasy life. The second subgroup of 15 took time to achieve a deep trance, saw hypnosis as very different from any prior experiences, and were more likely to exhibit amnesia for both hypnotic experience and waking fantasies.
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Roelvink, Peter W., Alena Lizonova, Jennifer G. M. Lee, Yuan Li, Jeffrey M. Bergelson, Robert W. Finberg, Douglas E. Brough, Imre Kovesdi, and Thomas J. Wickham. "The Coxsackievirus-Adenovirus Receptor Protein Can Function as a Cellular Attachment Protein for Adenovirus Serotypes from Subgroups A, C, D, E, and F." Journal of Virology 72, no. 10 (October 1, 1998): 7909–15. http://dx.doi.org/10.1128/jvi.72.10.7909-7915.1998.
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ABSTRACT Attachment of an adenovirus (Ad) to a cell is mediated by the capsid fiber protein. To date, only the cellular fiber receptor for subgroup C serotypes 2 and 5, the so-called coxsackievirus-adenovirus receptor (CAR) protein, has been identified and cloned. Previous data suggested that the fiber of the subgroup D serotype Ad9 also recognizes CAR, since Ad9 and Ad2 fiber knobs cross-blocked each other’s cellular binding. Recombinant fiber knobs and3H-labeled Ad virions from serotypes representing all six subgroups (A to F) were used to determine whether the knobs cross-blocked the binding of virions from different subgroups. With the exception of subgroup B, all subgroup representatives cross-competed, suggesting that they use CAR as a cellular fiber receptor as well. This result was confirmed by showing that CAR, produced in a soluble recombinant form (sCAR), bound to nitrocellulose-immobilized virions from the different subgroups except subgroup B. Similar results were found for blotted fiber knob proteins. The subgroup F virus Ad41 has both short and long fibers, but only the long fiber bound sCAR. The sCAR protein blocked the attachment of all virus serotypes that bound CAR. Moreover, CHO cells expressing human CAR, in contrast to untransformed CHO cells, all specifically bound the sCAR-binding serotypes. We conclude therefore that Ad serotypes from subgroups A, C, D, E, and F all use CAR as a cellular fiber receptor.
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Youngblood, Mark, Amar Sheth, Amy Zhao, Julio Montejo, Daniel Duran, Chang Li, Evgeniya Tyrtova, et al. "GENE-56. MENINGIOMA GENOMIC SUBGROUP AS A PREDICTOR OF POST-OPERATIVE PATIENT OUTCOMES: IMPLICATIONS FOR TREATMENT AND FOLLOW-UP." Neuro-Oncology 21, Supplement_6 (November 2019): vi109—vi110. http://dx.doi.org/10.1093/neuonc/noz175.458.
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Abstract BACKGROUND Meningiomas can be classified into six genomic subgroups based on mutations in NF2, SMARCB1, KLF4, POLR2A, or activating variants in the PI3K or Hedgehog signaling pathways. Previous work has identified specific associations of driver events with clinical and molecular features, such as tumor location. However, their utility in predicting post-operative patient outcomes is not well-explored. Similar to recently described epigenetic signatures, underlying genomic subgroup may provide prognostic value in meningioma management. METHODS Targeted sequencing data was used to classify over 500 meningiomas into established genomic subgroups, and available patient outcome data was assembled based on retrospective chart reviews. Collected data included recurrence (based on imaging), extent of resection (EOR), use of post-operative radiation, and radiologic follow-up period. Statistical associations between genomic subgroup and recurrence were assessed using Fisher’s exact, Kaplan-Meier, and Cox proportional hazards modeling, including stratification based on use of radiation, EOR, grade, and location. RESULTS Meningiomas in the PI3K subgroup exhibited higher rates of early recurrence during the first five post-operative years. This subgroup affiliation was found to be an independent predictor of recurrence free survival from Ki-67, grade, and other clinical features. By contrast, recurrence was rare in the POLR2A, SMARCB1, and KLF4 subgroups, and was typically associated with use of post-operative radiation in these cases. The longest average recurrence free survival was observed in POLR2A mutant meningiomas. CONCLUSIONS Our analysis identifies divergence in meningioma patient outcomes based on genomic subgroup and suggests patients with PI3K activating events may require closer surveillance. These tumors, which often occur near and encase critical neurovascular structures along the sphenoid wing, may further benefit from consideration of radiation and emerging precision therapies. Conversely, other subgroups rarely recur, suggesting caution be invoked with use of potentially morbid adjuvant treatment.
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Zhu, Yuan-Chang, Tong-Hua Wu, Guan-Gui Li, Biao Yin, Hong-Jie Liu, Cheng Song, Mei-Lan Mo, and Yong Zeng. "Decrease in fertilization and cleavage rates, but not in clinical outcomes for infertile men with AZF microdeletion of the Y chromosome." Zygote 23, no. 5 (October 15, 2014): 771–77. http://dx.doi.org/10.1017/s096719941400046x.
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SummaryThis study aimed to explore whether the presence of a Y chromosome azoospermia factor (AZF) microdeletion confers any adverse effect on embryonic development and clinical outcomes after intracytoplasmic sperm injection (ICSI) treatment. Fifty-seven patients with AZF microdeletion were included in the present study and 114 oligozoospermia and azoospermia patients without AZF microdeletion were recruited as controls. Both AZF and control groups were further divided into subgroups based upon the methods of semen collection: the AZF-testicular sperm extraction subgroup (AZF-TESE, n = 14), the AZF-ejaculation subgroup (AZF-EJA, n = 43), the control-TESE subgroup (n = 28) and the control-EJA subgroup (n = 86). Clinical data were analyzed in the two groups and four subgroups respectively. A retrospective case–control study was performed. A significantly lower fertilization rate (69.27 versus 75.70%, P = 0.000) and cleavage rate (89.55 versus 94.39%, P = 0.000) was found in AZF group compared with the control group. Furthermore, in AZF-TESE subgroup, the fertilization rate (67.54 versus 74.25%, P = 0.037) and cleavage rate (88.96 versus 94.79%, P = 0.022) were significantly lower than in the control-TESE subgroup; similarly, the fertilization rate (69.85 versus 75.85%, P = 0.004) and cleavage rate (89.36 versus 94.26%, P = 0.002) in AZF-EJA subgroup were significantly lower than in the control-EJA subgroup; however, the fertilization rate and cleavage rate in AZF-TESE (control-TESE) subgroup was similar to that in the AZF-EJA (control-EJA) subgroup. The other clinical outcomes were comparable between four subgroups (P > 0.05). Therefore, sperm from patients with AZF microdeletion, obtained either by ejaculation or TESE, may have lower fertilization and cleavage rates, but seem to have comparable clinical outcomes to those from patients without AZF microdeletion.
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Seibold, Heidi, Achim Zeileis, and Torsten Hothorn. "Model-Based Recursive Partitioning for Subgroup Analyses." International Journal of Biostatistics 12, no. 1 (May 1, 2016): 45–63. http://dx.doi.org/10.1515/ijb-2015-0032.
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AbstractThe identification of patient subgroups with differential treatment effects is the first step towards individualised treatments. A current draft guideline by the EMA discusses potentials and problems in subgroup analyses and formulated challenges to the development of appropriate statistical procedures for the data-driven identification of patient subgroups. We introduce model-based recursive partitioning as a procedure for the automated detection of patient subgroups that are identifiable by predictive factors. The method starts with a model for the overall treatment effect as defined for the primary analysis in the study protocol and uses measures for detecting parameter instabilities in this treatment effect. The procedure produces a segmented model with differential treatment parameters corresponding to each patient subgroup. The subgroups are linked to predictive factors by means of a decision tree. The method is applied to the search for subgroups of patients suffering from amyotrophic lateral sclerosis that differ with respect to their Riluzole treatment effect, the only currently approved drug for this disease.
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Spoer, Ben R., Filippa Juul, Pei Yang Hsieh, Lorna E. Thorpe, Marc N. Gourevitch, and Stella Yi. "Neighborhood-level Asian American Populations, Social Determinants of Health, and Health Outcomes in 500 US Cities." Ethnicity & Disease 31, no. 3 (July 15, 2021): 433–44. http://dx.doi.org/10.18865/ed.31.3.433.
Повний текст джерелаАнотація:
Introduction: The US Asian American (AA) population is projected to double by 2050, reaching ~43 million, and currently resides primarily in urban areas. Despite this, the geographic distribution of AA subgroup populations in US cities is not well-characterized, and social determinants of health (SDH) and health measures in places with significant AA/AA subgroup populations have not been described. Our research aimed to: 1) map the geographic distribution of AAs and AA subgroups at the city- and neighborhood- (census tract) level in 500 large US cities (population ≥66,000); 2) characterize SDH and health outcomes in places with significant AA or AA subgroup populations; and 3) compare SDH and health outcomes in places with significant AA or AA subgroup populations to SDH and health outcomes in places with significant non-Hispanic White (NHW) populations.Methods: Maps were generated using 2019 Census 5-year estimates. SDH and health outcome data were obtained from the City Health Dashboard, a free online data platform providing more than 35 measures of health and health drivers at the city and neighborhood level. T-tests compared SDH (unemployment, high-school completion, childhood poverty, income inequality, racial/ ethnic segregation, racial/ethnic diversity, percent uninsured) and health outcomes (obesity, frequent mental distress, cardiovascular disease mortality, life expectancy) in cities/neighborhoods with significant AA/AA subgroup populations to SDH and health outcomes in cities/neighborhoods with significant NHW populations (significant was defined as top population proportion quintile). We analyzed AA subgroups including Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, and Other AA.Results: The count and proportion of AA/ AA subgroup populations varied substantially across and within cities. When comparing cities with significant AA/AA subgroup populations vs NHW populations, there were few meaningful differences in SDH and health outcomes. However, when comparing neighborhoods within cities, areas with significant AA/AA subgroup vs NHW populations had less favorable SDH and health outcomes.Conclusion: When comparing places with significant AA vs NHW populations, city-level data obscured substantial variation in neighborhood-level SDH and health outcome measures. Our findings emphasize the dual importance of granular spatial and AA subgroup data in assessing the influence of SDH in AA populations.Ethn Dis. 2021;31(3):433-444; doi:10.18865/ed.31.3.433
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Hancock, Mark J., Per Kjaer, Lars Korsholm, and Peter Kent. "Interpretation of Subgroup Effects in Published Trials." Physical Therapy 93, no. 6 (June 1, 2013): 852–59. http://dx.doi.org/10.2522/ptj.20120296.
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With the rapidly expanding number of studies reporting on treatment subgroups come new challenges in analyzing and interpreting this sometimes complex area of the literature. This article discusses 3 important issues regarding the analysis and interpretation of existing trials or systematic reviews that report on treatment effect modifiers (subgroups) for specific physical therapy interventions. The key messages are: (1) point estimates of treatment modifier effect size (interaction effect) and their confidence intervals can be calculated using group-level data when individual patient-level data are not available; (2) interaction effects do not define the total effect size of the intervention in the subgroup but rather how much more effective it is in the subgroup than in those not in the subgroup; (3) recommendations regarding the use of an intervention in a subgroup need to consider the size and direction of the main effect and the interaction effect; and (4) rather than simply judging whether a treatment modifier effect is clinically important based only on the interaction effect size, a better criterion is to determine whether the combined effect of the interaction effect and main effect makes the difference between an overall effect that is clinically important and one that is not clinically important.
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Hsu, Yu-Yi, Jyoti Zalkikar, and Ram C. Tiwari. "Hierarchical Bayes approach for subgroup analysis." Statistical Methods in Medical Research 28, no. 1 (July 26, 2017): 275–88. http://dx.doi.org/10.1177/0962280217721782.
Повний текст джерелаАнотація:
In clinical data analysis, both treatment effect estimation and consistency assessment are important for a better understanding of the drug efficacy for the benefit of subjects in individual subgroups. The linear mixed-effects model has been used for subgroup analysis to describe treatment differences among subgroups with great flexibility. The hierarchical Bayes approach has been applied to linear mixed-effects model to derive the posterior distributions of overall and subgroup treatment effects. In this article, we discuss the prior selection for variance components in hierarchical Bayes, estimation and decision making of the overall treatment effect, as well as consistency assessment of the treatment effects across the subgroups based on the posterior predictive p-value. Decision procedures are suggested using either the posterior probability or the Bayes factor. These decision procedures and their properties are illustrated using a simulated example with normally distributed response and repeated measurements.
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Li, Hongjin, Anna L. Marsland, Yvette P. Conley, Susan M. Sereika, and Catherine M. Bender. "Genes Involved in the HPA Axis and the Symptom Cluster of Fatigue, Depressive Symptoms, and Anxiety in Women With Breast Cancer During 18 Months of Adjuvant Therapy." Biological Research For Nursing 22, no. 2 (January 7, 2020): 277–86. http://dx.doi.org/10.1177/1099800419899727.
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This study aimed to (1) identify subgroups of women with breast cancer with the psychological symptom cluster (fatigue, depressive symptoms, and anxiety) during the first 18 months of adjuvant therapy and (2) explore associations between demographic and clinical characteristics and variations in genetic polymorphisms related to hypothalamic–pituitary–adrenal (HPA) axis function and predicted symptom trajectory subgroup membership. We obtained symptom data at 4 time points from baseline to 18 months of adjuvant therapy among 292 postmenopausal women with breast cancer. Genetic data were collected in a subgroup at baseline ( N = 184). Group-based multitrajectory modeling was used to classify women into subgroups with similar psychological symptom cluster trajectories. Binary logistic regression was used to explore the associations between each genotypic and phenotypic predictor and predicted subgroup membership. Two distinct symptom subgroups (low and high) were identified based on the trajectories of the symptom cluster of fatigue, depressive symptoms, and anxiety over the first 18 months of adjuvant therapy. Women who were younger, less educated, and who received chemotherapy had greater likelihood of being in the high-symptom subgroup. Variation in genes regulating the HPA axis ( FKBP5 rs9394309 [odds ratio ( OR) = 3.98, p = .015], NR3C2 rs5525 [ OR = 2.54, p = .036], and CRHR1 rs12944712 [ OR = 3.99, p = .021]) was associated with membership in the high-symptom subgroup. These results may help to identify women with breast cancer who are at increased risk for psychological symptoms, facilitating the development of individualized and preemptive interventions to better manage these symptoms during adjuvant therapy.
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Umek, Lan, and Blaz Zupan. "Subgroup discovery in data sets with multi-dimensional responses." Intelligent Data Analysis 15, no. 4 (June 23, 2011): 533–49. http://dx.doi.org/10.3233/ida-2011-0481.
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Liu, Danlu, William Baskett, David Beversdorf, and Chi-Ren Shyu. "Exploratory Data Mining for Subgroup Cohort Discoveries and Prioritization." IEEE Journal of Biomedical and Health Informatics 24, no. 5 (May 2020): 1456–68. http://dx.doi.org/10.1109/jbhi.2019.2939149.
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Yeo, Tianrong, Fay Probert, Maciej Jurynczyk, Megan Sealey, Ana Cavey, Timothy D. W. Claridge, Mark Woodhall, et al. "Classifying the antibody-negative NMO syndromes." Neurology - Neuroimmunology Neuroinflammation 6, no. 6 (October 28, 2019): e626. http://dx.doi.org/10.1212/nxi.0000000000000626.
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ObjectiveTo determine whether unsupervised principal component analysis (PCA) of comprehensive clinico-radiologic data can identify phenotypic subgroups within antibody-negative patients with overlapping features of multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSDs), and to validate the phenotypic classifications using high-resolution nuclear magnetic resonance (NMR) plasma metabolomics with inference to underlying pathologies.MethodsForty-one antibody-negative patients were recruited from the Oxford NMO Service. Thirty-six clinico-radiologic parameters, focusing on features known to distinguish NMOSD and MS, were collected to build an unbiased PCA model identifying phenotypic subgroups within antibody-negative patients. Metabolomics data from patients with relapsing-remitting MS (RRMS) (n = 34) and antibody-positive NMOSD (Ab-NMOSD) (aquaporin-4 antibody n = 54, myelin oligodendrocyte glycoprotein antibody n = 20) were used to identify discriminatory plasma metabolites separating RRMS and Ab-NMOSD.ResultsPCA of the 36 clinico-radiologic parameters revealed 3 phenotypic subgroups within antibody-negative patients: an MS-like subgroup, an NMOSD-like subgroup, and a low brain lesion subgroup. Supervised multivariate analysis of metabolomics data from patients with RRMS and Ab-NMOSD identified myoinositol and formate as the most discriminatory metabolites (both higher in RRMS). Within antibody-negative patients, myoinositol and formate were significantly higher in the MS-like vs NMOSD-like subgroup; myoinositol (mean [SD], 0.0023 [0.0002] vs 0.0019 [0.0003] arbitrary units [AU]; p = 0.041); formate (0.0027 [0.0006] vs 0.0019 [0.0006] AU; p = 0.010) (AU).ConclusionsPCA identifies 3 phenotypic subgroups within antibody-negative patients and that the metabolite discriminators of RRMS and Ab-NMOSD suggest that these groupings have some pathogenic meaning. Thus, the identified clinico-radiologic discriminators may provide useful diagnostic clues when seeing antibody-negative patients in the clinic.
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Wang, Hongyue, Bokai Wang, Xin M. Tu, and Changyong Feng. "Inconsistency between overall and subgroup analyses." General Psychiatry 35, no. 3 (May 16, 2022): e100732. http://dx.doi.org/10.1136/gpsych-2021-100732.
Повний текст джерелаАнотація:
Suppose we have a sample of subjects in two treatment groups. To study the difference of the treatment effects, we can analyse the data using all subjects (overall analysis). We may also divide the subjects into several subgroups based on some covariates of interest (eg, gender), and study the treatment effects within each subgroup. The results of these two analyses may be different or even in opposite directions. In this paper, we give a general sufficient condition of consistency between the overall and subgroup analyses.
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Richter, Jakob, Katrin Madjar, and Jörg Rahnenführer. "Model-based optimization of subgroup weights for survival analysis." Bioinformatics 35, no. 14 (July 2019): i484—i491. http://dx.doi.org/10.1093/bioinformatics/btz361.
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AbstractMotivationTo obtain a reliable prediction model for a specific cancer subgroup or cohort is often difficult due to limited sample size and, in survival analysis, due to potentially high censoring rates. Sometimes similar data from other patient subgroups are available, e.g. from other clinical centers. Simple pooling of all subgroups can decrease the variance of the predicted parameters of the prediction models, but also increase the bias due to heterogeneity between the cohorts. A promising compromise is to identify those subgroups with a similar relationship between covariates and target variable and then include only these for model building.ResultsWe propose a subgroup-based weighted likelihood approach for survival prediction with high-dimensional genetic covariates. When predicting survival for a specific subgroup, for every other subgroup an individual weight determines the strength with which its observations enter into model building. MBO (model-based optimization) can be used to quickly find a good prediction model in the presence of a large number of hyperparameters. We use MBO to identify the best model for survival prediction of a specific subgroup by optimizing the weights for additional subgroups for a Cox model. The approach is evaluated on a set of lung cancer cohorts with gene expression measurements. The resulting models have competitive prediction quality, and they reflect the similarity of the corresponding cancer subgroups, with both weights close to 0 and close to 1 and medium weights.Availability and implementationmlrMBO is implemented as an R-package and is freely available at http://github.com/mlr-org/mlrMBO.