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Publisher: Oxford University Press   (Total: 409 journals)

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Showing 1 - 200 of 409 Journals sorted alphabetically
ACS Symposium Series     Full-text available via subscription   (Followers: 1, SJR: 0.189, CiteScore: 0)
Acta Biochimica et Biophysica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.79, CiteScore: 2)
Adaptation     Hybrid Journal   (Followers: 9, SJR: 0.143, CiteScore: 0)
Advances in Nutrition     Hybrid Journal   (Followers: 55, SJR: 2.196, CiteScore: 5)
Aesthetic Surgery J.     Hybrid Journal   (Followers: 6, SJR: 1.434, CiteScore: 1)
Aesthetic Surgery J. Open Forum     Open Access  
African Affairs     Hybrid Journal   (Followers: 67, SJR: 1.869, CiteScore: 2)
Age and Ageing     Hybrid Journal   (Followers: 90, SJR: 1.989, CiteScore: 4)
Alcohol and Alcoholism     Hybrid Journal   (Followers: 18, SJR: 1.376, CiteScore: 3)
American Entomologist     Hybrid Journal   (Followers: 8)
American Historical Review     Hybrid Journal   (Followers: 189, SJR: 0.467, CiteScore: 1)
American J. of Agricultural Economics     Hybrid Journal   (Followers: 47, SJR: 2.113, CiteScore: 3)
American J. of Clinical Nutrition     Hybrid Journal   (Followers: 199, SJR: 3.438, CiteScore: 6)
American J. of Epidemiology     Hybrid Journal   (Followers: 201, SJR: 2.713, CiteScore: 3)
American J. of Health-System Pharmacy     Full-text available via subscription   (Followers: 57, SJR: 0.595, CiteScore: 1)
American J. of Hypertension     Hybrid Journal   (Followers: 26, SJR: 1.322, CiteScore: 3)
American J. of Jurisprudence     Hybrid Journal   (Followers: 19, SJR: 0.281, CiteScore: 1)
American J. of Legal History     Full-text available via subscription   (Followers: 10, SJR: 0.116, CiteScore: 0)
American Law and Economics Review     Hybrid Journal   (Followers: 29, SJR: 1.053, CiteScore: 1)
American Literary History     Hybrid Journal   (Followers: 17, SJR: 0.391, CiteScore: 0)
Analysis     Hybrid Journal   (Followers: 24, SJR: 1.038, CiteScore: 1)
Animal Frontiers     Hybrid Journal   (Followers: 1)
Annals of Behavioral Medicine     Hybrid Journal   (Followers: 15, SJR: 1.423, CiteScore: 3)
Annals of Botany     Hybrid Journal   (Followers: 37, SJR: 1.721, CiteScore: 4)
Annals of Oncology     Hybrid Journal   (Followers: 57, SJR: 5.599, CiteScore: 9)
Annals of the Entomological Society of America     Full-text available via subscription   (Followers: 11, SJR: 0.722, CiteScore: 1)
Annals of Work Exposures and Health     Hybrid Journal   (Followers: 36, SJR: 0.728, CiteScore: 2)
Antibody Therapeutics     Open Access  
AoB Plants     Open Access   (Followers: 4, SJR: 1.28, CiteScore: 3)
Applied Economic Perspectives and Policy     Hybrid Journal   (Followers: 16, SJR: 0.858, CiteScore: 2)
Applied Linguistics     Hybrid Journal   (Followers: 59, SJR: 2.987, CiteScore: 3)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1, SJR: 1.241, CiteScore: 1)
Arbitration Intl.     Full-text available via subscription   (Followers: 22)
Arbitration Law Reports and Review     Hybrid Journal   (Followers: 14)
Archives of Clinical Neuropsychology     Hybrid Journal   (Followers: 31, SJR: 0.731, CiteScore: 2)
Aristotelian Society Supplementary Volume     Hybrid Journal   (Followers: 2)
Arthropod Management Tests     Hybrid Journal   (Followers: 2)
Astronomy & Geophysics     Hybrid Journal   (Followers: 45, SJR: 0.146, CiteScore: 0)
Behavioral Ecology     Hybrid Journal   (Followers: 55, SJR: 1.871, CiteScore: 3)
Bioinformatics     Hybrid Journal   (Followers: 366, SJR: 6.14, CiteScore: 8)
Biology Methods and Protocols     Hybrid Journal  
Biology of Reproduction     Full-text available via subscription   (Followers: 10, SJR: 1.446, CiteScore: 3)
Biometrika     Hybrid Journal   (Followers: 20, SJR: 3.485, CiteScore: 2)
BioScience     Hybrid Journal   (Followers: 30, SJR: 2.754, CiteScore: 4)
Bioscience Horizons : The National Undergraduate Research J.     Open Access   (Followers: 2, SJR: 0.146, CiteScore: 0)
Biostatistics     Hybrid Journal   (Followers: 17, SJR: 1.553, CiteScore: 2)
BJA : British J. of Anaesthesia     Hybrid Journal   (Followers: 195, SJR: 2.115, CiteScore: 3)
BJA Education     Hybrid Journal   (Followers: 67)
Brain     Hybrid Journal   (Followers: 73, SJR: 5.858, CiteScore: 7)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 50, SJR: 2.505, CiteScore: 5)
Briefings in Functional Genomics     Hybrid Journal   (Followers: 3, SJR: 2.15, CiteScore: 3)
British J. for the Philosophy of Science     Hybrid Journal   (Followers: 40, SJR: 2.161, CiteScore: 2)
British J. of Aesthetics     Hybrid Journal   (Followers: 24, SJR: 0.508, CiteScore: 1)
British J. of Criminology     Hybrid Journal   (Followers: 601, SJR: 1.828, CiteScore: 3)
British J. of Social Work     Hybrid Journal   (Followers: 88, SJR: 1.019, CiteScore: 2)
British Medical Bulletin     Hybrid Journal   (Followers: 6, SJR: 1.355, CiteScore: 3)
British Yearbook of Intl. Law     Hybrid Journal   (Followers: 36)
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 3, SJR: 1.376, CiteScore: 1)
Cambridge J. of Economics     Hybrid Journal   (Followers: 71, SJR: 0.764, CiteScore: 2)
Cambridge J. of Regions, Economy and Society     Hybrid Journal   (Followers: 12, SJR: 2.438, CiteScore: 4)
Cambridge Quarterly     Hybrid Journal   (Followers: 10, SJR: 0.104, CiteScore: 0)
Capital Markets Law J.     Hybrid Journal   (Followers: 2, SJR: 0.222, CiteScore: 0)
Carcinogenesis     Hybrid Journal   (Followers: 2, SJR: 2.135, CiteScore: 5)
Cardiovascular Research     Hybrid Journal   (Followers: 15, SJR: 3.002, CiteScore: 5)
Cerebral Cortex     Hybrid Journal   (Followers: 53, SJR: 3.892, CiteScore: 6)
CESifo Economic Studies     Hybrid Journal   (Followers: 24, SJR: 0.483, CiteScore: 1)
Chemical Senses     Hybrid Journal   (Followers: 1, SJR: 1.42, CiteScore: 3)
Children and Schools     Hybrid Journal   (Followers: 6, SJR: 0.246, CiteScore: 0)
Chinese J. of Comparative Law     Hybrid Journal   (Followers: 5, SJR: 0.412, CiteScore: 0)
Chinese J. of Intl. Law     Hybrid Journal   (Followers: 25, SJR: 0.329, CiteScore: 0)
Chinese J. of Intl. Politics     Hybrid Journal   (Followers: 11, SJR: 1.392, CiteScore: 2)
Christian Bioethics: Non-Ecumenical Studies in Medical Morality     Hybrid Journal   (Followers: 10, SJR: 0.183, CiteScore: 0)
Classical Receptions J.     Hybrid Journal   (Followers: 29, SJR: 0.123, CiteScore: 0)
Clean Energy     Open Access   (Followers: 1)
Clinical Infectious Diseases     Hybrid Journal   (Followers: 74, SJR: 5.051, CiteScore: 5)
Communication Theory     Hybrid Journal   (Followers: 26, SJR: 2.424, CiteScore: 3)
Communication, Culture & Critique     Hybrid Journal   (Followers: 28, SJR: 0.222, CiteScore: 1)
Community Development J.     Hybrid Journal   (Followers: 28, SJR: 0.268, CiteScore: 1)
Computer J.     Hybrid Journal   (Followers: 9, SJR: 0.319, CiteScore: 1)
Conservation Physiology     Open Access   (Followers: 3, SJR: 1.818, CiteScore: 3)
Contemporary Women's Writing     Hybrid Journal   (Followers: 10, SJR: 0.121, CiteScore: 0)
Contributions to Political Economy     Hybrid Journal   (Followers: 6, SJR: 0.906, CiteScore: 1)
Critical Values     Full-text available via subscription  
Current Developments in Nutrition     Open Access   (Followers: 4)
Current Legal Problems     Hybrid Journal   (Followers: 29)
Current Zoology     Full-text available via subscription   (Followers: 3, SJR: 1.164, CiteScore: 2)
Database : The J. of Biological Databases and Curation     Open Access   (Followers: 9, SJR: 1.791, CiteScore: 3)
Digital Scholarship in the Humanities     Hybrid Journal   (Followers: 14, SJR: 0.259, CiteScore: 1)
Diplomatic History     Hybrid Journal   (Followers: 22, SJR: 0.45, CiteScore: 1)
DNA Research     Open Access   (Followers: 5, SJR: 2.866, CiteScore: 6)
Dynamics and Statistics of the Climate System     Open Access   (Followers: 4)
Early Music     Hybrid Journal   (Followers: 17, SJR: 0.139, CiteScore: 0)
Econometrics J.     Hybrid Journal   (Followers: 32, SJR: 2.926, CiteScore: 1)
Economic J.     Hybrid Journal   (Followers: 117, SJR: 5.161, CiteScore: 3)
Economic Policy     Hybrid Journal   (Followers: 48, SJR: 3.584, CiteScore: 3)
ELT J.     Hybrid Journal   (Followers: 25, SJR: 0.942, CiteScore: 1)
English Historical Review     Hybrid Journal   (Followers: 57, SJR: 0.612, CiteScore: 1)
English: J. of the English Association     Hybrid Journal   (Followers: 20, SJR: 0.1, CiteScore: 0)
Environmental Entomology     Full-text available via subscription   (Followers: 12, SJR: 0.818, CiteScore: 2)
Environmental Epigenetics     Open Access   (Followers: 2)
Environmental History     Hybrid Journal   (Followers: 26, SJR: 0.408, CiteScore: 1)
EP-Europace     Hybrid Journal   (Followers: 3, SJR: 2.748, CiteScore: 4)
Epidemiologic Reviews     Hybrid Journal   (Followers: 9, SJR: 4.505, CiteScore: 8)
ESHRE Monographs     Hybrid Journal  
Essays in Criticism     Hybrid Journal   (Followers: 21, SJR: 0.113, CiteScore: 0)
European Heart J.     Hybrid Journal   (Followers: 68, SJR: 9.315, CiteScore: 9)
European Heart J. - Cardiovascular Imaging     Hybrid Journal   (Followers: 10, SJR: 3.625, CiteScore: 3)
European Heart J. - Cardiovascular Pharmacotherapy     Full-text available via subscription   (Followers: 3)
European Heart J. - Quality of Care and Clinical Outcomes     Hybrid Journal  
European Heart J. : Case Reports     Open Access   (Followers: 1)
European Heart J. Supplements     Hybrid Journal   (Followers: 8, SJR: 0.223, CiteScore: 0)
European J. of Cardio-Thoracic Surgery     Hybrid Journal   (Followers: 9, SJR: 1.681, CiteScore: 2)
European J. of Intl. Law     Hybrid Journal   (Followers: 214, SJR: 0.694, CiteScore: 1)
European J. of Orthodontics     Hybrid Journal   (Followers: 5, SJR: 1.279, CiteScore: 2)
European J. of Public Health     Hybrid Journal   (Followers: 21, SJR: 1.36, CiteScore: 2)
European Review of Agricultural Economics     Hybrid Journal   (Followers: 10, SJR: 1.172, CiteScore: 2)
European Review of Economic History     Hybrid Journal   (Followers: 30, SJR: 0.702, CiteScore: 1)
European Sociological Review     Hybrid Journal   (Followers: 44, SJR: 2.728, CiteScore: 3)
Evolution, Medicine, and Public Health     Open Access   (Followers: 12)
Family Practice     Hybrid Journal   (Followers: 15, SJR: 1.018, CiteScore: 2)
Fems Microbiology Ecology     Hybrid Journal   (Followers: 17, SJR: 1.492, CiteScore: 4)
Fems Microbiology Letters     Hybrid Journal   (Followers: 28, SJR: 0.79, CiteScore: 2)
Fems Microbiology Reviews     Hybrid Journal   (Followers: 35, SJR: 7.063, CiteScore: 13)
Fems Yeast Research     Hybrid Journal   (Followers: 14, SJR: 1.308, CiteScore: 3)
Food Quality and Safety     Open Access   (Followers: 1)
Foreign Policy Analysis     Hybrid Journal   (Followers: 25, SJR: 1.425, CiteScore: 1)
Forest Science     Hybrid Journal   (Followers: 8, SJR: 0.89, CiteScore: 2)
Forestry: An Intl. J. of Forest Research     Hybrid Journal   (Followers: 16, SJR: 1.133, CiteScore: 3)
Forum for Modern Language Studies     Hybrid Journal   (Followers: 6, SJR: 0.104, CiteScore: 0)
French History     Hybrid Journal   (Followers: 35, SJR: 0.118, CiteScore: 0)
French Studies     Hybrid Journal   (Followers: 21, SJR: 0.148, CiteScore: 0)
French Studies Bulletin     Hybrid Journal   (Followers: 10, SJR: 0.152, CiteScore: 0)
Gastroenterology Report     Open Access   (Followers: 3)
Genome Biology and Evolution     Open Access   (Followers: 17, SJR: 2.578, CiteScore: 4)
Geophysical J. Intl.     Hybrid Journal   (Followers: 38, SJR: 1.506, CiteScore: 3)
German History     Hybrid Journal   (Followers: 23, SJR: 0.161, CiteScore: 0)
GigaScience     Open Access   (Followers: 6, SJR: 5.022, CiteScore: 7)
Global Summitry     Hybrid Journal   (Followers: 1)
Glycobiology     Hybrid Journal   (Followers: 12, SJR: 1.493, CiteScore: 3)
Health and Social Work     Hybrid Journal   (Followers: 60, SJR: 0.388, CiteScore: 1)
Health Education Research     Hybrid Journal   (Followers: 15, SJR: 0.854, CiteScore: 2)
Health Policy and Planning     Hybrid Journal   (Followers: 24, SJR: 1.512, CiteScore: 2)
Health Promotion Intl.     Hybrid Journal   (Followers: 22, SJR: 0.812, CiteScore: 2)
History Workshop J.     Hybrid Journal   (Followers: 33, SJR: 1.278, CiteScore: 1)
Holocaust and Genocide Studies     Hybrid Journal   (Followers: 29, SJR: 0.105, CiteScore: 0)
Human Communication Research     Hybrid Journal   (Followers: 15, SJR: 2.146, CiteScore: 3)
Human Molecular Genetics     Hybrid Journal   (Followers: 9, SJR: 3.555, CiteScore: 5)
Human Reproduction     Hybrid Journal   (Followers: 74, SJR: 2.643, CiteScore: 5)
Human Reproduction Open     Open Access   (Followers: 1)
Human Reproduction Update     Hybrid Journal   (Followers: 20, SJR: 5.317, CiteScore: 10)
Human Rights Law Review     Hybrid Journal   (Followers: 63, SJR: 0.756, CiteScore: 1)
ICES J. of Marine Science: J. du Conseil     Hybrid Journal   (Followers: 58, SJR: 1.591, CiteScore: 3)
ICSID Review : Foreign Investment Law J.     Hybrid Journal   (Followers: 12)
ILAR J.     Hybrid Journal   (Followers: 3, SJR: 1.732, CiteScore: 4)
IMA J. of Applied Mathematics     Hybrid Journal   (SJR: 0.679, CiteScore: 1)
IMA J. of Management Mathematics     Hybrid Journal   (SJR: 0.538, CiteScore: 1)
IMA J. of Mathematical Control and Information     Hybrid Journal   (Followers: 2, SJR: 0.496, CiteScore: 1)
IMA J. of Numerical Analysis - advance access     Hybrid Journal   (SJR: 1.987, CiteScore: 2)
Industrial and Corporate Change     Hybrid Journal   (Followers: 10, SJR: 1.792, CiteScore: 2)
Industrial Law J.     Hybrid Journal   (Followers: 43, SJR: 0.249, CiteScore: 1)
Inflammatory Bowel Diseases     Hybrid Journal   (Followers: 44, SJR: 2.511, CiteScore: 4)
Information and Inference     Free  
Innovation in Aging     Open Access   (Followers: 1)
Insect Systematics and Diversity     Hybrid Journal  
Integrative and Comparative Biology     Hybrid Journal   (Followers: 10, SJR: 1.319, CiteScore: 2)
Integrative Biology     Full-text available via subscription   (Followers: 5, SJR: 1.36, CiteScore: 3)
Integrative Organismal Biology     Open Access  
Interacting with Computers     Hybrid Journal   (Followers: 10, SJR: 0.292, CiteScore: 1)
Interactive CardioVascular and Thoracic Surgery     Hybrid Journal   (Followers: 7, SJR: 0.762, CiteScore: 1)
Intl. Affairs     Hybrid Journal   (Followers: 69, SJR: 1.505, CiteScore: 3)
Intl. Data Privacy Law     Hybrid Journal   (Followers: 26)
Intl. Health     Hybrid Journal   (Followers: 6, SJR: 0.851, CiteScore: 2)
Intl. Immunology     Hybrid Journal   (Followers: 3, SJR: 2.167, CiteScore: 4)
Intl. J. for Quality in Health Care     Hybrid Journal   (Followers: 38, SJR: 1.348, CiteScore: 2)
Intl. J. of Constitutional Law     Hybrid Journal   (Followers: 62, SJR: 0.601, CiteScore: 1)
Intl. J. of Epidemiology     Hybrid Journal   (Followers: 272, SJR: 3.969, CiteScore: 5)
Intl. J. of Law and Information Technology     Hybrid Journal   (Followers: 5, SJR: 0.202, CiteScore: 1)
Intl. J. of Law, Policy and the Family     Hybrid Journal   (Followers: 25, SJR: 0.223, CiteScore: 1)
Intl. J. of Lexicography     Hybrid Journal   (Followers: 10, SJR: 0.285, CiteScore: 1)
Intl. J. of Low-Carbon Technologies     Open Access   (Followers: 1, SJR: 0.403, CiteScore: 1)
Intl. J. of Neuropsychopharmacology     Open Access   (Followers: 3, SJR: 1.808, CiteScore: 4)
Intl. J. of Public Opinion Research     Hybrid Journal   (Followers: 11, SJR: 1.545, CiteScore: 1)
Intl. J. of Refugee Law     Hybrid Journal   (Followers: 40, SJR: 0.389, CiteScore: 1)
Intl. J. of Transitional Justice     Hybrid Journal   (Followers: 13, SJR: 0.724, CiteScore: 2)
Intl. Mathematics Research Notices     Hybrid Journal   (Followers: 1, SJR: 2.168, CiteScore: 1)
Intl. Political Sociology     Hybrid Journal   (Followers: 40, SJR: 1.465, CiteScore: 3)
Intl. Relations of the Asia-Pacific     Hybrid Journal   (Followers: 24, SJR: 0.401, CiteScore: 1)
Intl. Studies Perspectives     Hybrid Journal   (Followers: 9, SJR: 0.983, CiteScore: 1)
Intl. Studies Quarterly     Hybrid Journal   (Followers: 51, SJR: 2.581, CiteScore: 2)
Intl. Studies Review     Hybrid Journal   (Followers: 25, SJR: 1.201, CiteScore: 1)
ISLE: Interdisciplinary Studies in Literature and Environment     Hybrid Journal   (Followers: 2, SJR: 0.15, CiteScore: 0)
ITNOW     Hybrid Journal   (Followers: 1, SJR: 0.103, CiteScore: 0)
J. of African Economies     Hybrid Journal   (Followers: 18, SJR: 0.533, CiteScore: 1)
J. of American History     Hybrid Journal   (Followers: 49, SJR: 0.297, CiteScore: 1)
J. of Analytical Toxicology     Hybrid Journal   (Followers: 14, SJR: 1.065, CiteScore: 2)
J. of Antimicrobial Chemotherapy     Hybrid Journal   (Followers: 15, SJR: 2.419, CiteScore: 4)
J. of Antitrust Enforcement     Hybrid Journal   (Followers: 1)
J. of Applied Poultry Research     Hybrid Journal   (Followers: 5, SJR: 0.585, CiteScore: 1)
J. of Biochemistry     Hybrid Journal   (Followers: 43, SJR: 1.226, CiteScore: 2)
J. of Breast Imaging     Full-text available via subscription   (Followers: 1)

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Similar Journals
Journal Cover
Biostatistics
Journal Prestige (SJR): 1.553
Citation Impact (citeScore): 2
Number of Followers: 17  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1465-4644 - ISSN (Online) 1468-4357
Published by Oxford University Press Homepage  [409 journals]
  • Biostatistics Volume 20 2019
    • Pages: 714 - 718
      Abstract: To search for subject areas, please see Biostatistics online at https://academic.oup.com/biostatistics, which is searchable across the volume.
      PubDate: Thu, 17 Oct 2019 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxz038
      Issue No: Vol. 20, No. 4 (2019)
       
  • Modeling recovery curves with application to prostatectomy
    • Authors: Wang F; Rudin C, Mccormick T, et al.
      Pages: 549 - 564
      Abstract: SummaryIn many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature.
      PubDate: Sat, 05 May 2018 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxy002
      Issue No: Vol. 20, No. 4 (2018)
       
  • A Bayesian hidden Potts mixture model for analyzing lung cancer pathology
           images
    • Authors: Li Q; Wang X, Liang F, et al.
      Pages: 565 - 581
      Abstract: SummaryDigital pathology imaging of tumor tissues, which captures histological details in high resolution, is fast becoming a routine clinical procedure. Recent developments in deep-learning methods have enabled the identification, characterization, and classification of individual cells from pathology images analysis at a large scale. This creates new opportunities to study the spatial patterns of and interactions among different types of cells. Reliable statistical approaches to modeling such spatial patterns and interactions can provide insight into tumor progression and shed light on the biological mechanisms of cancer. In this article, we consider the problem of modeling a pathology image with irregular locations of three different types of cells: lymphocyte, stromal, and tumor cells. We propose a novel Bayesian hierarchical model, which incorporates a hidden Potts model to project the irregularly distributed cells to a square lattice and a Markov random field prior model to identify regions in a heterogeneous pathology image. The model allows us to quantify the interactions between different types of cells, some of which are clinically meaningful. We use Markov chain Monte Carlo sampling techniques, combined with a double Metropolis–Hastings algorithm, in order to simulate samples approximately from a distribution with an intractable normalizing constant. The proposed model was applied to the pathology images of $205$ lung cancer patients from the National Lung Screening trial, and the results show that the interaction strength between tumor and stromal cells predicts patient prognosis (P = $0.005$). This statistical methodology provides a new perspective for understanding the role of cell–cell interactions in cancer progression.
      PubDate: Fri, 18 May 2018 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxy019
      Issue No: Vol. 20, No. 4 (2018)
       
  • Sequential rank agreement methods for comparison of ranked lists
    • Authors: Ekstrøm C; Gerds T, Jensen A.
      Pages: 582 - 598
      Abstract: SummaryThe comparison of alternative rankings of a set of items is a general and common task in applied statistics. Predictor variables are ranked according to magnitude of association with an outcome, prediction models rank subjects according to the personalized risk of an event, and genetic studies rank genes according to their difference in gene expression levels. We propose a sequential rank agreement measure to quantify the rank agreement among two or more ordered lists. This measure has an intuitive interpretation, it can be applied to any number of lists even if some are partially incomplete, and it provides information about the agreement along the lists. The sequential rank agreement can be evaluated analytically or be compared graphically to a permutation based reference set in order to identify changes in the list agreements. The usefulness of this measure is illustrated using gene rankings, and using data from two Danish ovarian cancer studies where we assess the within and between agreement of different statistical classification methods.
      PubDate: Sun, 03 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxy017
      Issue No: Vol. 20, No. 4 (2018)
       
  • Latent variable modeling for the microbiome
    • Authors: Sankaran K; Holmes S.
      Pages: 599 - 614
      Abstract: SummaryThe human microbiome is a complex ecological system, and describing its structure and function under different environmental conditions is important from both basic scientific and medical perspectives. Viewed through a biostatistical lens, many microbiome analysis goals can be formulated as latent variable modeling problems. However, although probabilistic latent variable models are a cornerstone of modern unsupervised learning, they are rarely applied in the context of microbiome data analysis, in spite of the evolutionary, temporal, and count structure that could be directly incorporated through such models. We explore the application of probabilistic latent variable models to microbiome data, with a focus on Latent Dirichlet allocation, Non-negative matrix factorization, and Dynamic Unigram models. To develop guidelines for when different methods are appropriate, we perform a simulation study. We further illustrate and compare these techniques using the data of Dethlefsen and Relman (2011, Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proceedings of the National Academy of Sciences108, 4554–4561), a study on the effects of antibiotics on bacterial community composition. Code and data for all simulations and case studies are available publicly.
      PubDate: Sun, 03 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxy018
      Issue No: Vol. 20, No. 4 (2018)
       
  • PERFect: PERmutation Filtering test for microbiome data
    • Authors: Smirnova E; Huzurbazar S, Jafari F.
      Pages: 615 - 631
      Abstract: SummaryThe human microbiota composition is associated with a number of diseases including obesity, inflammatory bowel disease, and bacterial vaginosis. Thus, microbiome research has the potential to reshape clinical and therapeutic approaches. However, raw microbiome count data require careful pre-processing steps that take into account both the sparsity of counts and the large number of taxa that are being measured. Filtering is defined as removing taxa that are present in a small number of samples and have small counts in the samples where they are observed. Despite progress in the number and quality of filtering approaches, there is no consensus on filtering standards and quality assessment. This can adversely affect downstream analyses and reproducibility of results across platforms and software. We introduce PERFect, a novel permutation filtering approach designed to address two unsolved problems in microbiome data processing: (i) define and quantify loss due to filtering by implementing thresholds and (ii) introduce and evaluate a permutation test for filtering loss to provide a measure of excessive filtering. Methods are assessed on three “mock experiment” data sets, where the true taxa compositions are known, and are applied to two publicly available real microbiome data sets. The method correctly removes contaminant taxa in “mock” data sets, quantifies and visualizes the corresponding filtering loss, providing a uniform data-driven filtering criteria for real microbiome data sets. In real data analyses PERFect tends to remove more taxa than existing approaches; this likely happens because the method is based on an explicit loss function, uses statistically principled testing, and takes into account correlation between taxa. The PERFect software is freely available at https://github.com/katiasmirn/PERFect.
      PubDate: Mon, 18 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxy020
      Issue No: Vol. 20, No. 4 (2018)
       
  • PEPA test: fast and powerful differential analysis from relative
           quantitative proteomics data using shared peptides
    • Authors: Jacob L; Combes F, Burger T.
      Pages: 632 - 647
      Abstract: SummaryWe propose a new hypothesis test for the differential abundance of proteins in mass-spectrometry based relative quantification. An important feature of this type of high-throughput analyses is that it involves an enzymatic digestion of the sample proteins into peptides prior to identification and quantification. Due to numerous homology sequences, different proteins can lead to peptides with identical amino acid chains, so that their parent protein is ambiguous. These so-called shared peptides make the protein-level statistical analysis a challenge and are often not accounted for. In this article, we use a linear model describing peptide–protein relationships to build a likelihood ratio test of differential abundance for proteins. We show that the likelihood ratio statistic can be computed in linear time with the number of peptides. We also provide the asymptotic null distribution of a regularized version of our statistic. Experiments on both real and simulated datasets show that our procedures outperforms state-of-the-art methods. The procedures are available via the pepa.test function of the DAPAR Bioconductor R package.
      PubDate: Mon, 18 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxy021
      Issue No: Vol. 20, No. 4 (2018)
       
  • Using multivariate mixed-effects selection models for analyzing
           batch-processed proteomics data with non-ignorable missingness
    • Authors: Wang J; Wang P, Hedeker D, et al.
      Pages: 648 - 665
      Abstract: SummaryIn quantitative proteomics, mass tag labeling techniques have been widely adopted in mass spectrometry experiments. These techniques allow peptides (short amino acid sequences) and proteins from multiple samples of a batch being detected and quantified in a single experiment, and as such greatly improve the efficiency of protein profiling. However, the batch-processing of samples also results in severe batch effects and non-ignorable missing data occurring at the batch level. Motivated by the breast cancer proteomic data from the Clinical Proteomic Tumor Analysis Consortium, in this work, we developed two tailored multivariate MIxed-effects SElection models (mvMISE) to jointly analyze multiple correlated peptides/proteins in labeled proteomics data, considering the batch effects and the non-ignorable missingness. By taking a multivariate approach, we can borrow information across multiple peptides of the same protein or multiple proteins from the same biological pathway, and thus achieve better statistical efficiency and biological interpretation. These two different models account for different correlation structures among a group of peptides or proteins. Specifically, to model multiple peptides from the same protein, we employed a factor-analytic random effects structure to characterize the high and similar correlations among peptides. To model biological dependence among multiple proteins in a functional pathway, we introduced a graphical lasso penalty on the error precision matrix, and implemented an efficient algorithm based on the alternating direction method of multipliers. Simulations demonstrated the advantages of the proposed models. Applying the proposed methods to the motivating data set, we identified phosphoproteins and biological pathways that showed different activity patterns in triple negative breast tumors versus other breast tumors. The proposed methods can also be applied to other high-dimensional multivariate analyses based on clustered data with or without non-ignorable missingness.
      PubDate: Sun, 24 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxy022
      Issue No: Vol. 20, No. 4 (2018)
       
  • Temporally dependent accelerated failure time model for capturing the
           impact of events that alter survival in disease mapping
    • Authors: Carroll R; Lawson A, Zhao S.
      Pages: 666 - 680
      Abstract: SummaryThe introduction of spatial and temporal frailty parameters in survival models furnishes a way to represent unmeasured confounding in the outcome of interest. Using a Bayesian accelerated failure time model, we are able to flexibly explore a wide range of spatial and temporal options for structuring frailties as well as examine the benefits of using these different structures in certain settings. A setting of particular interest for this work involved using temporal frailties to capture the impact of events of interest on breast cancer survival. Our results suggest that it is important to include these temporal frailties when there is a true temporal structure to the outcome and including them when a true temporal structure is absent does not sacrifice model fit. Additionally, the frailties are able to correctly recover the truth imposed on simulated data without affecting the fixed effect estimates. In the case study involving Louisiana breast cancer-specific mortality, the temporal frailty played an important role in representing the unmeasured confounding related to improvements in knowledge, education, and disease screenings as well as the impacts of Hurricane Katrina and the passing of the Affordable Care Act. In conclusion, the incorporation of temporal, in addition to spatial, frailties in survival analysis can lead to better fitting models and improved inference by representing both spatially and temporally varying unmeasured risk factors and confounding that could impact survival. Specifically, we successfully estimated changes in survival around the time of events of interest.
      PubDate: Sun, 24 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxy023
      Issue No: Vol. 20, No. 4 (2018)
       
  • A Bayesian space–time model for clustering areal units based on
           their disease trends
    • Authors: Napier G; Lee D, Robertson C, et al.
      Pages: 681 - 697
      Abstract: SummaryPopulation-level disease risk across a set of non-overlapping areal units varies in space and time, and a large research literature has developed methodology for identifying clusters of areal units exhibiting elevated risks. However, almost no research has extended the clustering paradigm to identify groups of areal units exhibiting similar temporal disease trends. We present a novel Bayesian hierarchical mixture model for achieving this goal, with inference based on a Metropolis-coupled Markov chain Monte Carlo ((MC)$^3$) algorithm. The effectiveness of the (MC)$^3$ algorithm compared to a standard Markov chain Monte Carlo implementation is demonstrated in a simulation study, and the methodology is motivated by two important case studies in the United Kingdom. The first concerns the impact on measles susceptibility of the discredited paper linking the measles, mumps, and rubella vaccination to an increased risk of Autism and investigates whether all areas in the Scotland were equally affected. The second concerns respiratory hospitalizations and investigates over a 10 year period which parts of Glasgow have shown increased, decreased, and no change in risk.
      PubDate: Mon, 18 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxy024
      Issue No: Vol. 20, No. 4 (2018)
       
  • Zero-inflated generalized Dirichlet multinomial regression model for
           microbiome compositional data analysis
    • Authors: Tang Z; Chen G.
      Pages: 698 - 713
      Abstract: SummaryThere is heightened interest in using high-throughput sequencing technologies to quantify abundances of microbial taxa and linking the abundance to human diseases and traits. Proper modeling of multivariate taxon counts is essential to the power of detecting this association. Existing models are limited in handling excessive zero observations in taxon counts and in flexibly accommodating complex correlation structures and dispersion patterns among taxa. In this article, we develop a new probability distribution, zero-inflated generalized Dirichlet multinomial (ZIGDM), that overcomes these limitations in modeling multivariate taxon counts. Based on this distribution, we propose a ZIGDM regression model to link microbial abundances to covariates (e.g. disease status) and develop a fast expectation–maximization algorithm to efficiently estimate parameters in the model. The derived tests enable us to reveal rich patterns of variation in microbial compositions including differential mean and dispersion. The advantages of the proposed methods are demonstrated through simulation studies and an analysis of a gut microbiome dataset.
      PubDate: Sun, 24 Jun 2018 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxy025
      Issue No: Vol. 20, No. 4 (2018)
       
 
 
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