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Publisher: Oxford University Press   (Total: 396 journals)

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Showing 1 - 200 of 396 Journals sorted alphabetically
ACS Symposium Series     Full-text available via subscription   (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: 50, SJR: 2.196, CiteScore: 5)
Aesthetic Surgery J.     Hybrid Journal   (Followers: 6, SJR: 1.434, CiteScore: 1)
African Affairs     Hybrid Journal   (Followers: 65, SJR: 1.869, CiteScore: 2)
Age and Ageing     Hybrid Journal   (Followers: 89, SJR: 1.989, CiteScore: 4)
Alcohol and Alcoholism     Hybrid Journal   (Followers: 18, SJR: 1.376, CiteScore: 3)
American Entomologist     Full-text available via subscription   (Followers: 7)
American Historical Review     Hybrid Journal   (Followers: 156, SJR: 0.467, CiteScore: 1)
American J. of Agricultural Economics     Hybrid Journal   (Followers: 42, SJR: 2.113, CiteScore: 3)
American J. of Clinical Nutrition     Hybrid Journal   (Followers: 154, SJR: 3.438, CiteScore: 6)
American J. of Epidemiology     Hybrid Journal   (Followers: 178, SJR: 2.713, CiteScore: 3)
American J. of Hypertension     Hybrid Journal   (Followers: 25, 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: 8, SJR: 0.116, CiteScore: 0)
American Law and Economics Review     Hybrid Journal   (Followers: 27, SJR: 1.053, CiteScore: 1)
American Literary History     Hybrid Journal   (Followers: 16, SJR: 0.391, CiteScore: 0)
Analysis     Hybrid Journal   (Followers: 22, 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: 36, SJR: 1.721, CiteScore: 4)
Annals of Oncology     Hybrid Journal   (Followers: 45, SJR: 5.599, CiteScore: 9)
Annals of the Entomological Society of America     Full-text available via subscription   (Followers: 10, SJR: 0.722, CiteScore: 1)
Annals of Work Exposures and Health     Hybrid Journal   (Followers: 32, SJR: 0.728, CiteScore: 2)
AoB Plants     Open Access   (Followers: 4, SJR: 1.28, CiteScore: 3)
Applied Economic Perspectives and Policy     Hybrid Journal   (Followers: 18, SJR: 0.858, CiteScore: 2)
Applied Linguistics     Hybrid Journal   (Followers: 56, 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: 20)
Arbitration Law Reports and Review     Hybrid Journal   (Followers: 14)
Archives of Clinical Neuropsychology     Hybrid Journal   (Followers: 30, SJR: 0.731, CiteScore: 2)
Aristotelian Society Supplementary Volume     Hybrid Journal   (Followers: 3)
Arthropod Management Tests     Hybrid Journal   (Followers: 2)
Astronomy & Geophysics     Hybrid Journal   (Followers: 43, SJR: 0.146, CiteScore: 0)
Behavioral Ecology     Hybrid Journal   (Followers: 52, SJR: 1.871, CiteScore: 3)
Bioinformatics     Hybrid Journal   (Followers: 308, SJR: 6.14, CiteScore: 8)
Biology Methods and Protocols     Hybrid Journal  
Biology of Reproduction     Full-text available via subscription   (Followers: 9, SJR: 1.446, CiteScore: 3)
Biometrika     Hybrid Journal   (Followers: 20, SJR: 3.485, CiteScore: 2)
BioScience     Hybrid Journal   (Followers: 29, SJR: 2.754, CiteScore: 4)
Bioscience Horizons : The National Undergraduate Research J.     Open Access   (Followers: 1, SJR: 0.146, CiteScore: 0)
Biostatistics     Hybrid Journal   (Followers: 17, SJR: 1.553, CiteScore: 2)
BJA : British J. of Anaesthesia     Hybrid Journal   (Followers: 167, SJR: 2.115, CiteScore: 3)
BJA Education     Hybrid Journal   (Followers: 64)
Brain     Hybrid Journal   (Followers: 68, SJR: 5.858, CiteScore: 7)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 49, 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: 35, SJR: 2.161, CiteScore: 2)
British J. of Aesthetics     Hybrid Journal   (Followers: 25, SJR: 0.508, CiteScore: 1)
British J. of Criminology     Hybrid Journal   (Followers: 587, SJR: 1.828, CiteScore: 3)
British J. of Social Work     Hybrid Journal   (Followers: 87, 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: 32)
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 4, SJR: 1.376, CiteScore: 1)
Cambridge J. of Economics     Hybrid Journal   (Followers: 64, SJR: 0.764, CiteScore: 2)
Cambridge J. of Regions, Economy and Society     Hybrid Journal   (Followers: 11, SJR: 2.438, CiteScore: 4)
Cambridge Quarterly     Hybrid Journal   (Followers: 9, 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: 14, SJR: 3.002, CiteScore: 5)
Cerebral Cortex     Hybrid Journal   (Followers: 45, SJR: 3.892, CiteScore: 6)
CESifo Economic Studies     Hybrid Journal   (Followers: 17, SJR: 0.483, CiteScore: 1)
Chemical Senses     Hybrid Journal   (Followers: 1, SJR: 1.42, CiteScore: 3)
Children and Schools     Hybrid Journal   (Followers: 5, SJR: 0.246, CiteScore: 0)
Chinese J. of Comparative Law     Hybrid Journal   (Followers: 4, SJR: 0.412, CiteScore: 0)
Chinese J. of Intl. Law     Hybrid Journal   (Followers: 23, SJR: 0.329, CiteScore: 0)
Chinese J. of Intl. Politics     Hybrid Journal   (Followers: 10, 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: 27, SJR: 0.123, CiteScore: 0)
Clean Energy     Open Access   (Followers: 1)
Clinical Infectious Diseases     Hybrid Journal   (Followers: 65, SJR: 5.051, CiteScore: 5)
Clinical Kidney J.     Open Access   (Followers: 3, SJR: 1.163, CiteScore: 2)
Communication Theory     Hybrid Journal   (Followers: 23, SJR: 2.424, CiteScore: 3)
Communication, Culture & Critique     Hybrid Journal   (Followers: 27, SJR: 0.222, CiteScore: 1)
Community Development J.     Hybrid Journal   (Followers: 27, SJR: 0.268, CiteScore: 1)
Computer J.     Hybrid Journal   (Followers: 9, SJR: 0.319, CiteScore: 1)
Conservation Physiology     Open Access   (Followers: 2, SJR: 1.818, CiteScore: 3)
Contemporary Women's Writing     Hybrid Journal   (Followers: 9, SJR: 0.121, CiteScore: 0)
Contributions to Political Economy     Hybrid Journal   (Followers: 5, SJR: 0.906, CiteScore: 1)
Critical Values     Full-text available via subscription  
Current Developments in Nutrition     Open Access   (Followers: 2)
Current Legal Problems     Hybrid Journal   (Followers: 29)
Current Zoology     Full-text available via subscription   (Followers: 2, SJR: 1.164, CiteScore: 2)
Database : The J. of Biological Databases and Curation     Open Access   (Followers: 8, SJR: 1.791, CiteScore: 3)
Digital Scholarship in the Humanities     Hybrid Journal   (Followers: 14, SJR: 0.259, CiteScore: 1)
Diplomatic History     Hybrid Journal   (Followers: 20, 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: 16, SJR: 0.139, CiteScore: 0)
Economic Policy     Hybrid Journal   (Followers: 42, SJR: 3.584, CiteScore: 3)
ELT J.     Hybrid Journal   (Followers: 24, SJR: 0.942, CiteScore: 1)
English Historical Review     Hybrid Journal   (Followers: 54, SJR: 0.612, CiteScore: 1)
English: J. of the English Association     Hybrid Journal   (Followers: 14, SJR: 0.1, CiteScore: 0)
Environmental Entomology     Full-text available via subscription   (Followers: 11, SJR: 0.818, CiteScore: 2)
Environmental Epigenetics     Open Access   (Followers: 3)
Environmental History     Hybrid Journal   (Followers: 27, SJR: 0.408, CiteScore: 1)
EP-Europace     Hybrid Journal   (Followers: 2, 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: 17, SJR: 0.113, CiteScore: 0)
European Heart J.     Hybrid Journal   (Followers: 57, SJR: 9.315, CiteScore: 9)
European Heart J. - Cardiovascular Imaging     Hybrid Journal   (Followers: 9, SJR: 3.625, CiteScore: 3)
European Heart J. - Cardiovascular Pharmacotherapy     Full-text available via subscription   (Followers: 1)
European Heart J. - Quality of Care and Clinical Outcomes     Hybrid Journal  
European Heart J. : Case Reports     Open Access  
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: 190, SJR: 0.694, CiteScore: 1)
European J. of Orthodontics     Hybrid Journal   (Followers: 4, SJR: 1.279, CiteScore: 2)
European J. of Public Health     Hybrid Journal   (Followers: 20, 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: 42, SJR: 2.728, CiteScore: 3)
Evolution, Medicine, and Public Health     Open Access   (Followers: 11)
Family Practice     Hybrid Journal   (Followers: 16, SJR: 1.018, CiteScore: 2)
Fems Microbiology Ecology     Hybrid Journal   (Followers: 12, SJR: 1.492, CiteScore: 4)
Fems Microbiology Letters     Hybrid Journal   (Followers: 26, SJR: 0.79, CiteScore: 2)
Fems Microbiology Reviews     Hybrid Journal   (Followers: 30, 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: 24, SJR: 1.425, CiteScore: 1)
Forest Science     Hybrid Journal   (Followers: 7, 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: 33, SJR: 0.118, CiteScore: 0)
French Studies     Hybrid Journal   (Followers: 20, SJR: 0.148, CiteScore: 0)
French Studies Bulletin     Hybrid Journal   (Followers: 10, SJR: 0.152, CiteScore: 0)
Gastroenterology Report     Open Access   (Followers: 2)
Genome Biology and Evolution     Open Access   (Followers: 13, SJR: 2.578, CiteScore: 4)
Geophysical J. Intl.     Hybrid Journal   (Followers: 35, SJR: 1.506, CiteScore: 3)
German History     Hybrid Journal   (Followers: 23, SJR: 0.161, CiteScore: 0)
GigaScience     Open Access   (Followers: 4, SJR: 5.022, CiteScore: 7)
Global Summitry     Hybrid Journal   (Followers: 1)
Glycobiology     Hybrid Journal   (Followers: 13, SJR: 1.493, CiteScore: 3)
Health and Social Work     Hybrid Journal   (Followers: 56, SJR: 0.388, CiteScore: 1)
Health Education Research     Hybrid Journal   (Followers: 15, SJR: 0.854, CiteScore: 2)
Health Policy and Planning     Hybrid Journal   (Followers: 25, SJR: 1.512, CiteScore: 2)
Health Promotion Intl.     Hybrid Journal   (Followers: 22, SJR: 0.812, CiteScore: 2)
History Workshop J.     Hybrid Journal   (Followers: 31, SJR: 1.278, CiteScore: 1)
Holocaust and Genocide Studies     Hybrid Journal   (Followers: 28, SJR: 0.105, CiteScore: 0)
Human Communication Research     Hybrid Journal   (Followers: 14, SJR: 2.146, CiteScore: 3)
Human Molecular Genetics     Hybrid Journal   (Followers: 8, SJR: 3.555, CiteScore: 5)
Human Reproduction     Hybrid Journal   (Followers: 69, SJR: 2.643, CiteScore: 5)
Human Reproduction Open     Open Access  
Human Reproduction Update     Hybrid Journal   (Followers: 18, SJR: 5.317, CiteScore: 10)
Human Rights Law Review     Hybrid Journal   (Followers: 58, SJR: 0.756, CiteScore: 1)
ICES J. of Marine Science: J. du Conseil     Hybrid Journal   (Followers: 53, SJR: 1.591, CiteScore: 3)
ICSID Review     Hybrid Journal   (Followers: 11)
ILAR J.     Hybrid Journal   (Followers: 2, 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: 36, SJR: 0.249, CiteScore: 1)
Inflammatory Bowel Diseases     Hybrid Journal   (Followers: 43, SJR: 2.511, CiteScore: 4)
Information and Inference     Free  
Integrative and Comparative Biology     Hybrid Journal   (Followers: 8, SJR: 1.319, CiteScore: 2)
Interacting with Computers     Hybrid Journal   (Followers: 11, SJR: 0.292, CiteScore: 1)
Interactive CardioVascular and Thoracic Surgery     Hybrid Journal   (Followers: 7, SJR: 0.762, CiteScore: 1)
Intl. Affairs     Hybrid Journal   (Followers: 62, SJR: 1.505, CiteScore: 3)
Intl. Data Privacy Law     Hybrid Journal   (Followers: 25)
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: 36, SJR: 1.348, CiteScore: 2)
Intl. J. of Constitutional Law     Hybrid Journal   (Followers: 64, SJR: 0.601, CiteScore: 1)
Intl. J. of Epidemiology     Hybrid Journal   (Followers: 238, 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: 24, SJR: 0.223, CiteScore: 1)
Intl. J. of Lexicography     Hybrid Journal   (Followers: 9, 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: 38, SJR: 0.389, CiteScore: 1)
Intl. J. of Transitional Justice     Hybrid Journal   (Followers: 11, SJR: 0.724, CiteScore: 2)
Intl. Mathematics Research Notices     Hybrid Journal   (Followers: 1, SJR: 2.168, CiteScore: 1)
Intl. Political Sociology     Hybrid Journal   (Followers: 39, SJR: 1.465, CiteScore: 3)
Intl. Relations of the Asia-Pacific     Hybrid Journal   (Followers: 23, SJR: 0.401, CiteScore: 1)
Intl. Studies Perspectives     Hybrid Journal   (Followers: 9, SJR: 0.983, CiteScore: 1)
Intl. Studies Quarterly     Hybrid Journal   (Followers: 47, 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: 17, SJR: 0.533, CiteScore: 1)
J. of American History     Hybrid Journal   (Followers: 46, 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: 41, SJR: 1.226, CiteScore: 2)
J. of Burn Care & Research     Hybrid Journal   (Followers: 9, SJR: 0.768, CiteScore: 2)
J. of Chromatographic Science     Hybrid Journal   (Followers: 18, SJR: 0.36, CiteScore: 1)
J. of Church and State     Hybrid Journal   (Followers: 11, SJR: 0.139, CiteScore: 0)
J. of Communication     Hybrid Journal   (Followers: 54, SJR: 4.411, CiteScore: 5)
J. of Competition Law and Economics     Hybrid Journal   (Followers: 35, SJR: 0.33, CiteScore: 0)
J. of Complex Networks     Hybrid Journal   (Followers: 2, SJR: 1.05, CiteScore: 4)
J. of Computer-Mediated Communication     Open Access   (Followers: 29, SJR: 2.961, CiteScore: 6)
J. of Conflict and Security Law     Hybrid Journal   (Followers: 12, SJR: 0.402, CiteScore: 0)
J. of Consumer Research     Full-text available via subscription   (Followers: 46, SJR: 5.856, CiteScore: 5)

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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  [396 journals]
  • Erratum: Methods for handling longitudinal outcome processes truncated by
           dropout and death
    • Authors: Wen L; Terrera G, Seaman S.
      Pages: 594 - 594
      Abstract: Biostatistics (2018) 19, 4, pp. 407–425
      PubDate: Thu, 15 Feb 2018 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxy001
      Issue No: Vol. 19, No. 4 (2018)
       
  • Methods for handling longitudinal outcome processes truncated by dropout
           and death
    • Authors: Wen L; Terrera G, Seaman S.
      Pages: 407 - 425
      Abstract: SUMMARYCohort data are often incomplete because some subjects drop out of the study, and inverse probability weighting (IPW), multiple imputation (MI), and linear increments (LI) are methods that deal with such missing data. In cohort studies of ageing, missing data can arise from dropout or death. Methods that do not distinguish between these reasons for missingness typically provide inference about a hypothetical cohort where no one can die (immortal cohort). It has been suggested that inference about the cohort composed of those who are still alive at any time point (partly conditional inference) may be more meaningful. MI, LI, and IPW can all be adapted to provide partly conditional inference. In this article, we clarify and compare the assumptions required by these MI, LI, and IPW methods for partly conditional inference on continuous outcomes. We also propose augmented IPW estimators for making partly conditional inference. These are more efficient than IPW estimators and more robust to model misspecification. Our simulation studies show that the methods give approximately unbiased estimates of partly conditional estimands when their assumptions are met, but may be biased otherwise. We illustrate the application of the missing data methods using data from the ‘Origins of Variance in the Old–old’ Twin study.
      PubDate: Tue, 26 Sep 2017 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxx045
      Issue No: Vol. 19, No. 4 (2017)
       
  • Survivor bias in Mendelian randomization analysis
    • Authors: Vansteelandt S; Dukes O, Martinussen T.
      Pages: 426 - 443
      Abstract: SUMMARYMendelian randomization studies employ genotypes as experimental handles to infer the effect of genetically modified exposures (e.g. vitamin D exposure) on disease outcomes (e.g. mortality). The statistical analysis of these studies makes use of the standard instrumental variables framework. Many of these studies focus on elderly populations, thereby ignoring the problem of left truncation, which arises due to the selection of study participants being conditional upon surviving up to the time of study onset. Such selection, in general, invalidates the assumptions on which the instrumental variables analysis rests. We show that Mendelian randomization studies of adult or elderly populations will therefore, in general, return biased estimates of the exposure effect when the considered genotype affects mortality; in contrast, standard tests of the causal null hypothesis that the exposure does not affect the mortality rate remain unbiased, even when they ignore this problem of left truncation. To eliminate “survivor bias” or “truncation bias” from the effect of exposure on mortality, we next propose various simple strategies under a semi-parametric additive hazard model. We examine the performance of the proposed methods in simulation studies and use them to infer the effect of vitamin D on all-cause mortality based on the Monica10 study with the genetic variant filaggrin as instrumental variable.
      PubDate: Wed, 27 Sep 2017 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxx050
      Issue No: Vol. 19, No. 4 (2017)
       
  • A Bayesian procedure for estimating the causal effects of nursing home
           bed-hold policy
    • Authors: Gutman R; Intrator O, Lancaster T.
      Pages: 444 - 460
      Abstract: SUMMARYNursing home bed-hold policies provide continuity of care for Medicaid beneficiaries by paying nursing homes to reserve beds so residents can return to their facility of occupancy following an acute hospitalization. In 2001, Michigan implemented bed-hold policies in nursing homes. We investigated the impact of these policies on mortality and hospitalizations using 1999–2004 quarterly data from nursing homes in Michigan and nursing homes in 11 states that did not implement such policies. Synthetic Control has been used to estimate the effects of policies by accounting for changes over time unrelated to the intervention. Synthetic Control is intended for scalar continuous outcome at each period, and assumes a single treated unit and multiple control units. We propose a Bayesian procedure to overcome these limitations. It imputes the outcomes of nursing homes in Michigan if they were not exposed to the policy by matching to non-exposed nursing homes that are associated with the exposed ones in the pre-policy period. Because sampling from a Bayesian model is computationally challenging, we describe an approximation procedure that can be implemented using existing software. Our approach can be applied to other studies that examine the impact of policies.
      PubDate: Tue, 03 Oct 2017 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxx049
      Issue No: Vol. 19, No. 4 (2017)
       
  • Robust distributed lag models using data adaptive shrinkage
    • Authors: Chen Y; Mukherjee B, Adar S, et al.
      Pages: 461 - 478
      Abstract: SUMMARYDistributed lag models (DLMs) have been widely used in environmental epidemiology to quantify the lagged effects of air pollution on an outcome of interest such as mortality or cardiovascular events. Generally speaking, DLMs can be applied to time-series data where the current measure of an independent variable and its lagged measures collectively affect the current measure of a dependent variable. The corresponding distributed lag (DL) function represents the relationship between the lags and the coefficients of the lagged exposure variables. Common choices include polynomials and splines. On one hand, such a constrained DLM specifies the coefficients as a function of lags and reduces the number of parameters to be estimated; hence, higher efficiency can be achieved. On the other hand, under violation of the assumption about the DL function, effect estimates can be severely biased. In this article, we propose a general framework for shrinking coefficient estimates from an unconstrained DLM, that are unbiased but potentially inefficient, toward the coefficient estimates from a constrained DLM to achieve a bias-variance trade-off. The amount of shrinkage can be determined in various ways, and we explore several such methods: empirical Bayes-type shrinkage, a hierarchical Bayes approach, and generalized ridge regression. We also consider a two-stage shrinkage approach that enforces the effect estimates to approach zero as lags increase. We contrast the various methods via an extensive simulation study and show that the shrinkage methods have better average performance across different scenarios in terms of mean squared error (MSE).We illustrate the methods by using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) to explore the association between PM$_{10}$, O$_3$, and SO$_2$ on three types of disease event counts in Chicago, IL, from 1987 to 2000.
      PubDate: Thu, 12 Oct 2017 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxx041
      Issue No: Vol. 19, No. 4 (2017)
       
  • Survival analysis with time-dependent covariates subject to missing data
           or measurement error: Multiple Imputation for Joint Modeling (MIJM)
    • Authors: Moreno-Betancur M; Carlin J, Brilleman S, et al.
      Pages: 479 - 496
      Abstract: SUMMARYModern epidemiological studies collect data on time-varying individual-specific characteristics, such as body mass index and blood pressure. Incorporation of such time-dependent covariates in time-to-event models is of great interest, but raises some challenges. Of specific concern are measurement error, and the non-synchronous updating of covariates across individuals, due for example to missing data. It is well known that in the presence of either of these issues the last observation carried forward (LOCF) approach traditionally used leads to bias. Joint models of longitudinal and time-to-event outcomes, developed recently, address these complexities by specifying a model for the joint distribution of all processes and are commonly fitted by maximum likelihood or Bayesian approaches. However, the adequate specification of the full joint distribution can be a challenging modeling task, especially with multiple longitudinal markers. In fact, most available software packages are unable to handle more than one marker and offer a restricted choice of survival models. We propose a two-stage approach, Multiple Imputation for Joint Modeling (MIJM), to incorporate multiple time-dependent continuous covariates in the semi-parametric Cox and additive hazard models. Assuming a primary focus on the time-to-event model, the MIJM approach handles the joint distribution of the markers using multiple imputation by chained equations, a computationally convenient procedure that is widely available in mainstream statistical software. We developed an R package “survtd” that allows MIJM and other approaches in this manuscript to be applied easily, with just one call to its main function. A simulation study showed that MIJM performs well across a wide range of scenarios in terms of bias and coverage probability, particularly compared with LOCF, simpler two-stage approaches, and a Bayesian joint model. The Framingham Heart Study is used to illustrate the approach.
      PubDate: Thu, 12 Oct 2017 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxx046
      Issue No: Vol. 19, No. 4 (2017)
       
  • Faster family-wise error control for neuroimaging with a parametric
           bootstrap
    • Authors: Vandekar S; Satterthwaite T, Rosen A, et al.
      Pages: 497 - 513
      Abstract: SUMMARYIn neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image. Recent studies have shown that the most common family-wise error (FWE) controlling procedures in imaging, which rely on classical mathematical inequalities or Gaussian random field theory, yield FWE rates (FWER) that are far from the nominal level. Depending on the approach used, the FWER can be exceedingly small or grossly inflated. Given the widespread use of neuroimaging as a tool for understanding neurological and psychiatric disorders, it is imperative that reliable multiple testing procedures are available. To our knowledge, only permutation joint testing procedures have been shown to reliably control the FWER at the nominal level. However, these procedures are computationally intensive due to the increasingly available large sample sizes and dimensionality of the images, and analyses can take days to complete. Here, we develop a parametric bootstrap joint testing procedure. The parametric bootstrap procedure works directly with the test statistics, which leads to much faster estimation of adjusted p-values than resampling-based procedures while reliably controlling the FWER in sample sizes available in many neuroimaging studies. We demonstrate that the procedure controls the FWER in finite samples using simulations, and present region- and voxel-wise analyses to test for sex differences in developmental trajectories of cerebral blood flow.
      PubDate: Fri, 20 Oct 2017 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxx051
      Issue No: Vol. 19, No. 4 (2017)
       
  • A classical regression framework for mediation analysis: fitting one model
           to estimate mediation effects
    • Authors: Saunders C; Blume J.
      Pages: 514 - 528
      Abstract: SUMMARYMediation analysis explores the degree to which an exposure’s effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron–Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches.
      PubDate: Thu, 26 Oct 2017 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxx054
      Issue No: Vol. 19, No. 4 (2017)
       
  • Marginal quantile regression for dependent data with a working odds-ratio
           matrix
    • Authors: Bossoli D; Bottai M.
      Pages: 529 - 545
      Abstract: SUMMARYDependent data arise frequently in applied research and several approaches to adjusting for the dependence among observations have been proposed in quantile regression. Cluster bootstrap is generally inefficient and computationally demanding, especially when the number of clusters is large. When the primary interest is on marginal quantiles, estimating equations have been proposed that estimate a working correlation matrix from the regression residuals’ sign. However, the Pearson’s correlation coefficient is an inadequate measure of dependence between binary variables because its range depends on their marginal probabilities. Instead, we propose to model the working correlation matrix through odds ratios. Different working structures can be easily estimated by suitable logistic regression models. These structures can be parametrized to depend on covariates and clusters. Simulations show that the proposed estimator has similar behavior to that of generalized estimating equations applied to regression for the mean. We study marginal quantiles of cognitive behavior with data from a randomized trial for treatment of obsessive compulsive disorder.
      PubDate: Thu, 02 Nov 2017 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxx052
      Issue No: Vol. 19, No. 4 (2017)
       
  • An empirical Bayes test for allelic-imbalance detection in ChIP-seq
    • Authors: Zhang Q; Keleş S.
      Pages: 546 - 561
      Abstract: SUMMARYChromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) has enabled discovery of genomic regions enriched with biological signals such as transcription factor binding and histone modifications. Allelic-imbalance (ALI) detection is a complementary analysis of ChIP-seq data for associating biological signals with single nucleotide polymorphisms (SNPs). It has been successfully used in elucidating functional roles of non-coding SNPs. Commonly used statistical approaches for ALI detection are often based on binomial testing and mixture models, both of which rely on strong assumptions on the distribution of the unobserved allelic probability, and have significant practical shortcomings. We propose Non-Parametric Binomial (NPBin) test for ALI detection and for modeling Binomial data in general. NPBin models the density of the unobserved allelic probability non-parametrically, and estimates its empirical null distribution via curve fitting. We demonstrate the advantages of NPBin in terms of interpretability of the estimated density and the accuracy in ALI detection using simulations and analysis of several ChIP-seq data sets. We also illustrate the generality of our modeling framework beyond ALI detection by an application to a baseball batting average prediction problem. This article has supplementary material available at Biostatistics online. The code and the sample input data have been also deposited to github https://github.com/QiZhangStat/ALIdetection.
      PubDate: Fri, 03 Nov 2017 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxx060
      Issue No: Vol. 19, No. 4 (2017)
       
  • Missing data and technical variability in single-cell RNA-sequencing
           experiments
    • Authors: Hicks S; Townes F, Teng M, et al.
      Pages: 562 - 578
      Abstract: SUMMARYUntil recently, high-throughput gene expression technology, such as RNA-Sequencing (RNA-seq) required hundreds of thousands of cells to produce reliable measurements. Recent technical advances permit genome-wide gene expression measurement at the single-cell level. Single-cell RNA-Seq (scRNA-seq) is the most widely used and numerous publications are based on data produced with this technology. However, RNA-seq and scRNA-seq data are markedly different. In particular, unlike RNA-seq, the majority of reported expression levels in scRNA-seq are zeros, which could be either biologically-driven, genes not expressing RNA at the time of measurement, or technically-driven, genes expressing RNA, but not at a sufficient level to be detected by sequencing technology. Another difference is that the proportion of genes reporting the expression level to be zero varies substantially across single cells compared to RNA-seq samples. However, it remains unclear to what extent this cell-to-cell variation is being driven by technical rather than biological variation. Furthermore, while systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies, these issues have received minimal attention in published studies based on scRNA-seq technology. Here, we use an assessment experiment to examine data from published studies and demonstrate that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we present evidence that some of these reported zeros are driven by technical variation by demonstrating that scRNA-seq produces more zeros than expected and that this bias is greater for lower expressed genes. In addition, this missing data problem is exacerbated by the fact that this technical variation varies cell-to-cell. Then, we show how this technical cell-to-cell variability can be confused with novel biological results. Finally, we demonstrate and discuss how batch-effects and confounded experiments can intensify the problem.
      PubDate: Mon, 06 Nov 2017 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxx053
      Issue No: Vol. 19, No. 4 (2017)
       
  • Prediction of individual outcomes for asthma sufferers
    • Authors: Curtis B; Megan E, Michael R, et al.
      Pages: 579 - 593
      Abstract: SUMMARYWe consider the problem of individual-specific medication level recommendation (initiation, removal, increase, or decrease) for asthma sufferers. Asthma is one of the most common chronic diseases in both adults and children, affecting 8% of the US population and costing $37–63 billion/year in the United States of America. Asthma is a complex disease, whose symptoms may wax and wane, making it difficult for clinicians to predict outcomes and prognosis. Improved ability to predict prognosis can inform decision making and may promote conversations between clinician and provider around optimizing medication therapy. Data from the US Medical Expenditure Panel Survey (MEPS) years 2000–2010 were used to fit a longitudinal model for a multivariate response of adverse events (Emergency Department or in-patient visits, excessive rescue inhaler use, and oral steroid use). To reduce bias in the estimation of medication effects, medication level was treated as a latent process which was restricted to be consistent with prescription refill data. This approach is demonstrated to be effective in the MEPS cohort via predictions on a validation hold out set and a synthetic data simulation study. This framework can be easily generalized to medication decisions for other conditions as well.
      PubDate: Mon, 06 Nov 2017 00:00:00 GMT
      DOI: 10.1093/biostatistics/kxx055
      Issue No: Vol. 19, No. 4 (2017)
       
 
 
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