自引率: 11.2%
被引量: 26353
通过率: 暂无数据
审稿周期: 12
版面费用: 暂无数据
国人发稿量: 64
投稿须知/期刊简介:
The Journal will publish papers on practical applications of statistics and other quantitative methods to medicine and its applied sciences. It will embrace all aspects of the collection analysis presentation and interpretation of medical data. Specific areas will include clinical trials diagnostic studies quality control laboratory experiments epidemiology and health care research. The journal will emphasize the relevance of numerical techniques and will aim to communicate statistical and quantitative ideas in a medical context. Examples of applications of statistics to specific projects articles explaining new statistical methods and reviews of general topics will be published; papers containing extensive mathematical theory will be excluded. The main criteria for publication will be appropriateness of the statistical method to the particular medical problem and clarity of exposition. The ultimate goal of Statistics in Medicine is to enhance communication between statisticians clinicians and medical researchers with the common purpose of advancing knowledge and understanding of quantitative aspects of medicine. It is intended that both the readers and authors of the Journal include statisticians clinicians epidemiologists health researchers mathematicians and computer scientists interested in medicine.
期刊描述简介:
The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.
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A Precision Mixture Risk Model to Identify Adverse Drug Events in Subpopulations Using a Case-Crossover Design.
被引量：- 发表：1970
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Group Response-Adaptive Randomization With Delayed and Missing Responses.
被引量：- 发表：1970
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Asymptotic Confidence Interval, Sample Size Formulas and Comparison Test for the Agreement Intra-Class Correlation Coefficient in Inter-Rater Reliability Studies.
被引量：- 发表：1970
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Functional Principal Component Analysis as an Alternative to Mixed-Effect Models for Describing Sparse Repeated Measures in Presence of Missing Data.
Analyzing longitudinal data in health studies is challenging due to sparse and error-prone measurements, strong within-individual correlation, missing data and various trajectory shapes. While mixed-effect models (MM) effectively address these challenges, they remain parametric models and may incur computational costs. In contrast, functional principal component analysis (FPCA) is a non-parametric approach developed for regular and dense functional data that flexibly describes temporal trajectories at a potentially lower computational cost. This article presents an empirical simulation study evaluating the behavior of FPCA with sparse and error-prone repeated measures and its robustness under different missing data schemes in comparison with MM. The results show that FPCA is well-suited in the presence of missing at random data caused by dropout, except in scenarios involving most frequent and systematic dropout. Like MM, FPCA fails under missing not at random mechanism. The FPCA was applied to describe the trajectories of four cognitive functions before clinical dementia and contrast them with those of matched controls in a case-control study nested in a population-based aging cohort. The average cognitive declines of future dementia cases showed a sudden divergence from those of their matched controls with a sharp acceleration 5 to 2.5 years prior to diagnosis.
被引量：- 发表：1970
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High-Dimensional Overdispersed Generalized Factor Model With Application to Single-Cell Sequencing Data Analysis.
The current high-dimensional linear factor models fail to account for the different types of variables, while high-dimensional nonlinear factor models often overlook the overdispersion present in mixed-type data. However, overdispersion is prevalent in practical applications, particularly in fields like biomedical and genomics studies. To address this practical demand, we propose an overdispersed generalized factor model (OverGFM) for performing high-dimensional nonlinear factor analysis on overdispersed mixed-type data. Our approach incorporates an additional error term to capture the overdispersion that cannot be accounted for by factors alone. However, this introduces significant computational challenges due to the involvement of two high-dimensional latent random matrices in the nonlinear model. To overcome these challenges, we propose a novel variational EM algorithm that integrates Laplace and Taylor approximations. This algorithm provides iterative explicit solutions for the complex variational parameters and is proven to possess excellent convergence properties. We also develop a criterion based on the singular value ratio to determine the optimal number of factors. Numerical results demonstrate the effectiveness of this criterion. Through comprehensive simulation studies, we show that OverGFM outperforms state-of-the-art methods in terms of estimation accuracy and computational efficiency. Furthermore, we demonstrate the practical merit of our method through its application to two datasets from genomics. To facilitate its usage, we have integrated the implementation of OverGFM into the R package GFM.
被引量：- 发表：1970
