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Statistics in Medicine

Wentao Lu, Xinlei Wang, Xiaowei Zhan, Adi Gazdar
In the field of gene set enrichment analysis (GSEA), meta-analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta-Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from component studies and (2) using enrichment scores whose distributions depend on the set sizes...
October 19, 2017: Statistics in Medicine
Haocheng Li, John Staudenmayer, Tianying Wang, Sarah Kozey Keadle, Raymond J Carroll
We take a functional data approach to longitudinal studies with complex bivariate outcomes. This work is motivated by data from a physical activity study that measured 2 responses over time in 5-minute intervals. One response is the proportion of time active in each interval, a continuous proportions with excess zeros and ones. The other response, energy expenditure rate in the interval, is a continuous variable with excess zeros and skewness. This outcome is complex because there are 3 possible activity patterns in each interval (inactive, partially active, and completely active), and those patterns, which are observed, induce both nonrandom and random associations between the responses...
October 19, 2017: Statistics in Medicine
Michael P Fay, Michael C Sachs, Kazutoyo Miura
The m:n:θb procedure is often used for validating an assay for precision, where m levels of an analyte are measured with n replicates at each level, and if all m estimates of coefficient of variation (CV) are less than θb , then the assay is declared validated for precision. The statistical properties of the procedure are unknown so there is no clear statistical statement of precision upon passing. Further, it is unclear how to modify the procedure for relative potency assays in which the constant standard deviation (SD) model fits much better than the traditional constant CV model...
October 19, 2017: Statistics in Medicine
Yihao Deng, N Rao Chaganty
Archimedean copulas are commonly used in a wide range of statistical models due to their simplicity, manageable analytical expressions, rich choices of generator functions, and other workable properties. However, the exchangeable dependence structure inherent to Archimedean copulas limits its application to familial data, where the dependence among family members is often different. When response variables are binary, modeling the familial associations becomes more challenging due to the stringent constraints imposed on the dependence parameters...
October 17, 2017: Statistics in Medicine
Mengyun Wu, Jian Huang, Shuangge Ma
Gene-gene (G×G) interactions have been shown to be critical for the fundamental mechanisms and development of complex diseases beyond main genetic effects. The commonly adopted marginal analysis is limited by considering only a small number of G factors at a time. With the "main effects, interactions" hierarchical constraint, many of the existing joint analysis methods suffer from prohibitively high computational cost. In this study, we propose a new method for identifying important G×G interactions under joint modeling...
October 16, 2017: Statistics in Medicine
Hongbin Zhang, Hubert Wong, Lang Wu
When modeling longitudinal data, the true values of time-varying covariates may be unknown because of detection-limit censoring or measurement error. A common approach in the literature is to empirically model the covariate process based on observed data and then predict the censored values or mismeasured values based on this empirical model. Such an empirical model can be misleading, especially for censored values since the (unobserved) censored values may behave very differently than observed values due to the underlying data-generation mechanisms or disease status...
October 16, 2017: Statistics in Medicine
Nicholas T Longford
Various forms of performance assessment are applied to public service institutions, such as hospitals, schools, police units, and local authorities. Difficulties arise in the interpretation of the results presented in some established formats because they require a good understanding and appreciation of the uncertainties involved. Usually the results have to be adapted to the perspectives of the users-managers of the assessed units, a consumer, or a central authority (a watchdog) that dispenses awards and sanctions...
October 16, 2017: Statistics in Medicine
Cen Wu, Yu Jiang, Jie Ren, Yuehua Cui, Shuangge Ma
Identification of gene-environment (G × E) interactions associated with disease phenotypes has posed a great challenge in high-throughput cancer studies. The existing marginal identification methods have suffered from not being able to accommodate the joint effects of a large number of genetic variants, while some of the joint-effect methods have been limited by failing to respect the "main effects, interactions" hierarchy, by ignoring data contamination, and by using inefficient selection techniques under complex structural sparsity...
October 16, 2017: Statistics in Medicine
Youngjoo Cho, Chen Hu, Debashis Ghosh
In many medical studies, estimation of the association between treatment and outcome of interest is often of primary scientific interest. Standard methods for its evaluation in survival analysis typically require the assumption of independent censoring. This assumption might be invalid in many medical studies, where the presence of dependent censoring leads to difficulties in analyzing covariate effects on disease outcomes. This data structure is called "semicompeting risks data," for which many authors have proposed an artificial censoring technique...
October 10, 2017: Statistics in Medicine
Paolo Giordani, Henk A L Kiers
In many situations, a researcher is interested in the analysis of the scores of a set of observation units on a set of variables. However, in medicine, it is very frequent that the information is replicated at different occasions. The occasions can be time-varying or refer to different conditions. In such cases, the data can be stored in a 3-way array or tensor. The Candecomp/Parafac and Tucker3 methods represent the most common methods for analyzing 3-way tensors. In this work, a review of these methods is provided, and then this class of methods is applied to a 3-way data set concerning hospital care data for a hospital in Rome (Italy) during 15 years distinguished in 3 groups of consecutive years (1892-1896, 1940-1944, 1968-1972)...
October 10, 2017: Statistics in Medicine
Satoshi Hattori, Xiao-Hua Zhou
Publication bias is one of the most important issues in meta-analysis. For standard meta-analyses to examine intervention effects, the funnel plot and the trim-and-fill method are simple and widely used techniques for assessing and adjusting for the influence of publication bias, respectively. However, their use may be subjective and can then produce misleading insights. To make a more objective inference for publication bias, various sensitivity analysis methods have been proposed, including the Copas selection model...
October 9, 2017: Statistics in Medicine
Sangbum Choi, Liang Zhu, Xuelin Huang
Modern medical treatments have substantially improved survival rates for many chronic diseases and have generated considerable interest in developing cure fraction models for survival data with a non-ignorable cured proportion. Statistical analysis of such data may be further complicated by competing risks that involve multiple types of endpoints. Regression analysis of competing risks is typically undertaken via a proportional hazards model adapted on cause-specific hazard or subdistribution hazard. In this article, we propose an alternative approach that treats competing events as distinct outcomes in a mixture...
October 6, 2017: Statistics in Medicine
Xianhong Xie, Xiaonan Xue, Howard D Strickler
Longitudinal measurement of biomarkers is important in determining risk factors for binary endpoints such as infection or disease. However, biomarkers are subject to measurement error, and some are also subject to left-censoring due to a lower limit of detection. Statistical methods to address these issues are few. We herein propose a generalized linear mixed model and estimate the model parameters using the Monte Carlo Newton-Raphson (MCNR) method. Inferences regarding the parameters are made by applying Louis's method and the delta method...
October 5, 2017: Statistics in Medicine
Frank Konietschke, Randolph R Aguayo, Wieland Staab
We study inference methods for the analysis of multireader diagnostic trials. In these studies, data are usually collected in terms of a factorial design involving the factors Modality and Reader. Furthermore, repeated measures appear in a natural way since the same patient is observed under different modalities by several readers and the repeated measures may have a quite involved dependency structure. The hypotheses are formulated in terms of the areas under the ROC curves. Currently, only global testing procedures exist for the analysis of such data...
October 5, 2017: Statistics in Medicine
Pavel Chernyavskiy, Mark P Little, Philip S Rosenberg
Age-period-cohort (APC) models are widely used to analyze population-level rates, particularly cancer incidence and mortality. These models are used for descriptive epidemiology, comparative risk analysis, and extrapolating future disease burden. Traditional APC models have 2 major limitations: (1) they lack parsimony because they require estimation of deviations from linear trends for each level of age, period, and cohort; and (2) rates observed at similar ages, periods, and cohorts are treated as independent, ignoring any correlations between them that may lead to biased parameter estimates and inefficient standard errors...
October 4, 2017: Statistics in Medicine
Annamaria Guolo
Control rate regression is a diffuse approach to account for heterogeneity among studies in meta-analysis by including information about the outcome risk of patients in the control condition. Correcting for the presence of measurement error affecting risk information in the treated and in the control group has been recognized as a necessary step to derive reliable inferential conclusions. Within this framework, the paper considers the problem of small sample size as an additional source of misleading inference about the slope of the control rate regression...
October 4, 2017: Statistics in Medicine
Sarwar Islam Mozumder, Mark Rutherford, Paul Lambert
In a competing risks analysis, interest lies in the cause-specific cumulative incidence function (CIF) that can be calculated by either (1) transforming on the cause-specific hazard or (2) through its direct relationship with the subdistribution hazard. We expand on current competing risks methodology from within the flexible parametric survival modelling framework (FPM) and focus on approach (2). This models all cause-specific CIFs simultaneously and is more useful when we look to questions on prognosis. We also extend cure models using a similar approach described by Andersson et al for flexible parametric relative survival models...
October 2, 2017: Statistics in Medicine
Anthony Atkinson, David Pedrosa
In an experiment including patients who underwent surgery for deep brain stimulation electrode placement, each patient responds to a set of 9 treatment combinations. There are 16 such sets, and the design problem is to choose which sets should be administered and in what proportions. Extensions to the methods of nonsequential optimum experimental design lead to identification of an unequally weighted optimum design involving 4 sets of treatment combinations. In the actual experiment, patients arrive sequentially and present with sets of prognostic factors...
September 28, 2017: Statistics in Medicine
Jessica M B Rees, Angela M Wood, Stephen Burgess
Methods have been developed for Mendelian randomization that can obtain consistent causal estimates while relaxing the instrumental variable assumptions. These include multivariable Mendelian randomization, in which a genetic variant may be associated with multiple risk factors so long as any association with the outcome is via the measured risk factors (measured pleiotropy), and the MR-Egger (Mendelian randomization-Egger) method, in which a genetic variant may be directly associated with the outcome not via the risk factor of interest, so long as the direct effects of the variants on the outcome are uncorrelated with their associations with the risk factor (unmeasured pleiotropy)...
September 27, 2017: Statistics in Medicine
Masaaki Doi, Fumihiro Takahashi, Yohei Kawasaki
Noninferiority trials have recently gained importance for the clinical trials of drugs and medical devices. In these trials, most statistical methods have been used from a frequentist perspective, and historical data have been used only for the specification of the noninferiority margin Δ>0. In contrast, Bayesian methods, which have been studied recently are advantageous in that they can use historical data to specify prior distributions and are expected to enable more efficient decision making than frequentist methods by borrowing information from historical trials...
September 27, 2017: Statistics in Medicine
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