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Chiou, Sy Han; Kang, Sangwook; Kim, Junghi; Yan, Jun
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6 Citations
The semiparametric accelerated failure time (AFT) model is not as widely used as the Cox relative risk model due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations for censored data provide promising tools to make the AFT models more attractive in practice. For multivariate AFT models, we propose a generalized estimating equations (GEE) approach, extending the GEE to censored data. The consistency of the regression coefficient estimator is robust to misspecification of working covariance, and the efficiency is higher when the working covariance structure is closer to the truth. The marginal error distributions and regression coefficients are allowed to be unique for each margin or partially shared across margins as needed. The initial estimator is a rankbased estimator with Gehan’s weight, but obtained from an induced smoothing approach with computational ease. The resulting estimator is consistent and asymptotically normal, with variance estimated through a multiplier resampling method. In a large scale simulation study, our estimator was up to three times as efficient as the estimateor that ignores the withincluster dependence, especially when the withincluster dependence was strong. The methods were applied to the bivariate failure times data from a diabetic retinopathy study.
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Vicari, Donatella
Analysis of linear relations between variables, given a third one, can be investigated for threeway threemode data, by defining new measures of linear dependence between occasions. In this paper, two partial correlation coefficients between matrices are proposed. Their properties are analyzed, in particular with respect to the absence of conditional linear dependence.
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RamosPollán, Raúl; GuevaraLópez, Miguel Ángel; Oliveira, Eugénio
6 Citations
This paper describes the BiomedTK software framework, created to perform massive explorations of machine learning classifiers configurations for biomedical data analysis over distributed Grid computing resources. BiomedTK integrates ROC analysis throughout the complete classifier construction process and enables explorations of large parameter sweeps for training third party classifiers such as artificial neural networks and support vector machines, offering the capability to harness the vast amount of computing power serviced by Grid infrastructures. In addition, it includes classifiers modified by the authors for ROC optimization and functionality to build ensemble classifiers and manipulate datasets (import/export, extract and transform data, etc.). BiomedTK was experimentally validated by training thousands of classifier configurations for representative biomedical UCI datasets reaching in little time classification levels comparable to those reported in existing literature. The comprehensive method herewith presented represents an improvement to biomedical data analysis in both methodology and potential reach of machine learning based experimentation.
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Galarza, Christian E.; Castro, Luis M. ; Louzada, Francisco; Lachos, Victor H.
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Longitudinal data are frequently analyzed using normal mixed effects models. Moreover, the traditional estimation methods are based on mean regression, which leads to nonrobust parameter estimation under nonnormal error distribution. However, at least in principle, quantile regression (QR) is more robust in the presence of outliers/influential observations and misspecification of the error distributions when compared to the conventional mean regression approach. In this context, this paper develops a likelihoodbased approach for estimating QR models with correlated continuous longitudinal data using the asymmetric Laplace distribution. Our approach relies on the stochastic approximation of the EM algorithm (SAEM algorithm), obtaining maximum likelihood estimates of the fixed effects and variance components in the case of nonlinear mixed effects (NLME) models. We evaluate the finite sample performance of the SAEM algorithm and asymptotic properties of the ML estimates through simulation experiments. Moreover, two real life datasets are used to illustrate our proposed method using the $$\texttt {qrNLMM}$$ package from $$\texttt {R}$$.
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Liu, JungTzu; Chen, ChiTian; Lan, K. K. Gordon; Tzeng, ChyngShyan; Hsiao, ChinFu; Tsou, HsiaoHui
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The traditionally uniform treatment effect assumption may be inappropriate in an multiregional clinical trial (MRCT) because of the impact on the drug effect due to regional differences. Lan and and Pinheiro (2012) proposed a discrete random effects model (DREM) to account the treatment effects heterogeneity among regions. However, the benefit of the overall drug effect and the consistency of the treatment effect in each region are two major issues in MRCTs. In this article, the power of benefit is derived under DREM and the overall sample size determination in an MRCT. Comparison of DREM and traditional continuous random effects model (CREM) is also illustrated here. In order to assess the treatment benefit and consistency simultaneously under DREM, we consider the concept of the Method 2 in “Basic Principles on Global Clinical Trials” guidance to construct the probability function of benefit and consistency. We also optimize the sample size allocation to reach maximum power for the benefit and consistency.
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Sun, Julian; Samorodnitsky, Gennady
Selecting the number of upper order statistics to use in extremal inference or selecting the threshold above which we perform the extremal inference is a common step in applications of extreme value theory. Not only is the selection itself difficult, but the large part of the sample below the threshold may potentially carry useful information. We propose an approach that takes an extremal parameter estimator and modifies it to allow for using multiple thresholds instead of a single one. We apply this approach to the problem of estimating the extremal index and demonstrate its power both on simulated and real data.
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Davis, Richard A.; Lii, KehShin; Politis, Dimitris N.
The object of this note is to point out and discuss a simple transformation^{2} of an absolutely continuous kvariate distribution F(xi, …, x_{k}) into the uniform distribution on the kdimensional hypercube. A discussion of related transformations has been given by P. Lévy [1].
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