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 Author:
 Williamson, S. Faye; Jacko, Peter; Villar, Sofía S.; Jaki, Thomas
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 136153
 ISSN:
 01679473
 Subject:
 Bayesian theory; clinical trials; dynamic programming; models; motivation; patients
 Abstract:
 ... Development of treatments for rare diseases is challenging due to the limited number of patients available for participation. Learning about treatment effectiveness with a view to treat patients in the larger outside population, as in the traditional fixed randomised design, may not be a plausible goal. An alternative goal is to treat the patients within the trial as effectively as possible. Using ...
 DOI:
 10.1016/j.csda.2016.09.006
 PubMed:
 28630525
 PubMed Central:
 PMC5473477

http://dx.doi.org/10.1016/j.csda.2016.09.006
 Author:
 Frumento, Paolo; Bottai, Matteo
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 5363
 ISSN:
 01679473
 Subject:
 birth weight; computer software; equations; men; regression analysis; variance covariance matrix
 Abstract:
 ... An estimation equation for censored, truncated quantile regression is introduced. The asymptotic covariance matrix has a relatively simple expression and can be estimated from the data. Simulation results are presented, and the described estimator is used to evaluate the effects of birth weight on percentiles of survival time after age 65 with a populationbased cohort of Swedish men. The proposed ...
 DOI:
 10.1016/j.csda.2016.08.015

http://dx.doi.org/10.1016/j.csda.2016.08.015
 Author:
 Masoudi, Ehsan; Holling, Heinz; Wong, Weng Kee
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 330345
 ISSN:
 01679473
 Subject:
 algorithms; nonlinear models
 Abstract:
 ... Finding optimal designs for nonlinear models is complicated because the design criterion depends on the model parameters. If a plausible region for these parameters is available, a minimax optimal design may be used to remove this dependency by minimizing the maximum inefficiency that may arise due to misspecification in the parameters. Minimax optimal designs are often analytically intractable an ...
 DOI:
 10.1016/j.csda.2016.06.014

http://dx.doi.org/10.1016/j.csda.2016.06.014
 Author:
 Kobilinsky, André; Monod, Hervé; Bailey, R.A.
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 311329
 ISSN:
 01679473
 Subject:
 algorithms; computer software
 Abstract:
 ... The R package planor enables the user to search for, and construct, factorial designs satisfying given conditions. The user specifies the factors and their numbers of levels, the factorial terms which are assumed to be nonzero, and the subset of those which are to be estimated. Both block and treatment factors can be allowed for, and they may have either fixed or random effects, as well as hierar ...
 DOI:
 10.1016/j.csda.2016.09.003

http://dx.doi.org/10.1016/j.csda.2016.09.003
 Author:
 Friedrich, Sarah; Konietschke, Frank; Pauly, Markus
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 3852
 ISSN:
 01679473
 Subject:
 social sciences; statistical inference
 Abstract:
 ... Repeated measures and split plot plans are often the preferred design of choice when planning experiments in life and social sciences. They are typically analyzed by meanbased methods from MANOVA or linear mixed models, requiring certain assumptions on the underlying parametric distribution. However, if count, ordinal or score data are present, these techniques show their limits since means are n ...
 DOI:
 10.1016/j.csda.2016.06.016

http://dx.doi.org/10.1016/j.csda.2016.06.016
 Author:
 Muff, Stefanie; Ott, Manuela; Braun, Julia; Held, Leonhard
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 177193
 ISSN:
 01679473
 Subject:
 Bayesian theory; Weibull statistics; cardiovascular diseases; case studies; cigarettes; computer software; models; mortality; regression analysis; systolic blood pressure; Switzerland
 Abstract:
 ... Measurement error (ME) in explanatory variables is a common problem in regression and survival analysis, as it may cause bias in the estimated parameters. It is shown how the integrated nested Laplace approximations (INLA) method can handle classical and Berkson ME in a single explanatory variable, illustrated for the case of a Weibull regression model. To this end, a twocomponent error model to ...
 DOI:
 10.1016/j.csda.2017.03.001

http://dx.doi.org/10.1016/j.csda.2017.03.001
 Author:
 SlavtchovaBojkova, Maroussia; Trayanov, Plamen; Dimitrov, Stoyan
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 111124
 ISSN:
 01679473
 Subject:
 algorithms; carcinogenesis; disease control; equations; longevity; medical treatment; models; mutants; mutation; neoplasms
 Abstract:
 ... The appearance of mutations in cancer development plays a crucial role in the disease control and its medical treatment. Motivated by the practical significance, it is of interest to model the event of occurrence of a mutant cell that will possibly lead to a path of indefinite survival. A twotype branching process model in continuous time is proposed for describing the relationship between the wa ...
 DOI:
 10.1016/j.csda.2016.12.013

http://dx.doi.org/10.1016/j.csda.2016.12.013
 Author:
 Kim, GiSoo; Paik, Myunghee Cho; Kim, Hongsoo
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 8899
 ISSN:
 01679473
 Subject:
 data collection; health care costs; health policy; observational studies; California
 Abstract:
 ... An estimator of the population average causal treatment effect is proposed for multilevel clustered data from observational studies when the treatment assignment mechanism is clusterspecific nonignorable. This is motivated from a health policy study to evaluate the cost associated with rehospitalization due to premature discharge. The proposed estimator utilizes clusterlevel calibration condit ...
 DOI:
 10.1016/j.csda.2016.10.002

http://dx.doi.org/10.1016/j.csda.2016.10.002
 Author:
 RodríguezDíaz, Juan M.
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 287296
 ISSN:
 01679473
 Subject:
 covariance; experimental design; models; variance
 Abstract:
 ... In the optimal design of experiments setup, different optimality criteria can be considered depending on the objectives of the practitioner. One of the most used is coptimality, which for a given model looks for the design that minimizes the variance of the linear combination of the parameters’ estimators given by vector c. coptimal designs are needed when dealing with standardized criteria, and ...
 DOI:
 10.1016/j.csda.2016.10.019

http://dx.doi.org/10.1016/j.csda.2016.10.019
 Author:
 Gauthier, B.; Pronzato, L.
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 375394
 ISSN:
 01679473
 Subject:
 Bayesian theory; linear models; probability
 Abstract:
 ... The construction of optimal designs for randomfield interpolation models via convex design theory is considered. The definition of an Integrated MeanSquared Error (IMSE) criterion yields a particular Karhunen–Loève expansion of the underlying random field. After spectral truncation, the model can be interpreted as a Bayesian (or regularised) linear model based on eigenfunctions of this Karhunen– ...
 DOI:
 10.1016/j.csda.2016.10.018

http://dx.doi.org/10.1016/j.csda.2016.10.018
 Author:
 Won, JoongHo; Wu, Xiao; Lee, Sang Han; Lu, Ying
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 154176
 ISSN:
 01679473
 Subject:
 Alzheimer disease; Markov chain; biomarkers; blood; cohort studies; image analysis; odds ratio; prediction; technology; variance
 Abstract:
 ... Medical imaging techniques are being rapidly developed and used for diagnosis and prognostic predictions. To validate a prognostic predictive utility of a new imaging marker, a temporal association needs to be established to show an association between its baseline value with a subsequent chance of having the relevant clinical outcome. Validation of such techniques has several difficulties. First, ...
 DOI:
 10.1016/j.csda.2016.12.017

http://dx.doi.org/10.1016/j.csda.2016.12.017
 Author:
 Leatherman, Erin R.; Dean, Angela M.; Santner, Thomas J.
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 346362
 ISSN:
 01679473
 Subject:
 Bayesian theory; computational methodology; computers; prediction
 Abstract:
 ... Combined designs for experiments involving a physical system and a simulator of the physical system are evaluated in terms of their accuracy of predicting the mean of the physical system. Comparisons are made among designs that are (1) locally optimal under the minimum integrated mean squared prediction error criterion for the combined physical system and simulator experiments, (2) locally optimal ...
 DOI:
 10.1016/j.csda.2016.07.013

http://dx.doi.org/10.1016/j.csda.2016.07.013
 Author:
 McGree, J.M.
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 207225
 ISSN:
 01679473
 Subject:
 Bayesian theory; algorithms; entropy; model uncertainty; models; utility functions
 Abstract:
 ... The total entropy utility function is considered for the dual purpose of model discrimination and parameter estimation in Bayesian design. A sequential design setting is considered where it is shown how to efficiently estimate the total entropy utility function in discrete data settings. Utility estimation relies on forming particle approximations to a number of intractable integrals which is affo ...
 DOI:
 10.1016/j.csda.2016.05.020

http://dx.doi.org/10.1016/j.csda.2016.05.020
 Author:
 da Silva, Marcelo A.; Gilmour, Steven G.; Trinca, Luzia A.
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 261272
 ISSN:
 01679473
 Subject:
 methodology
 Abstract:
 ... Compound optimum design criteria which allow pure error degrees of freedom may produce designs that break down when even a single run is missing, if the number of experimental units is small. The inclusion, in the compound criteria, of a measure of leverage uniformity is proposed in order to produce designs that are more robust to missing observations. By appropriately choosing the weights of each ...
 DOI:
 10.1016/j.csda.2016.05.023

http://dx.doi.org/10.1016/j.csda.2016.05.023
 Author:
 Blagus, Rok; Lusa, Lara
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 1937
 ISSN:
 01679473
 Subject:
 biomedical research; models; patients; prediction; probability
 Abstract:
 ... In clinical research the goal is often to correctly estimate the probability of an event. For this purpose several characteristics of the patients are measured and used to develop a prediction model which can be used to predict the class membership for future patients. Ensemble classifiers are combinations of many different classifiers and they can be useful because combining a set of classifiers ...
 DOI:
 10.1016/j.csda.2016.07.016

http://dx.doi.org/10.1016/j.csda.2016.07.016
 Author:
 He, Yong; Zhang, Xinsheng; Wang, Pingping; Zhang, Liwen
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 457474
 ISSN:
 01679473
 Subject:
 data collection; mathematical models; statistics; stock exchange
 Abstract:
 ... A multiple testing procedure is proposed to estimate the high dimensional Gaussian copula graphical model and nonparametric rankbased correlation coefficient estimators are exploited to construct the test statistics, which achieve modeling flexibility and estimation robustness. Compared to the existing methods depending on regularization technique, the proposed method avoids the ambiguous relatio ...
 DOI:
 10.1016/j.csda.2016.06.012

http://dx.doi.org/10.1016/j.csda.2016.06.012
 Author:
 Dhaene, Geert; Zhu, Yu
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 398423
 ISSN:
 01679473
 Subject:
 models
 Abstract:
 ... Outlierrobust estimators are proposed for linear dynamic fixedeffect panel data models where the number of observations is large and the number of time periods is small. In the simple setting of estimating the AR(1) coefficient from stationary Gaussian panel data, the estimator is (a linear transformation of) the median ratio of adjacent firstdifferenced data pairs. Its influence function is bo ...
 DOI:
 10.1016/j.csda.2016.05.021

http://dx.doi.org/10.1016/j.csda.2016.05.021
 Author:
 Schmidli, Heinz; Neuenschwander, Beat; Friede, Tim
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 100110
 ISSN:
 01679473
 Subject:
 Bayesian theory; clinical trials; macular degeneration; metaanalysis; models; patients; prediction; variance
 Abstract:
 ... Continuous endpoints are common in clinical trials. The design and analysis of such trials is often based on models assuming normally distributed data, possibly after an appropriate transformation. When planning a new trial, information on the variance of the endpoint is usually available from historical trials. Although the idea to use historical data for a new trial is not new, literature on how ...
 DOI:
 10.1016/j.csda.2016.08.007

http://dx.doi.org/10.1016/j.csda.2016.08.007
 Author:
 Wang, Zheyu; Sebestyen, Krisztian; Monsell, Sarah E.
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 125135
 ISSN:
 01679473
 Subject:
 Alzheimer disease; algorithms; biomarkers; image analysis; immunologic factors; models; nutritional status; prognosis; statistical analysis
 Abstract:
 ... A modelbased clustering method is proposed to address two research aims in Alzheimer’s disease (AD): to evaluate the accuracy of imaging biomarkers in AD prognosis, and to integrate biomarker information and standard clinical test results into the diagnoses. One challenge in such biomarker studies is that it is often desired or necessary to conduct the evaluation without relying on clinical diagn ...
 DOI:
 10.1016/j.csda.2016.10.026
 PubMed:
 28966420
 PubMed Central:
 PMC5613685

http://dx.doi.org/10.1016/j.csda.2016.10.026
 Author:
 Maruotti, Antonello; Punzo, Antonio
 Source:
 Computational statistics & data analysis 2017 v.113 pp. 475496
 ISSN:
 01679473
 Subject:
 Markov chain; algorithms; linear models; statistical analysis
 Abstract:
 ... A class of multivariate linear models under the longitudinal setting, in which unobserved heterogeneity may evolve over time, is introduced. A latent structure is considered to model heterogeneity, having a discrete support and following a firstorder Markov chain. Heavytailed multivariate distributions are introduced to deal with outliers. Maximum likelihood estimation is performed to estimate p ...
 DOI:
 10.1016/j.csda.2016.05.024

http://dx.doi.org/10.1016/j.csda.2016.05.024