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Lu Mao, Dan-Yu Lin, Donglin Zeng
Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution...
February 17, 2017: Biometrics
Steffen Ventz, William T Barry, Giovanni Parmigiani, Lorenzo Trippa
We develop a general class of response-adaptive Bayesian designs using hierarchical models, and provide open source software to implement them. Our work is motivated by recent master protocols in oncology, where several treatments are investigated simultaneously in one or multiple disease types, and treatment efficacy is expected to vary across biomarker-defined subpopulations. Adaptive trials such as I-SPY-2 (Barker et al., 2009) and BATTLE (Zhou et al., 2008) are special cases within our framework. We discuss the application of our adaptive scheme to two distinct research goals...
February 17, 2017: Biometrics
Shuai Chen, Lu Tian, Tianxi Cai, Menggang Yu
Many statistical methods have recently been developed for identifying subgroups of patients who may benefit from different available treatments. Compared with the traditional outcome-modeling approaches, these methods focus on modeling interactions between the treatments and covariates while by-pass or minimize modeling the main effects of covariates because the subgroup identification only depends on the sign of the interaction. However, these methods are scattered and often narrow in scope. In this article, we propose a general framework, by weighting and A-learning, for subgroup identification in both randomized clinical trials and observational studies...
February 17, 2017: Biometrics
David Petroff
Replacing missing data using the baseline observation carried forward (BOCF) technique is known to be fraught with problems. Despite recommendations to the contrary, BOCF and the related last observation carried forward (LOCF) continue to be used in some fields of research. We first show the use of BOCF in testing for a change in a single sample is essentially equivalent to what results from a completer analysis. Next, we derive a simple method based only on summary statistics for adjusting inference from a completer analysis in situations where the estimand of the completer analysis is expected to be close to that of the full analysis set...
February 15, 2017: Biometrics
Gregory Vaughan, Robert Aseltine, Kun Chen, Jun Yan
Forward stagewise estimation is a revived slow-brewing approach for model building that is particularly attractive in dealing with complex data structures for both its computational efficiency and its intrinsic connections with penalized estimation. Under the framework of generalized estimating equations, we study general stagewise estimation approaches that can handle clustered data and non-Gaussian/non-linear models in the presence of prior variable grouping structure. As the grouping structure is often not ideal in that even the important groups may contain irrelevant variables, the key is to simultaneously conduct group selection and within-group variable selection, that is, bi-level selection...
February 13, 2017: Biometrics
Simon N Wood, Matteo Fasiolo
We consider the optimization of smoothing parameters and variance components in models with a regular log likelihood subject to quadratic penalization of the model coefficients, via a generalization of the method of Fellner (1986) and Schall (1991). In particular: (i) we generalize the original method to the case of penalties that are linear in several smoothing parameters, thereby covering the important cases of tensor product and adaptive smoothers; (ii) we show why the method's steps increase the restricted marginal likelihood of the model, that it tends to converge faster than the EM algorithm, or obvious accelerations of this, and investigate its relation to Newton optimization; (iii) we generalize the method to any Fisher regular likelihood...
February 13, 2017: Biometrics
Paolo Frumento, Matteo Bottai
Quantile regression coefficient functions describe how the coefficients of a quantile regression model depend on the order of the quantile. A method for parametric modeling of quantile regression coefficient functions was discussed in a recent article. The aim of the present work is to extend the existing framework to censored and truncated data. We propose an estimator and derive its asymptotic properties. We discuss goodness-of-fit measures, present simulation results, and analyze the data that motivated this article...
February 9, 2017: Biometrics
L F McMillan, R M Fewster
We propose a method for visualizing genetic assignment data by characterizing the distribution of genetic profiles for each candidate source population. This method enhances the assignment method of Rannala and Mountain (1997) by calculating appropriate graph positions for individuals for which some genetic data are missing. An individual with missing data is positioned in the distributions of genetic profiles for a population according to its estimated quantile based on its available data. The quantiles of the genetic profile distribution for each population are calculated by approximating the cumulative distribution function (CDF) using the saddlepoint method, and then inverting the CDF to get the quantile function...
February 9, 2017: Biometrics
Hanwen Huang
The LASSO method estimates coefficients by minimizing the residual sum of squares plus a penalty term. The regularization parameter λ in LASSO controls the trade-off between data fitting and sparsity. We derive relationship between λ and the false discovery proportion (FDP) of LASSO estimator and show how to select λ so as to achieve a desired FDP. Our estimation is based on the asymptotic distribution of LASSO estimator in the limit of both sample size and dimension going to infinity with fixed ratio. We use a factor analysis model to describe the dependence structure of the design matrix...
February 9, 2017: Biometrics
Sheng Wu, Weng Kee Wong, Catherine M Crespi
We consider design issues for cluster randomized trials (CRTs) with a binary outcome where both unit costs and intraclass correlation coefficients (ICCs) in the two arms may be unequal. We first propose a design that maximizes cost efficiency (CE), defined as the ratio of the precision of the efficacy measure to the study cost. Because such designs can be highly sensitive to the unknown ICCs and the anticipated success rates in the two arms, a local strategy based on a single set of best guesses for the ICCs and success rates can be risky...
February 9, 2017: Biometrics
Joanna H Shih, Michael P Fay
Pearson's chi-square test has been widely used in testing for association between two categorical responses. Spearman rank correlation and Kendall's tau are often used for measuring and testing association between two continuous or ordered categorical responses. However, the established statistical properties of these tests are only valid when each pair of responses are independent, where each sampling unit has only one pair of responses. When each sampling unit consists of a cluster of paired responses, the assumption of independent pairs is violated...
February 9, 2017: Biometrics
Huaihou Chen, Donglin Zeng, Yuanjia Wang
Precise modeling of disease progression in neurodegenerative disorders may enable early intervention before clinical manifestation of a disease, which is crucial since early intervention at the premanifest stage is expected to be more effective. Neuroimaging biomarkers are indicative of the underlying disease pathology and may be used to predict future disease occurrence at the premanifest stage. As observed in many pivotal studies, longitudinal measurements of clinical outcomes, such as motor or cognitive symptoms, often present nonlinear sigmoid shapes over time, where the inflection points of the trajectories mark a meaningful time in disease progression...
February 9, 2017: Biometrics
Heng Wang, Ping-Shou Zhong
Missing values appear very often in many applications, but the problem of missing values has not received much attention in testing order-restricted alternatives. Under the missing at random (MAR) assumption, we impute the missing values nonparametrically using kernel regression. For data with imputation, the classical likelihood ratio test designed for testing the order-restricted means is no longer applicable since the likelihood does not exist. This article proposes a novel method for constructing test statistics for assessing means with an increasing order or a decreasing order based on jackknife empirical likelihood (JEL) ratio...
February 9, 2017: Biometrics
Antonio Gasparrini, Fabian Scheipl, Ben Armstrong, Michael G Kenward
Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM framework through the use of penalized splines within generalized additive models (GAM). This extension offers built-in model selection procedures and the possibility of accommodating assumptions on the shape of the lag structure through specific penalties. In addition, this framework includes, as special cases, simpler models previously proposed for linear relationships (DLMs)...
January 30, 2017: Biometrics
Lyndsay Shand, Bo Li
We propose to model a spatio-temporal random field that has nonstationary covariance structure in both space and time domains by applying the concept of the dimension expansion method in Bornn et al. (2012). Simulations are conducted for both separable and nonseparable space-time covariance models, and the model is also illustrated with a streamflow dataset. Both simulation and data analyses show that modeling nonstationarity in both space and time can improve the predictive performance over stationary covariance models or models that are nonstationary in space but stationary in time...
January 30, 2017: Biometrics
Barbara Bogacka, Mahbub A H M Latif, Steven G Gilmour, Kuresh Youdim
In this article, we present a new method for optimizing designs of experiments for non-linear mixed effects models, where a categorical factor with covariate information is a design variable combined with another design factor. The work is motivated by the need to efficiently design preclinical experiments in enzyme kinetics for a set of Human Liver Microsomes. However, the results are general and can be applied to other experimental situations where the variation in the response due to a categorical factor can be partially accounted for by a covariate...
January 28, 2017: Biometrics
Tao Wang, Hongyu Zhao
Understanding the factors that alter the composition of the human microbiota may help personalized healthcare strategies and therapeutic drug targets. In many sequencing studies, microbial communities are characterized by a list of taxa, their counts, and their evolutionary relationships represented by a phylogenetic tree. In this article, we consider an extension of the Dirichlet multinomial distribution, called the Dirichlet-tree multinomial distribution, for multivariate, over-dispersed, and tree-structured count data...
January 23, 2017: Biometrics
Ying Yan, Haibo Zhou, Jianwen Cai
The case-cohort study design is an effective way to reduce cost of assembling and measuring expensive covariates in large cohort studies. Recently, several weighted estimators were proposed for the case-cohort design when multiple diseases are of interest. However, these existing weighted estimators do not make effective use of the covariate information available in the whole cohort. Furthermore, the auxiliary information for the expensive covariates, which may be available in the studies, cannot be incorporated directly...
January 23, 2017: Biometrics
David Soave, Lei Sun
We generalize Levene's test for variance (scale) heterogeneity between k groups for more complex data, when there are sample correlation and group membership uncertainty. Following a two-stage regression framework, we show that least absolute deviation regression must be used in the stage 1 analysis to ensure a correct asymptotic χk-12/(k-1) distribution of the generalized scale (gS) test statistic. We then show that the proposed gS test is independent of the generalized location test, under the joint null hypothesis of no mean and no variance heterogeneity...
January 18, 2017: Biometrics
Zhixiang Lin, Tao Wang, Can Yang, Hongyu Zhao
In this article, we first propose a Bayesian neighborhood selection method to estimate Gaussian Graphical Models (GGMs). We show the graph selection consistency of this method in the sense that the posterior probability of the true model converges to one. When there are multiple groups of data available, instead of estimating the networks independently for each group, joint estimation of the networks may utilize the shared information among groups and lead to improved estimation for each individual network...
January 18, 2017: Biometrics
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