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Yeonhee Park, Zhihua Su, Hongtu Zhu
Motivated by searching for associations between genetic variants and brain imaging phenotypes, the aim of this article is to develop a groupwise envelope model for multivariate linear regression in order to establish the association between both multivariate responses and covariates. The groupwise envelope model allows for both distinct regression coefficients and distinct error structures for different groups. Statistically, the proposed envelope model can dramatically improve efficiency of tests and of estimation...
March 21, 2017: Biometrics
E Juarez-Colunga, G L Silva, C B Dean
Panel counts are often encountered in longitudinal, such as diary, studies where individuals are followed over time and the number of events occurring in time intervals, or panels, is recorded. This article develops methods for situations where, in addition to the counts of events, a mark, denoting a measure of severity of the events, is recorded. In many situations there is an association between the panel counts and their marks. This is the case for our motivating application that studies the effect of two hormone therapy treatments in reducing counts and severities of vasomotor symptoms in women after hysterectomy/ovariectomy...
March 17, 2017: Biometrics
Xiang Zhan, Anna Plantinga, Ni Zhao, Michael C Wu
To fully understand the role of microbiome in human health and diseases, researchers are increasingly interested in assessing the relationship between microbiome composition and host genomic data. The dimensionality of the data as well as complex relationships between microbiota and host genomics pose considerable challenges for analysis. In this article, we apply a kernel RV coefficient (KRV) test to evaluate the overall association between host gene expression and microbiome composition. The KRV statistic can capture nonlinear correlations and complex relationships among the individual data types and between gene expression and microbiome composition through measuring general dependency...
March 10, 2017: Biometrics
Yu-Ru Su, Chong-Zhi Di, Li Hsu
Functional data arise frequently in biomedical studies, where it is often of interest to investigate the association between functional predictors and a scalar response variable. While functional linear models (FLM) are widely used to address these questions, hypothesis testing for the functional association in the FLM framework remains challenging. A popular approach to testing the functional effects is through dimension reduction by functional principal component (PC) analysis. However, its power performance depends on the choice of the number of PCs, and is not systematically studied...
March 10, 2017: Biometrics
Peijun Sang, Liangliang Wang, Jiguo Cao
Functional principal component analysis (FPCA) is a popular approach in functional data analysis to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). Most existing FPCA approaches use a set of flexible basis functions such as B-spline basis to represent the FPCs, and control the smoothness of the FPCs by adding roughness penalties. However, the flexible representations pose difficulties for users to understand and interpret the FPCs...
March 10, 2017: Biometrics
Bochao Jia, Suwa Xu, Guanghua Xiao, Vishal Lamba, Faming Liang
In recent years, next generation sequencing (NGS) has gradually replaced microarray as the major platform in measuring gene expressions. Compared to microarray, NGS has many advantages, such as less noise and higher throughput. However, the discreteness of NGS data also challenges the existing statistical methodology. In particular, there still lacks an appropriate statistical method for reconstructing gene regulatory networks using NGS data in the literature. The existing local Poisson graphical model method is not consistent and can only infer certain local structures of the network...
March 10, 2017: Biometrics
Yu Zheng, Tianxi Cai
Reliable and accurate risk prediction is fundamental for successful management of clinical conditions. Estimating comprehensive risk prediction models precisely, however, is a difficult task, especially when the outcome of interest is time to a rare event and the number of candidate predictors, p, is not very small. Another challenge in developing accurate risk models arises from potential model misspecification. Time-specific generalized linear models estimated with inverse censoring probability weighting are robust to model misspecification, but may be inefficient in the rare event setting...
March 10, 2017: Biometrics
Wei Dai, Ming Yang, Chaolong Wang, Tianxi Cai
Genome-wide association studies (GWAS) and next generation sequencing studies (NGSS) are often performed in family studies to improve power in identifying genetic variants that are associated with clinical phenotypes. Efficient analysis of genome-wide studies with familial data is challenging due to the difficulty in modeling shared but unmeasured genetic and/or environmental factors that cause dependencies among family members. Existing genetic association testing procedures for family studies largely rely on generalized estimating equations (GEE) or linear mixed-effects (LME) models...
March 8, 2017: Biometrics
Susan M Shortreed, Ashkan Ertefaie
Methodological advancements, including propensity score methods, have resulted in improved unbiased estimation of treatment effects from observational data. Traditionally, a "throw in the kitchen sink" approach has been used to select covariates for inclusion into the propensity score, but recent work shows including unnecessary covariates can impact both the bias and statistical efficiency of propensity score estimators. In particular, the inclusion of covariates that impact exposure but not the outcome, can inflate standard errors without improving bias, while the inclusion of covariates associated with the outcome but unrelated to exposure can improve precision...
March 8, 2017: Biometrics
Yakir Berchenko, Jonathan D Rosenblatt, Simon D W Frost
Respondent-driven sampling (RDS) is an approach to sampling design and analysis which utilizes the networks of social relationships that connect members of the target population, using chain-referral. RDS sampling will typically oversample participants with many acquaintances. Naïve estimators, such as the sample average, will thus be biased towards the state of the most highly connected individuals. Current methodology cannot estimate population size from RDS, and promotes inverse probability weighted estimators for population parameters such as HIV prevalence...
March 3, 2017: Biometrics
Yu Deng, Donglin Zeng, Jinying Zhao, Jianwen Cai
In many epidemiology studies, family data with survival endpoints are collected to investigate the association between risk factors and disease incidence. Sometimes the risk of the disease may change when a certain risk factor exceeds a certain threshold. Finding this threshold value could be important for disease risk prediction and diseases prevention. In this work, we propose a change-point proportional hazards model for clustered event data. The model incorporates the unknown threshold of a continuous variable as a change point in the regression...
March 3, 2017: Biometrics
Satoshi Morita, Peter Müller
The identification of good predictive biomarkers allows investigators to optimize the target population for a new treatment. We propose a novel utility-based Bayesian population finding (BaPoFi) method to analyze data from a randomized clinical trial with the aim of finding a sensitive patient population. Our approach is based on casting the population finding process as a formal decision problem together with a flexible probability model, Bayesian additive regression trees (BART), to summarize observed data...
March 3, 2017: Biometrics
Eric J Tchetgen Tchetgen, Kathleen E Wirth
The instrumental variable (IV) design is a well-known approach for unbiased evaluation of causal effects in the presence of unobserved confounding. In this article, we study the IV approach to account for selection bias in regression analysis with outcome missing not at random. In such a setting, a valid IV is a variable which (i) predicts the nonresponse process, and (ii) is independent of the outcome in the underlying population. We show that under the additional assumption (iii) that the IV is independent of the magnitude of selection bias due to nonresponse, the population regression in view is nonparametrically identified...
February 23, 2017: Biometrics
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
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