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Journal of the American Statistical Association

Jun Fan, Ming Yuan
No abstract text is available yet for this article.
2017: Journal of the American Statistical Association
Kristin A Linn, Eric B Laber, Leonard A Stefanski
A dynamic treatment regime is a sequence of decision rules, each of which recommends treatment based on features of patient medical history such as past treatments and outcomes. Existing methods for estimating optimal dynamic treatment regimes from data optimize the mean of a response variable. However, the mean may not always be the most appropriate summary of performance. We derive estimators of decision rules for optimizing probabilities and quantiles computed with respect to the response distribution for two-stage, binary treatment settings...
2017: Journal of the American Statistical Association
Ailin Fan, Rui Song, Wenbin Lu
We propose a systematic method for testing and identifying a subgroup with an enhanced treatment effect. We adopts a change-plane technique to first test the existence of a subgroup, and then identify the subgroup if the null hypothesis on non-existence of such a subgroup is rejected. A semiparametric model is considered for the response with an unspecified baseline function and an interaction between a subgroup indicator and treatment. A doubly-robust test statistic is constructed based on this model, and asymptotic distributions of the test statistic under both null and local alternative hypotheses are derived...
2017: Journal of the American Statistical Association
Ian Barnett, Rajarshi Mukherjee, Xihong Lin
It is of substantial interest to study the effects of genes, genetic pathways, and networks on the risk of complex diseases. These genetic constructs each contain multiple SNPs, which are often correlated and function jointly, and might be large in number. However, only a sparse subset of SNPs in a genetic construct is generally associated with the disease of interest. In this article, we propose the generalized higher criticism (GHC) to test for the association between an SNP set and a disease outcome. The higher criticism is a test traditionally used in high-dimensional signal detection settings when marginal test statistics are independent and the number of parameters is very large...
2017: Journal of the American Statistical Association
Kwun Chuen Gary Chan, Mei-Cheng Wang
Recurrent event processes with marker measurements are mostly and largely studied with forward time models starting from an initial event. Interestingly, the processes could exhibit important terminal behavior during a time period before occurrence of the failure event. A natural and direct way to study recurrent events prior to a failure event is to align the processes using the failure event as the time origin and to examine the terminal behavior by a backward time model. This paper studies regression models for backward recurrent marker processes by counting time backward from the failure event...
2017: Journal of the American Statistical Association
Nilanjan Chatterjee, Yi-Hau Chen, Paige Maas, Raymond J Carroll
Information from various public and private data sources of extremely large sample sizes are now increasingly available for research purposes. Statistical methods are needed for utilizing information from such big data sources while analyzing data from individual studies that may collect more detailed information required for addressing specific hypotheses of interest. In this article, we consider the problem of building regression models based on individual-level data from an "internal" study while utilizing summary-level information, such as information on parameters for reduced models, from an "external" big data source...
March 2016: Journal of the American Statistical Association
Mengjie Chen, Zhao Ren, Hongyu Zhao, Harrison Zhou
A tuning-free procedure is proposed to estimate the covariate-adjusted Gaussian graphical model. For each finite subgraph, this estimator is asymptotically normal and efficient. As a consequence, a confidence interval can be obtained for each edge. The procedure enjoys easy implementation and efficient computation through parallel estimation on subgraphs or edges. We further apply the asymptotic normality result to perform support recovery through edge-wise adaptive thresholding. This support recovery procedure is called ANTAC, standing for Asymptotically Normal estimation with Thresholding after Adjusting Covariates...
March 2016: Journal of the American Statistical Association
Michalis K Titsias, Christopher C Holmes, Christopher Yau
Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward-backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-time dynamic programming recursions that, conditional on a user-specified constraint in the number of segments, allow us to (i) find MAP sequences, (ii) compute posterior probabilities, and (iii) simulate sample paths...
January 2, 2016: Journal of the American Statistical Association
Ci-Ren Jiang, John A D Aston, Jane-Ling Wang
Positron emission tomography (PET) is an imaging technique which can be used to investigate chemical changes in human biological processes such as cancer development or neurochemical reactions. Most dynamic PET scans are currently analyzed based on the assumption that linear first-order kinetics can be used to adequately describe the system under observation. However, there has recently been strong evidence that this is not the case. To provide an analysis of PET data which is free from this compartmental assumption, we propose a nonparametric deconvolution and analysis model for dynamic PET data based on functional principal component analysis...
January 2, 2016: Journal of the American Statistical Association
Min Qian
This comment deals with issues related to the article by Chen, Zeng, and Kosorok. We present several potential modifications of the outcome weighted learning approach. Those modifications are based on truncated l2 loss. One advantage of l2 loss is that it is differentiable everywhere, which makes it more stable and computationally more tractable.
2016: Journal of the American Statistical Association
Yize Zhao, Matthias Chung, Brent A Johnson, Carlos S Moreno, Qi Long
Our work is motivated by a prostate cancer study aimed at identifying mRNA and miRNA biomarkers that are predictive of cancer recurrence after prostatectomy. It has been shown in the literature that incorporating known biological information on pathway memberships and interactions among biomarkers improves feature selection of high-dimensional biomarkers in relation to disease risk. Biological information is often represented by graphs or networks, in which biomarkers are represented by nodes and interactions among them are represented by edges; however, biological information is often not fully known...
2016: Journal of the American Statistical Association
Fernando A Quintana, Wesley O Johnson, Elaine Waetjen, Ellen Gold
Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart...
2016: Journal of the American Statistical Association
Emre Barut, Jianqing Fan, Anneleen Verhasselt
Independence screening is powerful for variable selection when the number of variables is massive. Commonly used independence screening methods are based on marginal correlations or its variants. When some prior knowledge on a certain important set of variables is available, a natural assessment on the relative importance of the other predictors is their conditional contributions to the response given the known set of variables. This results in conditional sure independence screening (CSIS). CSIS produces a rich family of alternative screening methods by different choices of the conditioning set and can help reduce the number of false positive and false negative selections when covariates are highly correlated...
2016: Journal of the American Statistical Association
Ritabrata Das, Moulinath Banerjee, Bin Nan, Huiyong Zheng
Estimation of change-point locations in the broken-stick model has significant applications in modeling important biological phenomena. In this article we present a computationally economical likelihood-based approach for estimating change-point(s) efficiently in both cross-sectional and longitudinal settings. Our method, based on local smoothing in a shrinking neighborhood of each change-point, is shown via simulations to be computationally more viable than existing methods that rely on search procedures, with dramatic gains in the multiple change-point case...
2016: Journal of the American Statistical Association
Kyu Ha Lee, Francesca Dominici, Deborah Schrag, Sebastien Haneuse
Readmission following discharge from an initial hospitalization is a key marker of quality of health care in the United States. For the most part, readmission has been studied among patients with 'acute' health conditions, such as pneumonia and heart failure, with analyses based on a logistic-Normal generalized linear mixed model (Normand et al., 1997). Naïve application of this model to the study of readmission among patients with 'advanced' health conditions such as pancreatic cancer, however, is problematic because it ignores death as a competing risk...
2016: Journal of the American Statistical Association
Jon Wakefield, Daniel Simpson, Jessica Godwin
No abstract text is available yet for this article.
2016: Journal of the American Statistical Association
Guanhua Chen, Donglin Zeng, Michael R Kosorok
In dose-finding clinical trials, it is becoming increasingly important to account for individual level heterogeneity while searching for optimal doses to ensure an optimal individualized dose rule (IDR) maximizes the expected beneficial clinical outcome for each individual. In this paper, we advocate a randomized trial design where candidate dose levels assigned to study subjects are randomly chosen from a continuous distribution within a safe range. To estimate the optimal IDR using such data, we propose an outcome weighted learning method based on a nonconvex loss function, which can be solved efficiently using a difference of convex functions algorithm...
2016: Journal of the American Statistical Association
Sung Won Han, Gong Chen, Myun-Seok Cheon, Hua Zhong
Graphical models are a popular approach to find dependence and conditional independence relationships between gene expressions. Directed acyclic graphs (DAGs) are a special class of directed graphical models, where all the edges are directed edges and contain no directed cycles. The DAGs are well known models for discovering causal relationships between genes in gene regulatory networks. However, estimating DAGs without assuming known ordering is challenging due to high dimensionality, the acyclic constraints, and the presence of equivalence class from observational data...
2016: Journal of the American Statistical Association
Zhe Yu, Raquel Prado, Erin B Quinlan, Steven C Cramer, Hernando Ombao
Stroke is a disturbance in blood supply to the brain resulting in the loss of brain functions, particularly motor function. A study was conducted by the UCI Neurorehabilitation Lab to investigate the impact of stroke on motor-related brain regions. Functional MRI (fMRI) data were collected from stroke patients and healthy controls while the subjects performed a simple motor task. In addition to affecting local neuronal activation strength, stroke might also alter communications (i.e., connectivity) between brain regions...
2016: Journal of the American Statistical Association
Rui Pan, Hansheng Wang, Runze Li
This paper is concerned with the problem of feature screening for multi-class linear discriminant analysis under ultrahigh dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant features is larger than usual. This makes the related classification problem much more challenging than the conventional one, where the number of classes is small (very often two). To solve the problem, we propose a novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh dimensional predictor...
2016: Journal of the American Statistical Association
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