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Journal of the American Statistical Association

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https://www.readbyqxmd.com/read/29348701/statistical-significance-and-the-dichotomization-of-evidence-the-relevance-of-the-asa-statement-on-statistical-significance-and-p-values-for-statisticians
#1
Eric B Laber, Kerby Shedden
No abstract text is available yet for this article.
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/29276318/random-partition-distribution-indexed-by-pairwise-information
#2
David B Dahl, Ryan Day, Jerry W Tsai
We propose a random partition distribution indexed by pairwise similarity information such that partitions compatible with the similarities are given more probability. The use of pairwise similarities, in the form of distances, is common in some clustering algorithms (e.g., hierarchical clustering), but we show how to use this type of information to define a prior partition distribution for flexible Bayesian modeling. A defining feature of the distribution is that it allocates probability among partitions within a given number of subsets, but it does not shift probability among sets of partitions with different numbers of subsets...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/29225385/extrinsic-local-regression-on-manifold-valued-data
#3
Lizhen Lin, Brian St Thomas, Hongtu Zhu, David B Dunson
We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in shape analysis, neuroscience, medical imaging and many other areas. Our approach embeds the manifold where the responses lie onto a higher dimensional Euclidean space, obtains a local regression estimate in that space, and then projects this estimate back onto the image of the manifold. Outside the regression setting both intrinsic and extrinsic approaches have been proposed for modeling i...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/29200540/a-functional-varying-coefficient-single-index-model-for-functional-response-data
#4
Jialiang Li, Chao Huang, Hongtu Zhu
Motivated by the analysis of imaging data, we propose a novel functional varying-coefficient single index model (FVCSIM) to carry out the regression analysis of functional response data on a set of covariates of interest. FVCSIM represents a new extension of varying-coefficient single index models for scalar responses collected from cross-sectional and longitudinal studies. An efficient estimation procedure is developed to iteratively estimate varying coefficient functions, link functions, index parameter vectors, and the covariance function of individual functions...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/29151658/generalized-scalar-on-image-regression-models-via-total-variation
#5
Xiao Wang, Hongtu Zhu
The use of imaging markers to predict clinical outcomes can have a great impact in public health. The aim of this paper is to develop a class of generalized scalar-on-image regression models via total variation (GSIRM-TV), in the sense of generalized linear models, for scalar response and imaging predictor with the presence of scalar covariates. A key novelty of GSIRM-TV is that it is assumed that the slope function (or image) of GSIRM-TV belongs to the space of bounded total variation in order to explicitly account for the piecewise smooth nature of most imaging data...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/29151657/mwpcr-multiscale-weighted-principal-component-regression-for-high-dimensional-prediction
#6
Hongtu Zhu, Dan Shen, Xuewei Peng, Leo Yufeng Liu
We propose a multiscale weighted principal component regression (MWPCR) framework for the use of high dimensional features with strong spatial features (e.g., smoothness and correlation) to predict an outcome variable, such as disease status. This development is motivated by identifying imaging biomarkers that could potentially aid detection, diagnosis, assessment of prognosis, prediction of response to treatment, and monitoring of disease status, among many others. The MWPCR can be regarded as a novel integration of principal components analysis (PCA), kernel methods, and regression models...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/28966418/evaluating-utility-measurement-from-recurrent-marker-processes-in-the-presence-of-competing-terminal-events
#7
Yifei Sun, Mei-Cheng Wang
In follow-up studies, utility marker measurements are usually collected upon the occurrence of recurrent events until a terminal event such as death takes place. In this article, we define the recurrent marker process to characterize utility accumulation over time. For example, with medical cost and repeated hospitalizations being treated as marker and recurrent events respectively, the recurrent marker process is the trajectory of cumulative cost, which stops to increase after death. In many applications, competing risks arise as subjects are at risk of more than one mutually exclusive terminal event, such as death from different causes, and modeling the recurrent marker process for each failure type is often of interest...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/28966417/latent-class-survival-models-linked-by-principal-stratification-to-investigate-heterogenous-survival-subgroups-among-individuals-with-early-stage-kidney-cancer
#8
Brian L Egleston, Robert G Uzzo, Yu-Ning Wong
Rates of kidney cancer have been increasing, with small incidental tumors experiencing the fastest growth rates. Much of the increase could be due to increased use of CT scans, MRIs, and ultrasounds for unrelated conditions. Many tumors might never have been detected or become symptomatic in the past. This suggests that many patients might benefit from less aggressive therapy, such as active surveillance by which tumors are surgically removed only if they become sufficiently large. However, it has been difficult for clinicians to identify subgroups of patients for whom treatment might be especially beneficial or harmful...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/28943684/joint-scale-change-models-for-recurrent-events-and-failure-time
#9
Gongjun Xu, Sy Han Chiou, Chiung-Yu Huang, Mei-Cheng Wang, Jun Yan
Recurrent event data arise frequently in various fields such as biomedical sciences, public health, engineering, and social sciences. In many instances, the observation of the recurrent event process can be stopped by the occurrence of a correlated failure event, such as treatment failure and death. In this article, we propose a joint scale-change model for the recurrent event process and the failure time, where a shared frailty variable is used to model the association between the two types of outcomes. In contrast to the popular Cox-type joint modeling approaches, the regression parameters in the proposed joint scale-change model have marginal interpretations...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/28943683/variable-screening-via-quantile-partial-correlation
#10
Shujie Ma, Runze Li, Chih-Ling Tsai
In quantile linear regression with ultra-high dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best subset selection. Our proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution to the conditional quantiles but are marginally uncorrelated or weakly correlated with the response...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/28943682/residual-weighted-learning-for-estimating-individualized-treatment-rules
#11
Xin Zhou, Nicole Mayer-Hamblett, Umer Khan, Michael R Kosorok
Personalized medicine has received increasing attention among statisticians, computer scientists, and clinical practitioners. A major component of personalized medicine is the estimation of individualized treatment rules (ITRs). Recently, Zhao et al. (2012) proposed outcome weighted learning (OWL) to construct ITRs that directly optimize the clinical outcome. Although OWL opens the door to introducing machine learning techniques to optimal treatment regimes, it still has some problems in performance. (1) The estimated ITR of OWL is affected by a simple shift of the outcome...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/28943681/robust-treatment-comparison-based-on-utilities-of-semi-competing-risks-in-non-small-cell-lung-cancer
#12
Thomas A Murray, Peter F Thall, Ying Yuan, Sarah McAvoy, Daniel R Gomez
A design is presented for a randomized clinical trial comparing two second-line treatments, chemotherapy versus chemotherapy plus reirradiation, for treatment of recurrent non-small-cell lung cancer. The central research question is whether the potential efficacy benefit that adding reirradiation to chemotherapy may provide justifies its potential for increasing the risk of toxicity. The design uses two co-primary outcomes: time to disease progression or death, and time to severe toxicity. Because patients may be given an active third-line treatment at disease progression that confounds second-line treatment effects on toxicity and survival following disease progression, for the purpose of this comparative study follow-up ends at disease progression or death...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/28919653/comments-on-personalized-dose-finding-using-outcome-weighted-learning
#13
COMMENT
Jun Fan, Ming Yuan
No abstract text is available yet for this article.
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/28890584/interactive-q-learning-for-quantiles
#14
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
https://www.readbyqxmd.com/read/28804182/change-plane-analysis-for-subgroup-detection-and-sample-size-calculation
#15
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
https://www.readbyqxmd.com/read/28736464/the-generalized-higher-criticism-for-testing-snp-set-effects-in-genetic-association-studies
#16
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
https://www.readbyqxmd.com/read/28694552/semiparametric-modeling-and-estimation-of-the-terminal-behavior-of-recurrent-marker-processes-before-failure-events
#17
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
https://www.readbyqxmd.com/read/27570323/constrained-maximum-likelihood-estimation-for-model-calibration-using-summary-level-information-from-external-big-data-sources
#18
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
https://www.readbyqxmd.com/read/27499564/asymptotically-normal-and-efficient-estimation-of-covariate-adjusted-gaussian-graphical-model
#19
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
https://www.readbyqxmd.com/read/27226674/statistical-inference-in-hidden-markov-models-using-k-segment-constraints
#20
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
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