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Articles 91 - 106 of 106
Full-Text Articles in Biostatistics
A Tale Of Two Streets: Incorporating Grouping Structure In High Dimensional Data Mining, Shuangge Ma
A Tale Of Two Streets: Incorporating Grouping Structure In High Dimensional Data Mining, Shuangge Ma
Shuangge Ma
No abstract provided.
Estimating Subject-Specific Dependent Competing Risk Profile With Censored Event Time Observations, Yi Li, Lu Tian, L. J. Wei
Estimating Subject-Specific Dependent Competing Risk Profile With Censored Event Time Observations, Yi Li, Lu Tian, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Class Of Semiparametric Mixture Cure Survival Models With Dependent Censoring, Megan Othus, Yi Li, Ram C. Tiwari
A Class Of Semiparametric Mixture Cure Survival Models With Dependent Censoring, Megan Othus, Yi Li, Ram C. Tiwari
Harvard University Biostatistics Working Paper Series
No abstract provided.
Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei
Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Identification Of Yeast Transcriptional Regulation Networks Using Multivariate Random Forests, Yuanyuan Xiao, Mark Segal
Identification Of Yeast Transcriptional Regulation Networks Using Multivariate Random Forests, Yuanyuan Xiao, Mark Segal
Mark R Segal
The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression …
Computer Intensive Methods Lecture 1, Shuangge Ma
Detection Of Gene Pathways With Predictive Power For Breast Cancer Prognosis, Shuangge Ma
Detection Of Gene Pathways With Predictive Power For Breast Cancer Prognosis, Shuangge Ma
Shuangge Ma
Prognosis of breast cancer is of great scientific and practical interest. Biomedical studies suggest that clinical and environmental risk factors do not have satisfactory predictive power for prognosis. Multiple gene profiling studies have been conducted, searching for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with similar biological functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Although multiple pathway analysis methods are available, they have certain drawbacks and are not suitable for the proposed analysis. In this article, we develop a new …
Optimal Cutpoint Estimation With Censored Data, Mithat Gonen, Camelia Sima
Optimal Cutpoint Estimation With Censored Data, Mithat Gonen, Camelia Sima
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
We consider the problem of selecting an optimal cutpoint for a continuous marker when the outcome of interest is subject to right censoring. Maximal chi square methods and receiver operating characteristic (ROC) curves-based methods are commonly-used when the outcome is binary. In this article we show that selecting the cutpoint that maximizes the concordance, a metric similar to the area under an ROC curve, is equivalent to maximizing the Youden index, a popular criterion when the ROC curve is used to choose a threshold. We use this as a basis for proposing maximal concordance as a metric to use with …
A New Class Of Rank Tests For Interval-Censored Data, Guadalupe Gomez, Ramon Oller Pique
A New Class Of Rank Tests For Interval-Censored Data, Guadalupe Gomez, Ramon Oller Pique
Harvard University Biostatistics Working Paper Series
No abstract provided.
Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Semiparametric Maximum Likelihood Estimation In Normal Transformation Models For Bivariate Survival Data, Yi Li, Ross L. Prentice, Xihong Lin
Semiparametric Maximum Likelihood Estimation In Normal Transformation Models For Bivariate Survival Data, Yi Li, Ross L. Prentice, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Survival Analysis With Large Dimensional Covariates: An Application In Microarray Studies, David A. Engler, Yi Li
Survival Analysis With Large Dimensional Covariates: An Application In Microarray Studies, David A. Engler, Yi Li
Harvard University Biostatistics Working Paper Series
Use of microarray technology often leads to high-dimensional and low- sample size data settings. Over the past several years, a variety of novel approaches have been proposed for variable selection in this context. However, only a small number of these have been adapted for time-to-event data where censoring is present. Among standard variable selection methods shown both to have good predictive accuracy and to be computationally efficient is the elastic net penalization approach. In this paper, adaptation of the elastic net approach is presented for variable selection both under the Cox proportional hazards model and under an accelerated failure time …
Chess, Chance And Conspiracy, Mark Segal
Chess, Chance And Conspiracy, Mark Segal
Mark R Segal
Chess and chance are seemingly strange bedfellows. Luck and/or randomness have no apparent role in move selection when the game is played at the highest levels. However, when competition is at the ultimate level, that of the World Chess Championship (WCC), chess and conspiracy are not strange bedfellows, there being a long and colorful history of accusations levied between participants. One such accusation, frequently repeated, was that all the games in the 1985 WCC (Karpov vs Kasparov) were fixed and prearranged move by move. That this claim was advanced by a former World Champion, Bobby Fischer, argues that it ought …
Linear Regression Of Censored Length-Biased Lifetimes, Ying Qing Chen, Yan Wang
Linear Regression Of Censored Length-Biased Lifetimes, Ying Qing Chen, Yan Wang
UW Biostatistics Working Paper Series
Length-biased lifetimes may be collected in observational studies or sample surveys due to biased sampling scheme. In this article, we use a linear regression model, namely, the accelerated failure time model, for the population lifetime distributions in regression analysis of the length-biased lifetimes. It is discovered that the associated regression parameters are invariant under the length-biased sampling scheme. According to this discovery, we propose the quasi partial score estimating equations to estimate the population regression parameters. The proposed methodologies are evaluated and demonstrated by simulation studies and an application to actual data set.
New Statistical Paradigms Leading To Web-Based Tools For Clinical/Translational Science, Knut M. Wittkowski
New Statistical Paradigms Leading To Web-Based Tools For Clinical/Translational Science, Knut M. Wittkowski
COBRA Preprint Series
As the field of functional genetics and genomics is beginning to mature, we become confronted with new challenges. The constant drop in price for sequencing and gene expression profiling as well as the increasing number of genetic and genomic variables that can be measured makes it feasible to address more complex questions. The success with rare diseases caused by single loci or genes has provided us with a proof-of-concept that new therapies can be developed based on functional genomics and genetics.
Common diseases, however, typically involve genetic epistasis, genomic pathways, and proteomic pattern. Moreover, to better understand the underlying biologi-cal …