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Deep sequencing will soon generate comprehensive sequence information in large disease samples. Although the power to detect association with an individual rare variant is limited, pooling variants by gene or pathway into a composite test provides an alternative strategy for identifying susceptibility genes. We describe a statistical method for detecting(More)
For modern evidence-based medicine, a well thought-out risk scoring system for predicting the occurrence of a clinical event plays an important role in selecting prevention and treatment strategies. Such an index system is often established based on the subject's 'baseline' genetic or clinical markers via a working parametric or semi-parametric model. To(More)
For the past two decades the Cox proportional hazards model has been used extensively to examine the covariate effects on the hazard function for the failure time variable. On the other hand, the accelerated failure time model, which simply regresses the logarithm of the survival time over the covariates, has seldom been utilized in the analysis of censored(More)
MOTIVATION Oligonucleotide microarrays allow genotyping of thousands of single-nucleotide polymorphisms (SNPs) in parallel. Recently, this technology has been applied to loss-of-heterozygosity (LOH) analysis of paired normal and tumor samples. However, methods and software for analyzing such data are not fully developed. RESULT Here, we report automated(More)
OBJECTIVE To compare the safety of nelfinavir and nevirapine-based antiretroviral treatment in HIV-1-infected pregnant women. METHODS In Pediatric AIDS Clinical Trials Group Protocol 1022, 38 antiretroviral-naive pregnant women at 10-30 weeks' gestation were randomized to nelfinavir or nevirapine with zidovudine plus lamivudine. The study was suspended(More)
Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models Hajime Uno, Tianxi Cai, Lu Tian and L. J. Wei Hajime Uno is Associate Professor, Division of Biostatistics, School of Pharmaceutical Sciences, Kitasato University, Tokyo, Japan 108-8641 . Tianxi Cai is Associate Professor, Department of Biostatistics, Harvard School of Public(More)
In comparing two treatments under a typical sequential clinical trial setting, a 50-50 randomization design generates reliable data for making efficient inferences about the treatment difference for the benefit of patients in the general population. However, if the treatment difference is large and the endpoint of the study is potentially fatal, it does not(More)
A robust statistical method to detect linkage or association between a genetic marker and a set of distinct phenotypic traits is to combine univariate trait-specific test statistics for a more powerful overall test. This procedure does not need complex modeling assumptions, can easily handle the problem with partially missing trait values, and is applicable(More)
Gene expression array profiles identify subclasses of breast cancers with different clinical outcomes and different molecular features. The present study attempted to correlate genomic alterations (loss of heterozygosity; LOH) with subclasses of breast cancers having distinct gene expression signatures. Hierarchical clustering of expression array data from(More)
In a long term clinical trial to evaluate a new treatment, quite often each study subject may experience a number of 'failures' that correspond to repeated occurrences of the same type of event or events of entirely different natures during his/her follow-up period. To obtain efficient inference procedures for the therapeutic effect over time, it is(More)