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Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance(More)
DNA microarrays have made it possible to estimate the level of expression of thousands of genes for a sample of cells. Although biomedical investigators have been quick to adopt this powerful new research tool, accurate analysis and interpretation of the data have provided unique challenges. Indeed, many investigators are not experienced in the analytical(More)
Determining sample sizes for microarray experiments is important but the complexity of these experiments, and the large amounts of data they produce, can make the sample size issue seem daunting, and tempt researchers to use rules of thumb in place of formal calculations based on the goals of the experiment. Here we present formulae for determining sample(More)
Many gene expression studies attempt to develop a predictor of pre-defined diagnostic or prognostic classes. If the classes are similar biologically, then the number of genes that are differentially expressed between the classes is likely to be small compared to the total number of genes measured. This motivates a two-step process for predictor development,(More)
DNA microarrays are assays that simultaneously provide information about expression levels of thousands of genes and are consequently finding wide use in biomedical research. In order to control the many sources of variation and the many opportunities for misanalysis, DNA microarray studies require careful planning. Different studies have different(More)
MOTIVATION In microarray experiments investigators sometimes wish to pool RNA samples before labeling and hybridization due to insufficient RNA from each individual sample or to reduce the number of arrays for the purpose of saving cost. The basic assumption of pooling is that the expression of an mRNA molecule in the pool is close to the average expression(More)
PURPOSE A common goal of gene expression microarray studies is the development of a classifier that can be used to divide patients into groups with different prognoses, or with different expected responses to a therapy. These types of classifiers are developed on a training set, which is the set of samples used to train a classifier. The question of how(More)
BACKGROUND Tissue Microarrays (TMAs) have emerged as a powerful tool for examining the distribution of marker molecules in hundreds of different tissues displayed on a single slide. TMAs have been used successfully to validate candidate molecules discovered in gene array experiments. Like gene expression studies, TMA experiments are data intensive,(More)
Constructing a confidence interval for the actual, conditional error rate of a prediction rule from multivariate data is problematic because this error rate is not a population parameter in the traditional sense--it is a functional of the training set. When the training set changes, so does this "parameter." A valid method for constructing confidence(More)