Statistical methods for analyzing immunosignatures

Statistical methods for analyzing tissue microarray data.

Tissue microarrays (TMAs) are a new high-throughput tool for the study of protein expression patterns in tissues and are increasingly used to evaluate the diagnostic and prognostic importance of biomarkers. TMA data are rather challenging to analyze. Covariates are highly skewed, non-normal, and may be highly correlated. We present statistical methods for relating TMA data to censored time-to-e...

Statistical methods for analyzing sequentially randomized trials.

In this issue of the Journal, Thall et al. present the results of a clinical trial that makes use of sequential randomization, a novel trial design that allows the investigator to study adaptive treatment strategies. Our aim is to complement this groundbreaking work by reviewing the current state of the art of statistical methods available for such analyses. Using the data collected by Thall et...

Statistical Methods for Analyzing DNA-DNA Hybridization Data

Quantitative Comparison of Statistical Methods for Analyzing Human Metabolomics Data

1Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 3Departments of Medicine & Pharmacology, University of California San Diego, La Jolla, CA 4Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 5Preventive Medicine, Department of Medicine,...

Statistical methods for analyzing ancient DNA from hominins.

In the past few years, the number of autosomal DNA sequences from human fossils has grown explosively and numerous partial or complete sequences are available from our closest relatives, Neanderthal and Denisovans. I review commonly used statistical methods applied to these sequences. These methods fall into three broad classes: methods for estimating levels of contamination, descriptive method...