Novel statistical methodology for analyzing longitudinal biomarker data

Transition Models for Analyzing Longitudinal Data with Bivariate Mixed Ordinal and Nominal Responses

In many longitudinal studies, nominal and ordinal mixed bivariate responses are measured. In these studies, the aim is to investigate the effects of explanatory variables on these time-related responses. A regression analysis for these types of data must allow for the correlation among responses during the time. To analyze such ordinal-nominal responses, using a proposed weighting approach, an ...

Ridge regression for longitudinal biomarker data.

Technological advances facilitating the acquisition of large arrays of biomarker data have led to new opportunities to understand and characterize disease progression over time. This creates an analytical challenge, however, due to the large numbers of potentially informative markers, the high degrees of correlation among them, and the time-dependent trajectories of association. We propose a mi...

Toward improved statistical methods for analyzing Cotinine-Biomarker health association data

BACKGROUND Serum cotinine, a metabolite of nicotine, is frequently used in research as a biomarker of recent tobacco smoke exposure. Historically, secondhand smoke (SHS) research uses suboptimal statistical methods due to censored serum cotinine values, meaning a measurement below the limit of detection (LOD). METHODS We compared commonly used methods for analyzing censored serum cotinine dat...

Statistical Tools for Analyzing Water Quality Data

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...