Now showing items 1-6 of 6
Disequilibrium fine-mapping of a rare allele via coalescent models of gene ancestry
Genetic linkage studies based on pedigree data have limited resolution, due to the relatively small number of segregations. Disequilibrium mapping, which uses population associations to infer the location of a disease mutation, provides one possible strategy for narrowing the candidate region. We develop a coalescent model ...
An evaluation of saddlepoint approximations in the generalized linear model
Higher order asymptotic methods based on the saddlepoint approximation to a density provide fast and accurate approximate inference in a variety of situations. The double saddlepoint approximation to the exact conditional density and distribution function in particular is useful in generalized linear model problems which are ...
A comparison of three trough to peak estimators derived from ambulatory blood pressure data
A general multivariate model is proposed to analyze ABPM data obtained from multiple subjects in which two ABPM series, typically at baseline and on randomized therapy in a clinical trial, are obtained on each subject. The model is a specific formulation of the general repeated measures multivariate regression model in which ...
Additive hazards regression with incomplete covariate data
This dissertation addresses two incomplete covariate data problems in the additive hazards (AH) regression model for failure time data. Both are examples of two-phase designs where some covariate is measured only on a subset of the total sample. The first is the case-cohort design, where the covariates are ascertained on all ...
Study design issues in the analysis of complex genetic traits
Many common diseases that potentially have a large public health impact, such heart disease, cancer, and diabetes, are known or thought to have a genetic component to risk. These traits are often complex with multiple contributing genetic and non-genetic factors. The identification of genetic risk factors through genetic ...
A statistical model for fluorescence image cytometry
Fluorescence image cytometry is a common laboratory method used to analyze tissue and culture specimens at the cellular level. Fluorescence imaging is useful because fluorescent stains are highly specific and imaging allows for direct spatial measurements. A statistical model was developed for analysis of fluorescence images. ...