ProQuest Subject Index - Biostatistics at HARVARD UNIVERSITY

ProQuest^® Dissertations & Theses

The world's most comprehensive collection of dissertations and theses. Learn more...

TOP >> HARVARD UNIVERSITY >> Biostatistics

Browse Institutions

You are viewing titles for HARVARD UNIVERSITY in the subject Biostatistics available through the UMI Dissertations & Thesis Gradworks site.

Analyses of periodic observations and time series data with applications to HIV prevention and state-space models	Analysis of family studies of disease

Assessing and testing for population stratification and its impact on power of family-based association studies in the United States	Bias-reduction in variable selection and the analysis of competing risks with missing covariate values with right-censored data

Contributions to family-based association tests in candidate genes	Contributions to the theory of causal directed acyclic graphs

Creating linked, interactive views of multivariate data	Design and analysis of quantile equivalence bridging trials

Design of observational longitudinal studies	Detecting spatial clustering for discrete, censored, or longitudinal outcomes

Dose-response models for mixed dependent outcomes in developmental toxicity	Family-based association tests with longitudinal measurements

Identifying informative biological markers in high-dimensional genomic data and clinical trials	Joint modeling of longitudinal and state-change processes

Leveraging hidden correlations in high-dimensional biological data	Methodological challenges for the estimation of optimal dynamic treatment regimes from observational studies

Methodology for failure time data with missing cause of failure or dependent censoring	Multivariate analysis of non-commensurate outcomes

Nonparametric methods for inference after variable selection, comparisons of survival distributions, and random effects meta-analysis, and reporting of subgroup analyses	Novel methodologies for genetic association testing

Novel statistical modeling and selection methodologies for high dimensional genomic data	Resampling methods for high-dimensional data and nonparametric regression with applications to brain imaging, bioinformatics, and sleep/circadian medicine

Resampling-based methods for multiple hypothesis testing	Robust inference using higher order influence functions

Statistical issues in microarry data analysis: Array-to-array normalization, Empirical Bayes batch effect adjustment, and Pearson's correlation coefficient in the context of replicated experiments	Statistical issues in the design and analysis of developmental toxicity studies

Statistical methods for community-based cancer interventions and health disparities research	Statistical methods for the detection and quantification of infectious disease epidemics

Statistical models for fertility-related issues in adjuvant treatment for breast cancer	Statistical models for removing microarray batch effects and analyzing genome tiling microarrays

Stochastics and networks in genomic data	Structural equation models: Fitting, diagnostics, and applications to environmental epidemiology

Supervised and unsupervised latent class models for high-dimensional data	The analyses of array CGH data and current status data

Using functional regression models to analyze longitudinal data	Using mixed effects models to integrate high-dimensional, genomic data and an array-based analysis of the evolution of brain aging