2005
Fall 2005
Monday, October 17, 2005 at 11 am
Speaker: Professor Denise Scholtens, Department of Preventive Medicine, Northwestern University
Title: Local modeling of global interactome networks
Abstract: Accurate systems biology modeling requires a complete catalog of protein complexes and their constituent proteins. We discuss a graph theoretic/statistical algorithm for local dynamic modeling of protein complexes using data from affinity purification-mass spectrometry experiments. The algorithm readily accommodates multicomplex membership by individual proteins and dynamic complex composition, two biological realities not accounted for in existing topological descriptions of the overall protein network. A penalized likelihood approach guides the protein complex modeling algorithm. With an accurate complex membership catalog in place, systems biology can proceed with greater precision.
Monday, October 31, 2005 at 11 am
Speaker: Professor Hua Yun Chen, Department of Epidemiology & Biostatistics, University of Illinois at Chicago
Title: Approximation to locally semiparametric efficient scores in missing data problems through likelihood robustification
Abstract: In parametric/semiparametric models with missing data, the efficient estimator often cannot be obtained without additional model assumptions even if the efficient estimator has a simple form when no missing data are involved. Robins et al. proposed to find the locally efficient estimator as a compromise and showed that the locally efficient estimator have the doubly robust property when the missing data are missing at random in Rubin's sense. In practice, the approach proposed by Robins et al. to finding a locally efficient estimator can be very challenge to implement. We propose an alternative representation of the efficient score through likelihood robustification. The proposed representation is straightforward to obtain, can be applied to missing data with arbitrary missing patterns, and is amenable to computing the locally efficient score. The estimator based on the proposed representation has the doubly robust property when missing data are MAR, and only requires correct specification of the missing data mechanism model for consistency when missing data are nonignorable. Estimation and inferences on the parameters are proposed. Applications of the proposed method are illustrated by examples. The performance of the approach is examined by a simulation study.
Monday, November 14, 2005 at 11 am
Dr. Guei-Feng (Cindy) Tsai, Department of Statistics, Northwestern University
Title: Semi-nonparametric Models and Inference for High Dimensional Microarray Data
Abstract: We develop a new approach to analyze high dimensional cell-cycle microarray data with no replicates. There are two kinds of correlations for cell-cycle microarray data. Measurements are correlated within a gene, and measurements are also correlated between genes since some genes may be biologically related. The proposed procedure combines a classification method, the quadratic inference function method and nonparametric techniques for complex high dimensional data. We first perform a gene classifying analysis to classify genes into classes with similar cell-cycle patterns, including a class with no cell-cycle phenomena at all. We use genes within the same class as pseudo-replicates to build nonparametric models and inference functions. In order to incorporate correlation of longitudinal measurements, the quadratic inference function method is also applied. This approach allows us to perform chi-squared tests for testing whether the coefficients are time varying or not. This also allows us to determine whether certain genes regulate cell cycles. A real data example on cell-cycle microarray data as well as simulations are illustrated.
Friday, December 9, 2005 at 11 am
Speaker: Dr. Alex Dmitrienko, Eli Lilly
Title: Branching tests in clinical trials with multiple objectives
Abstract: This talk discusses branching multiple tests with clinical trial applications. Branching tests arise in clinical trials with hierarchically ordered multiple objectives, for example, in the context of multiple dose-control tests with logical restrictions or analysis of multiple endpoints. The proposed branching approach is based on the principle of closed testing and generalizes the serial and parallel gatekeeping approaches. The branching testing methodology will be illustrated using a clinical trial with multiple endpoints (primary, secondary and tertiary) and multiple objectives (superiority and non-inferiority testing) as well as a dose-finding trial with multiple endpoints.