Spring 2009 Meeting
Held on March 5, 2009 at the Crowne Plaza Hotel.
The Program consists of three presentations:
- Semi-parametric Joint Modeling of Longitudinal and Survival Data
Liang Li, Ph.D., Department of Quantitative Health Sciences, Cleveland Clinic
-
Advances in Machine Learning: Tree-Based Algorithms: Alternatives to
Standard Statistical Modeling CART, TreeNet/MART and Random Forests
Dan Steinberg, Ph.D., Salford Systems
-
Crossover Versus Parallel Design: Dose-Escalation Design Considerations
for First-In-Human (FIH) Studies
Zhiwu Yan, Ph.D., Abbott Laboratories
Semi-parametric Joint Modeling of Longitudinal and Survival Data by Liang Li, Ph.D., Department of Quantitative Health Sciences, Cleveland Clinic
Biographical Background
Dr. Liang Li is currently an Assistant Staff (equivalent to Assistant Professor)
of Biostatistics at the Department of Quantitative Health Sciences, Cleveland
Clinic. He received his Ph.D. in Statistics from the University of Wisconsin-
Madison in 2003. His research interests include measurement error models and
analysis of longitudinal and survival data. He has over 20 publications on
biostatistics methodology (published in journals including Biometrics,
Statistics in Medicine, and Journal of Pharmaceutical Statistics), as well as on
medical applications (published in NEJM, JAMA, Circulation, etc.).
Abstract
In many longitudinal clinical studies, the level and progression rate of
repeatedly measured biomarkers on each subject quantify the severity of the
disease and that subject's susceptibility to progression of the disease. It is
of scientific and clinical interest to relate such quantities to a later
time-to-event clinical endpoint such as patient survival. This is usually done
with a shared parameter model. In such models, the longitudinal biomarker data
and the survival outcome of each subject are assumed to be conditionally
independent given subject-level severity or susceptibility (also called frailty
in statistical terms). I will review some of the latest developments for shared
parameter models and present a new estimation method that can handle a general
class of problems in which the conditional distribution of longitudinal data is
modeled by a linear mixed-effect model, and the conditional distribution of the
survival data is given by a Cox proportional hazard model. We allow unknown
regression coefficients and timedependent covariates in both models. The
proposed estimators are maximizers of an exact correction to the joint
log-likelihood with the frailties eliminated as nuisance parameters, an idea
that originated from correction of covariate measurement error in measurement
error models. The corrected joint log likelihood is shown to be asymptotically
concave and leads to consistent and asymptotically normal estimators. Unlike
most published methods for joint modeling, the proposed estimation procedure
does not rely on distributional assumptions of the frailties. The proposed
method was studied in simulations and applied to a data set from the
Hemodialysis (HEMO) Trial. This is joint work with Tom Greene and Bo Hu.
Advances in Machine Learning: Tree-Based Algorithms: Alternatives to Standard Statistical Modeling CART, TreeNet/MART and Random Forests by Dan Steinberg, Ph.D., Salford Systems
Biographical Background
Dr. Dan Steinberg, the President of Salford Systems, founded the company
in 1982 just after receiving his Ph.D. in Economics at Harvard. He also
served as a Member of Technical Staff at AT&T Bell Laboratories and
Assistant Professor of Economics at the University of California, San Diego,
and has participated in dozens of consulting projects for Fortune 100
clients. He has been honored by the SAS User's Group International (SUGI)
and led the modeling teams that won the KDDCup 2000 and the 2002 Duke/Teradata
Churn modeling competition. Dr. Steinberg has published articles in
statistics, econometrics, computer science, and marketing journals, and has
been a featured data mining issues speaker for the American Marketing
Association, American Statistical Association, the Direct Marketing
Association and the Casualty Actuarial Society.
Abstract
Recent methodological developments using entirely new technology are
showing enormous promise. Dr. Dan Steinberg, Salford Systems' CEO, will discuss
the classic advanced data mining methodology developed by Stanford University
Professor Jerome Friedman (Statistics, and Stanford Linear Accelerator Center)
and former University of California Professor Emeritus, Leo Breiman. TreeNet/MART
and Random Forests methodologies will be presented and the application and
results of real-world analyses will be shown.
Crossover Versus Parallel Design: Dose-Escalation Design Considerations for First-In-Human (FIH) Studies by Zhiwu Yan, Ph.D., Abbott Laboratories
Biographical Background
Zhiwu Yan received his Ph.D. in mathematical statistics in 2006 from the
University of Illinois at Chicago. He joined Abbott in September 2006 and is a
Research Statistician II. His research interests include optimal design of
experiments, specializing in crossover designs.
Abstract
First-in-human studies present particular risks to human subjects. Consequently,
such studies are typically small in sample size and are conducted in a
timelagged, dose-escalation fashion. On account of the smallnumber of subjects,
it is important to select efficient designs to increase the power of statistical
analyses. Crossover designs are often recommended since they generally provide
more precise inferences about the treatment effects as compared to parallel
designs. On the other hand, various potential problems associated with crossover
designs, such as carryover effects, complicated analyses, more dropouts and
ethical issues are also reasons for which people prefer parallel designs. Due to
the restrictions on the dosing orders in an FIH study, a crossover design we
choose may lose considerable amount of power to an optimal crossover design and,
in the worst case scenario (e.g., there are carryover effects), it could even be
inferior to a parallel design. We will give discussions on this issue. Another
important statistical issue that we are going to talk about is that: in a
crossover study do we really gain power by inclusion of 3 the baseline
(pre-dose) measurements as a covariate in statistical analyses?
Last updated: 03/31/09 by: Clint Lovell
