Held on October 19, 2006 at the Renaissance Chicago North Shore.
The Program consisted of three presentations:
- Identifiability Assumptions for Missing Covariate Data in Failure Time Regression Models Paul Rathouz, Ph.D., University of Chicago
- Phase II Selection Design With Adaptive Randomization in a Limited-Resource Environment Hyung Woo Kim, Ph.D., TGRD
- Observed Confidence Levels: Theory and Application Alan M. Polansky, Ph.D., Northern Illinois University
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Identifiability Assumptions for Missing Covariate Data in Failure Time Regression Models by Paul Rathouz, Ph.D., University of Chicago
Biographical Background Dr. Paul Rathouz is currently an Associate Professor of Biostatistics in the Department of Health Studies at the University of Chicago. He has published many peer reviewed papers covering a wide range of topics in health statistics. Dr. Rathouz's statistical interests include work on nuisance parameters, estimating functions, missing data, and longitudinal data. His areas of application include aging research, childhood psychiatric disorders (especially natural history studies) and some work in environmental epidemiology. He has been a recipient of several fellowships, honors, and awards, including the James E. Grizzle Distinguished Alumnus Award, for outstanding contributions to Biostatistical methodology, consulting and/or teaching in Department of Biostatistics at the University of North Carolina at Chapel Hill. He has also been involved in organizing sessions at several ENAR/IMS meetings. Dr. Rathouz holds a Bachelors degree in Mathematics from Rice University, a Masters degree in Biostatistics from the University of North Carolina at Chapel Hill, and a PhD in Biostatistics from Johns Hopkins University.
Abstract Prospective, Methods in the literature for missing covariate data in survival models have relied on the missing at random (MAR) assumption to render regression parameters identifiable. MAR means that missingness can depend on the observed exit time, and whether or not that exit is a failure or a censoring event. By considering ways in which missingness of covariate ~ X could depend on the true but possibly censored failure time T and the true censoring time C, we attempt to identify missingness mechanisms which would yield MAR data. We find that, under various reasonable assumptions about how missingness might depend on T and/or C, additional strong assumptions are needed to obtain MAR. We conclude that MAR is difficult to justify in practical applications. One exception arises when missingness is independent of T, and C is independent of the value of the missing ~ X. As alternatives to MAR, we propose two new missingness assumptions. In one, the missingness depends on T but not on ~ C; in the other, the situation is reversed. For each, we show that the failure time model is identifiable. When missingness is independent of T, we show that the naive complete record analysis will yield a consistent estimator of the failure time distribution. When missingness is independent of C, we develop a complete-record likelihood function and a corresponding estimator for parametric failure time models. We propose analyses to evaluate the plausibility of either assumption in a particular data set, and illustrate the ideas using data from the literature on this problem.
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Phase II Selection Design With Adaptive Randomization in a Limited-Resource Environment by Hyung Woo Kim, Ph.D., TGRD
Biographical Background Dr. Hyung Woo Kim is currently working at Takeda Global Research & Development as a Senior Statistician. Before he joined Takeda, he spent six years at MD Anderson Cancer Center, Fred Hutchinson Cancer Research Center, and Bristol-Myers Squibb Company working on clinical trials in oncology. He received MS in Statistics from Iowa State University and PhD in Biostatistics from University of Texas, School of Public Health. He has published several statistical papers and co-authored many medical papers.
Abstract In clinical research where there are several competing treatments E1, E2,…, ET of interest, there is limited number of patients that can participate in clinical trials. With standard approaches of today, such as Simon’s [1], Gehan’s [2], and Fleming’s [2] multi stage design, the choice of a treatment to which patients are assigned with priority will be in question. In addition, researchers conduct these trials one at a time. In case competing treatments are tested simultaneously, they need to compete with each other to accrue the required number of patients.
We propose a design for testing a null hypothesis H0: p £ p0 against an alternative hypothesis H1: p ³ p1 for all competing treatments within one clinical trial setup. Patients are randomized to one of the competing treatments in an adaptive randomized fashion. Initially, the randomization is balanced, and it will shift in favor of treatments that are performing better. As a result, a treatment with better performance will have higher patient accrual rates and be advanced to the next level of trials sooner than other treatments.
We will restrict our attention to the case where the number of available patients, N, is limited. Using simulation, we will compare two approaches to phase II trials. One uses the standard approach of today, and the other uses adaptive assignment. We will focus our attention to the number of drugs considered, number of false positives, number of true positives, proportion of patients who respond, and the time to find a drug with the best performance.
[1] Simon, R. ‘Optimal Two-Stage Designs for Phase II Clinical Trials’, Controlled Clinical Trials, 10, 1-10 (1989) [2] Gehan, E. ‘The Determination of The Number of Patients Required in a Preliminary and a Follow-up Trial of a New Chemotherapeutic Agent’, Journal of Chronic Disease, 13, 346-353 (1961) [3] Fleming, T. ‘One-Sample Multiple Testing Procedure for Phase II Clinical Trials, Biometrics, 38, 143-151 (1982)
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Observed Confidence Levels: Theory and Application by Alan M. Polansky, Ph.D., Northern Illinois University
Biographical Background Professor Alan M. Polansky received his Ph.D. from Southern Methodist University in 1995 under the direction of Dr. William Schucany. He is currently an Associate Professor in the Division of Statistics at Northern Illinois University.
Abstract Suppose that a sample is taken from a population whose parameter is a member of a parameter space that has been divided into a countable set of possibly overlapping subsets. The problem of regions is concerned with establishing which of these subsets contains the true parameter value on the basis of the sample. The problem has many applications that include model selection and classification. A new approach to this problem is based on assigning a measure to each of the subsets.
This measure represents the amount of confidence there is that the true parameter value is in that subset. Such a measure provides a simple method to account for the inherent variability in the data and to simultaneously consider the plausibility that the parameter is within each of the regions. This talk investigates the asymptotic properties of several methods for computing such a measure for the case of a parameter vector following the smooth function model using multivariate Edgeworth expansion theory. The applicability of the results to finite samples is investigated through an empirical study, and the methods are demonstrated using an example.