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Spring 2007 Meeting

Held on March 22, 2007 at the Deerfield Embassy Suites.

The Program consisted of three presentations:

  1. How to Obtain Accurate Sample Size and Power Using nQuery Advisor
    Brian Sullivan, MS, Statistical Solutions
     
  2. Methodological Challenges In Analyzing Patient-Reported Outcomes
    Elizabeth A. Hahn, MA, Department of Preventive Medicine and Institute for Healthcare Studies, Northwestern University
     
  3. Practical Considerations in Applied Nonlinear Regression Modeling: Design, Estimation and Testing
    Timothy E. O’Brien, Ph.D., Department of Mathematics and Statistics, Loyola University of Chicago

How to Obtain Accurate Sample Size and Power Using nQuery Advisor by Brian Sullivan, MS, Statistical Solutions

Biographical Background
Brian Sullivan graduated from the University of Limerick with a Masters degree (Research) in  Statistics. The main focus of his research work was in applying a Graphical  Modeling technique to large sets of data whilst accounting for missing  values. He has been working at Statistical Solutions as a Customer Support  Technician since November 2006. He was previously graduated with a Bachelor’s degree in Applied Science and Computing from the Dublin Institute of  Technology.

Abstract
This presentation will focus on the application side of sample size calculations. Several examples will be presented for different statistical methods. These will include approaches for clinical trials, χ2 tests, ANOVA and regression. The process involved in calculating appropriate sample size will be outlined in each case. The presentation will also include a demonstration of all calculations using the sample size software nQuery Advisor.


Methodological Challenges In Analyzing Patient-Reported Outcomes by Elizabeth A. Hahn, MA, Department of Preventive Medicine and Institute for Healthcare Studies, Northwestern University

Biographical Background
Elizabeth A. Hahn, MA, is Assistant Professor, Department of Preventive Medicine and Institute for Healthcare Studies, Feinberg School of Medicine, Northwestern University; Director of Biostatistics, Center on Outcomes, Research and Education (CORE), Evanston Northwestern Healthcare; and Director of the Outcomes Measurement and Survey Core, a shared resource of the Robert H. Lurie Comprehensive Cancer Center of Northwestern University (RHLCCC). She serves on the Cancer Control and Prevention: Health Policy and Health Services Research Peer Review Committee for the American Cancer Society (ACS), and the RHLCCC peer review committee for the ACS Institutional Research Grant program.

She is a medical sociologist and biostatistician with expertise in the design, implementation, coordination and statistical analysis of international clinical trials, survey research studies and other health services research projects. She has expertise in the application and interpretation of probabilistic measurement models (item response theory) as well as statistical models based on classical test theory. She participates in workshops and symposia to discuss research design, measurement and statistical analysis issues related to self-report data, and provides consultation to international researchers in the design and analysis of clinical trials. She co-teaches a course on Survey Design and Methodology in the Feinberg School of Medicine, Masters of Public Health program.

Her research primarily involves patient-reported outcomes (PROs) in patients with cancer and other chronic illnesses, with a focus on underserved populations and health disparities. She has been principal investigator on research grants funded by AHRQ/NCI, ACS, Coleman Foundation and NHLBI, and co-investigator on research grants funded by ACS, American Heart Association, CDC, NCI, NIAMS, NHLBI and several foundations. Her research includes the development of a bilingual, multimedia Talking Touchscreen that allows patients with varying literacy levels and computer skills to self-administer PRO questionnaires.

Abstract
The analysis of patient-reported outcomes (PRO) data in an international clinical trial presents methodological, statistical and interpretive challenges. Specific strategies are needed regarding the psychometric measurement properties of self-report instruments, cross-cultural measurement equivalence, definitions of clinical significance, missing data, longitudinal modeling, and descriptions of clinically interpretable results. Development and implementation of these strategies is illustrated using the 16-country International Randomized IFN vs. STI571 (IRIS) Study of 1106 newly diagnosed patients with chronic phase chronic myeloid leukemia. The primary endpoint was the duration of progression-free survival; PROs were secondary endpoints. Crossover to the other treatment was permitted because of intolerance or lack of efficacy. The Functional Assessment of Cancer Therapy-Biologic Response Modifiers (FACT-BRM) was completed as a measure of health-related quality of life at baseline, months 1–6, 9, 12, 18, and 24 in the patient’s preferred language. The methodological issues and specific strategies developed to address them are summarized. An item response theory (IRT) measurement model was used to evaluate psychometrics, including cross-cultural comparability (three languages), and to aid in interpretation of treatment differences. A mixed effects model was chosen for the longitudinal analyses, with a pattern-mixture technique to adjust for nonignorable missing data. Crossover effects were added as a time-dependent covariate. To better understand the meaning of the PRO scores, a clinically significant treatment effect was prespecified, and a modified forest plot was used to summarize IRT responses. 1049 patients (95%) participated in the assessments. The patterns of dropout and change were quite different for the treatment arms. This study presented major methodological challenges to PRO data analysis, all of which were addressed using state-of the-science modeling techniques. The analysis plan and results may be useful for statisticians, researchers and clinicians who analyze and interpret PROs.


Practical Considerations in Applied Nonlinear Regression Modeling: Design, Estimation and Testing by Timothy E. O’Brien, Ph.D., Department of Mathematics and Statistics, Loyola University of Chicago

Biographical Background
Dr. Timothy E. O’Brien is a tenured associate professor with the graduate faculty in the Department of Mathematics and Statistics, Loyola University of Chicago.  Dr. O’Brien received his Ph.D. in Statistics from North Carolina State University in 1993.  His dissertation topic, “New Design Strategies for Parameter Estimation and Model Discrimination in Nonlinear Regression Models” focuses on optimal experimental design, generalized linear and nonlinear modeling, and computer intensive methods, with applications to drug synergy research.  Dr. O’Brien also received an M.A. in Statistics from the University of Rochester (1987), an M.A. in Mathematics from Syracuse University (1985), and a B.A. in Mathematics and Economics from Pace University (1978).  He is a member of ASA, ENAR, IASC, IASE, and ISI.

Dr. O’Brien has made several contributions to the theory and methods of optimal experimental design, particularly regarding nonlinear modeling.  Some of his publications appear (or will appear) in Biometrika, Statistica Sinica, Journal of Statistical Planning and Inference, The American Statistician, Journal of Agricultural, Biological, and Environmental Statistics, the Journal of Chemical Ecology, Computational Statistics and Data Analysis, and the Journal of Data Science.  Dr. O’Brien also published three book chapters on optimal design, robust design, and lack of fit, for nonlinear regression models, as well as several refereed conference proceedings (e.g., Proceedings of the 15th Conference on Applied Statistics in Agriculture, Proceedings of Agro-Industrie et Methodes Statistiques) and collaborative papers in refereed biomedical journals (e.g., Development, Annals of Neurology, Cell and Tissue Research), which illustrate the immediate application of his theoretical work.  Dr. O’Brien has served numerous times as a referee for top tier statistical journals, and he is frequently invited to both domestic and international conferences and universities to speak on his theoretical developments and their applications to pharmacology and pharmacokinetics.  Dr. O’Brien won a SUGI Best Contributed Paper Award for demonstrating how some of his metholdogical work on optimal designs for nonlinear regression models can be implemented with SAS®

Dr. O’Brien's previous industrial and academic work experience contributed greatly to both the direction and applications targeted for his current research activities.  For example, Dr. O’Brien spent two years as a biostatistical consultant at Janssen Pharmaceutics NV, two years as an internal statistical consultant and biostatistician at Novartis Pharma AG, and three years as an assistant statistician at Glaxo. In addition, Dr. O’Brien also provided statistical consulting services to SmithKline, Bristol Myers Squibb, Chiron, and Amgen.  Dr. O’Brien’s previous domestic academic experience includes Assistant Professor positions at Loyola University of Chicago, the University of Georgia, and Washington State University.  Internationally, Dr. O’Brien has been a Visiting Professor at both Limburgs Universitair Centrum (Belgium) and Katholieke Universiteit Leuven (Belgium), a Visiting Lecturer at the University of Natal at Pietermaritzburg (South Africa), and he was awarded two postdoctoral fellowships, one at the Universität Augsburg (Germany) and the other at INRA, Laboratorie de Biometrie (France).

Dr. O’Brien has a strong interest in and commitment to statistical and mathematical education.  He has developed and taught a wide range of theoretical and applied statistics, statistical computing, statistical programming, statistical consulting, and mathematics courses, at both the graduate and undergraduate levels, at both domestic and universities abroad.  In addition, Dr. O’Brien has taken the time to supervise more than a dozen directed reading courses with graduate students in nonlinear mixed modeling, generalized linear models, nonlinear regression, differential geometry, optimal design, multivariate statistics, survival analysis, advanced statistical inference, and drug synergy, some of which led to the students’ dissertation research.  Dr. O’Brien has been invited to conferences on teaching statistics and also to universities to share his ideas on successful teaching, and he has recently written an invited book chapter on “Innovative Methods in Undergraduate Courses Following Calculus” which is to appear in the MAA Notes series.  Perhaps the clearest demonstration of Dr. O’Brien’s commitment to education was his two-year tour with the Peace Corps in French West Africa where he taught several mathematics courses preparing lycée students for the French Boccaleureate exam and entrance into university

Abstract
Researchers often find that nonlinear regression models are more applicable for modeling various biological, physical and chemical processes than are linear ones since they tend to fit the data well and since these models (and model parameters) are more scientifically meaningful.  These researchers are thus often in a position of requiring optimal or near-optimal designs for a given nonlinear model.  A common shortcoming of most optimal designs for nonlinear models used in practical settings, however, is that these designs typically focus only on (first-order) parameter variance or predicted variance, and thus ignore the inherent nonlinear of the assumed model function.  Another shortcoming of optimal designs is that they often have only p support points, where p is the number of model parameters.  Measures of marginal curvature, first introduced in Clarke (1987) and further developed in Haines et al (2004), provide a useful means of assessing this nonlinearity.

 This talk examines the reliability of Clarke’s marginal curvature measures (vis-à-vis other curvature and nonlinearity measures) in practical settings, and introduces a design criterion that combines variance minimization with nonlinearity minimization.  These techniques, coded in the SAS® software packages (NLIN and NLMIXED procedures), are illustrated in the context of a dissolution model given in Weiss and Lansky (1998) as well as other examples.


Last updated: 03/31/09  by: Clint Lovell

 

 

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