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Fall 2008 Meeting

Held on October 16, 2008 at the Radisson Hotel Northbrook.

The Program consisted of three presentations:

  1. Some Clinical Trial Design Questions and Answers 
    Peter A. Lachenbruch, Ph.D., Oregon State University
     
  2. Clinical Trials with Dropout: Longitudinal Assessment of Chronic Pain
    Ronald A. Thisted, Ph.D., University of Chicago
     

  3. Bayesian Distributed Lag Models: Estimating Effects of Particulate Matter Air Pollution on Daily Mortality
    Leah Welty, Ph.D., Northwestern University


Some Clinical Trial Design Questions and Answers by Peter A. Lachenbruch, Ph.D., Oregon State University

Biographical Background
Dr. Peter Lachenbruch received his Ph. D. from UCLA in Biostatistics. He has held positions on the faculties of the University of North Carolina (1965-1976), the University of Iowa (1976-1985), and UCLA (1985-1984). He was employed by the FDA/CBER from 1994 to 2005 and recently retired as the Director of the Division of Biostatistics. He is currently Professor of Public Health at Oregon State University (2006– present). He is a Fellow of the American Statistical Association and a former elected member of the International Statistical Institute. He has held many professional offices and is the President of the American Statistical Association for 2008. 

He has statistical interests in Discriminant Analysis, Two-part Models, Model-Independent Inference, Statistical Computing, and Data Analysis. He has application interests in Rheumatology, Psychiatry, Pediatrics, Gerontology and Accident Epidemiology. He has more than 180 publications in these fields. Dr. Lachenbruch serves on the Editorial Boards of Statistics in Medicine, Methods of Information in Medicine, Journal of Biopharmaceutical Statistics, and Statistical Methods in Medical Research. He served on advisory panels to the George Mason University Department of Statistics, the Ohio State University Department of Statistics, Cytel. He serves on the DSMB to a VA clinical trial, an NIH clinical trial and on the OSMB for the Women’s Health Initiative.

Abstract
Many questions arise in clinical trials by those who are just beginning, and, of course, some gaps are in each of our knowledge bases. This talk is in response to questions I’ve fielded over several years. Some of the answers represent my opinions and some the experience I’ve had at the FDA and elsewhere. If you are preparing a submission for the FDA, you should rely on their insights. Here are the questions: 

• What pitfalls do the FDA see when information from preclinical data (or early clinical data on a similar investigational product) is formulated into a Phase I protocol?
• What are the various types of study designs available when there are more than two comparators?
• What controls are appropriate when there cannot be any blinding in the trial?
• What are the problems seen by the FDA with randomization in trials?
• What are the steps in designing a dose-ranging/dose-escalation study?
• How does one deal with multiple variables that will affect the outcome measure (with an understanding of fixed randomization schemes and adaptive/dynamic randomization schemes)?
• What factors are used to estimate sample size?
• How does the investigator choose the margin of equivalence or non-inferiority (delta or Δ) in comparative clinical trials?
• How does the investigator deal with missing data?
• How does an investigator determine what data should and should not be included in analyses (especially in the cases of protocol violations, withdrawals and drop-outs)?
• What types of analyses should be
used when there are multiple time points (multiple observations) of data collection (and there is no dichotomous outcome / endpoint)?
• In analyzing data from clinical trials involving multiple sites, should site be treated as a fixed or random effect? 
• When should a planned interim analysis (for safety and/or efficacy and/or sample size reestimation) be appropriate? What are the pros and cons? What are the methods used?


Clinical Trials with Dropout: Longitudinal Assessment of Chronic Pain by Ronald A. Thisted, Ph.D., University of Chicago

Biographical Background
Ronald Thisted received his Ph.D. in Statistics from Stanford in 1977. He has been on the faculty in the Department of Statistics at the University of Chicago since 1976. Since 1999 he has also been Chairman of the Department of Health Studies, which covers the areas of biostatistics, epidemiology, and health services research. He is active in the design and analysis of clinical trials, with an emphasis on Phase III studies. Since 1979 he has consulted with the pharmaceutical industry on study design and analysis methods, and he has served as an expert witness in pharmaceutical patent litigation. In addition to methodological issues related to clinical trials, his research also includes computational methods for statistics, statistical studies of authorship, and observational methods.

Abstract
Some patients in long-term treatment trials withdraw from the study before their participation is scheduled to end, so that outcome data for a subset of patients is incomplete. Methods for handling missing data include analysis only of complete cases, ad hoc approaches to imputation such as carrying forward baseline or most recent observations, and model-based methods such as mixed effects models. In the context of evaluating treatments for chronic pain, regulators have suggested that replacing missing outcome data with patients' baseline values—the baseline carried- forward (BCF) algorithm—leads to a conservative estimate of treatment effect. Many factors can lead to dropout, including side effects, disease remission or exacerbation, inability to comply with study requirements, unrelated illness, or death. We examine how the validity of estimates and tests for treatment effects for several missing-data methods are affected by underlying models for pain response and by the factors that lead to dropout. We examine the BCF algorithm in some detail, focusing on validity, conservativeness, sample size implications, pitfalls, and alternatives.


Bayesian Distributed Lag Models: Estimating Effects of Particulate Matter Air Pollution on Daily Mortality by Leah J. Welty, Ph.D., Northwestern University

Biographical Background
Assistant Professor, Department of Preventive Medicine, Northwestern University. Postdoc, Dept of Biostatistics, Johns Hopkins University. PhD from the University of Chicago, 2003

Abstract
A distributed lag model (DLM) is a regression model that includes lagged exposure variables as covariates; its corresponding distributed lag (DL) function describes the relationship between the lag and the coefficient of the lagged exposure variable. DLMs have recently been used in environmental epidemiology for quantifying the cumulative effects of weather and air pollution on mortality and morbidity. Standard methods for formulating DLMs include unconstrained, polynomial, and penalized spline DLMs. These methods may fail to take full advantage of prior information about the shape of the DL function for environmental exposures, or for any other exposure with effects that are believed to smoothly approach zero as lag increases, and are therefore at risk of producing sub-optimal estimates. We propose a Bayesian DLM (BDLM) that incorporates prior knowledge about the shape of the DL function and also allows the degree of smoothness of the DL function to be estimated from the data. In a simulation study, we compare our Bayesian approach with alternative methods that use unconstrained, polynomial and penalized spline DLMs. We also show that BDLMs encompass penalized spline DLMs: under certain assumptions, imposing a prior on the DL coefficients is analogous to smoothing the DL coefficients with a penalty specified by the prior. We apply our BDLM to data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) to estimate the short term health effects of particulate matter air pollution on mortality from 1987-2000 for Chicago, Illinois.


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

 

 

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