Speakers & Topics

  • Susan Ellenberg, FDA
    Much Ado About Placebo Controlled Trials: Sorting Out the Ethical and Scientific Issues
  • Stephen Lagakos, Harvard School of Public Health
    Analysis of Eradication Studies of Chronic Viral Infections (with Debbie Cheng)
  • Bradley Efron, Stanford University
    Bootstrap Biostat
  • Marvin Zelen, Harvard School of Public Health & Dana Farber Cancer Center (with Sandra Lee) Planning Public Health Programs for the Early Detection of Disease: Applications to Breast Cancer
  • Mitchell Gail, NIH
    A Comparison of Cohort, Case Control, and Family Study Designs for Estimation Gene Penetrance
  • Joseph Heyse, Discussant, Merck Research Laboratories

Abstracts

Much Ado About Placebo Controlled Trials: Sorting Out the Ethical and Scientific Issues

By Susan S. Ellenberg, FDA

Placebo-controlled trials (or more generally, trials in which active therapy is withheld from the control group) regularly generate controversy. Concerns about the ethics of such trials have sometimes been raised in setting of serious diseases where available therapies are non-existent or unsatisfactory, and a new experimental treatment raises hopes of improved outcomes. More recently, it has been argued that regardless of the seriousness of the disease, placebo-controlled trials are unethical if a known effective agent is available for use as an active control. It has been further argued that when known effective therapy is available, the comparison of a new therapy to placebo is of little scientific interest or value. This presentation will address the ethical concerns that have been raised and will explore scientific, regulatory and public health implications of each side of this debate.


Analysis of Eradication Studies of Chronic Viral Infections

By Stephen Lagakos, Harvard School of Public Health (Joint Research with Debbie M. Cheng)

In studies of treatments for some chronic viral infections, the objective is to estimate the probabilities of developing viral eradication and viral resistance. Complications arise as eradication is an occult event that is not directly observable and the laboratory methods used to assess eradication status result in unusual types of censored observations. We discuss nonparametric methods for the analysis of viral eradication/resistance data in these settings. We show that the unconstrained nonparametric maximum likelihood estimator of the subdistributions of eradications and resistance are obtainable in closed form. In small samples, these estimators may be inadmissible, so we also present an algorithm for obtaining the constrained MLEs based on an isotonic regression of the unconstrained MLEs. Estimators of several functionals of the eradication and resistance subdistibutions are also developed and discussed. The methods are illustrated with the results from recent hepatitis C clinical trials.


Bootstrap Biostat

By Bradley Efron, Stanford University

Real biostatistics problems, unlike their textbook cousins, can be messy and frustratingly awkward to analyze. I will discuss a few recent examples where the bootsrap helped me out of sticky inferential situations.


Planning Public Health Programs for the Early Detection of Disease: Applications to Breast Cancer

By Marvin Zelen, Harvard School of Public Health and Dana-Farber Cancer Institute (Joint Research with Sandra J. Lee)

This paper discusses stochastic models for selecting examination schedules targeted at earlier detection of chronic diseases. The general aim is to provide guidelines for public health programs in the choice of examination schedules. The main features of such schedules are: the initial age to begin the scheduled examination programs, the intervals between subsequent examinations and the number of examinations. The aim of the early detection programs is to identify individuals in the preclinical state which may result in higher cure rates or longer survival. We illustrate the applicability of our methods to scheduling examinations for female breast cancer.


A Comparison of Cohort, Case Control, and Family Study Designs for Estimation Gene Penetrance

By Mitchell Gail, NIH

Because mutations are now measurable, it is possible to estimate the risk associated with mutations using standard epidemiologic designs, as well as family study designs more common in genetic epidemiology. One such family study design, the “kin-cohort design” is based on probands who agree to be genotyped and to provide disease information on their first-degree relatives. We further consider an extension of the kin-cohort design in which a relative of the proband is also genotyped. We compare sample sizes needed to obtain population-based estimated of gene penetrance (the chance of developing disease for those with the deleterious mutation) from cohort, case-control and kin-cohort designs. We mention the strengths and weaknesses of these designs, including a serious bias that afflicts the kin-cohort design when the willingness of a potential probant to volunteer depends on the disease histories of his or her relatives.


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