American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
Columbia University
Department of Biostatistics Colloquium



HETEROGENEITY OF TREATMENT EFFECTS,
N-OF-1 TRIALS, AND EVIDENCE FARMING

by

Naihua Duan, Ph.D.
Departments of Biostatistics and Psychiatry, Columbia University
Division of Biostatistics, New York State Psychiatric Institute


Abstract

Should clinical treatments be standardized or customized? This depends on the presence and magnitude of the heterogeneity of treatment effects (HTE): is the impact of the treatment uniform across patients, or does it vary across patients in important ways that should be taken into account in clinical decision making? The outcomes movement has tended to focus on the main effect for treatment, averaged across patients, as the foundation for guidelines to be followed in routine clinical practices. Most clinical studies are designed to detect the main effect for treatment, and thus are suboptimal for studying HTE. More efforts would be desirable to design and conduct multi-purpose clinical studies that provide better information about HTE. As an example, within-patient cross-over trials, a.k.a. N-of-1 trials, offer an approach to customize treatment for individual patients, and develop a knowledge base to understand HTE, for chronic conditions with reversible treatment effects. Evidence farming is another potentially valuable paradigm that attempts to engage practicing clinicians to learn from their own practice data to inform their own clinical decision-making. I will discuss the conceptual issues underlying HTE, N-of-1 trials, and evidence farming, and provide a brief synthesis of several on-going exploratory studies on these topics.


Date: Thursday, September 6, 2007
Time: 4:00 - 5:00 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Conference Room (Room 627)
New York, New York

RESERVATIONS ARE NOT REQUIRED

Refreshments will be served at 3:30 P.M. in the
Biostatistics Conference Room (Room 627).


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