Abstract #301044

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JSM 2003 Abstract #301044
Activity Number: 31
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #301044
Title: Sample Size Determination for Nonlinear Models
Author(s): Mohammad K. Alam*+ and Thomas E. Bradstreet and Marepalli B. Rao
Companies: North Dakota State University and Merck Research Laboratories and North Dakota State University
Address: 237 E Court University Village, Fargo, ND, 58102,
Keywords: nonlinear regression ; logistic function ; PET ; slope ; OCC90 ; sample size
Abstract:

Nonlinear regression in Positron Emission Tomography (PET) studies is used to explore the relationship between an unknown continuous response variable Y, representing percent receptor occupancy in the brain, and a lognormally distributed random variable X, representing the plasma concentration of the drug in the blood. The relevant ligand-receptor occupancy theory indicates from a deterministic point of view, that this relationship follows a four-parameter (minimum, maximum, slope, OCC90) logistic function. The OCC90 parameter is the drug plasma concentration at which 90% occupancy in the brain is achieved. To compare the brain receptor occupancy vs. drug plasma concentration relationship in two populations, a natural question is, How large of a random sample from each population should be drawn? First, we present some sample size results obtained asymptotically for one and two sample problems. Then we take a second approach where we linearize the logistic function and sample size methodologies are again determined for one sample and two sample problems. Comparisons between the two procedures are made.

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