JSM 2004 - Toronto

Abstract #302039

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Activity Number: 56
Type: Contributed
Date/Time: Sunday, August 8, 2004 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #302039
Title: Comparing Restricted Cubic Splines to Fractional Polynomials for Obtaining Reference Percentiles of Clinical Measurements
Author(s): Stephen D. Weigand*+ and Steven D. Edland and Clifford R. Jack, Jr. and Maria M. Shiung
Companies: Mayo Clinic and Mayo Foundation & Mayo Clinic and Mayo Clinic and Mayo Clinic
Address: Division of Biostatistics, Harwick 7, Rochester, MN, 55905-0001,
Keywords: reference percentiles ; restricted cubic splines ; fractional polynomials ; normalizing transformations
Abstract:

For quantitative clinical measurements, it is often informative to know the patient's estimated percentile relative to sample data from a "normal" or reference population. A useful method to obtain the reference percentiles, which may depend on several covariates, involves first transforming the reference sample measurements to a standard normal using a Box-Cox or other transformation. Using the quantiles of the standard normal distribution and a back-transformation, reference percentiles can be obtained. Transformation parameters can be made to vary as a function of covariates using, for example, restricted cubic splines or fractional polynomials. We compare the performance of these two modeling approaches using human brain measurements obtained from magnetic resonance imaging data. Transformations examined include the Box-Cox and exponential normal.

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