JSM 2005 Online Program

Abstract #304522

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	439
Type:	Topic Contributed
Date/Time:	Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Statistics in Epidemiology
Abstract - #304522
Title:	Parametric and Nonparametric Approaches for Method Comparisons in Diagnostic Devices: Strengths and Weaknesses
Author(s):	May Mo*+ and Mojtaba Noursalehi
Companies:	Abbott Laboratories and Abbott Laboratories
Address:	Abbott Laboratories, Abbott Park, IL, 60064, United States
Keywords:	Method Comparison ; accuracy ; non-parametric ; Passing-Bablok ; Deming
Abstract:	Method comparison for accuracy in quantitative diagnostic assays often is evaluated using linear regression methods as described in CLSI Guideline EP 9-A. The regression slope and intercept are used to determine whether the investigational and reference assays yield equivalent results and to estimate the systematic bias between the two assays. The traditional least-squares regression method, however, assumes no measurement error in the reference assay, and has strict distributional assumptions that may not reflect the actual data. There are alternative linear regression methods found in literature, parametric or nonparametric, that can be applied to method comparison studies: Deming regression, Theil's method, Passing-Bablok method. What are the strengths and weaknesses of these methods? What are the data assumptions for each method? How robust is the method when an assumption is violated? Is one method superior in the context of typical method comparison data? These questions will be discussed theoretically and examined with real and simulated data.

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