199726 Comparison of analytical techniques for multiple endpoints in clinical trials

Monday, November 9, 2009: 4:30 PM

Darcy Vavrek, ND, MS , Center for Outcomes Studies, Western States Chiropractic College, Portland, OR
Nancy Temkin, PhD , Department of Biostatistics, University of Washington, Seattle, WA
Background: Multiple study endpoints often present such challenges as skewed distributions, high correlation, random missingness, null treatment effects in some of the endpoints, and, in the case of studies of traumatic head injury, high numbers of deaths.

Purpose: This paper discusses strategies for analyzing data with multiple endpoints for a positive treatment effect when comparing the treatment group to the placebo group.

Methods: We explored three kinds of multiple endpoint strategies: testing endpoints individually using a Bonferroni correction (Chi-Square test, Mann-Whitney U test, Student's t-test), creating a composite outcome measure from the multiple endpoints using O'Brien's rank sum method, and incorporating all the intact endpoints into one statistical model (logistic regression, ordinal logistic regression, or linear regression) by using GEE to account for correlation among the endpoints. We used simulation to assess the power of each of these methods under various scenarios of correlation, missingness, null treatment effects, and death rates.

Results: O'Brien's rank sum composite endpoint was the method most robust to high numbers of outcome measures with high correlation, missingness, and a null effect in one or two out of four measures. Logistic regression with GEE was the method most robust to high rates of death among study subjects.

Conclusions: When the scientific question is regarding a treatment effect across all measures, then O'Brien's rank sum method is robust to the most situations. When interpretability of the results regarding a treatment effect across all measures is critical, then logistic regression with GEE may be the technique of choice.

Learning Objectives:
1.List strategies for dealing with normal multiple endpoint data. 2.Identify the types of challenges to normality a researcher may face. 3.Name strategies for dealing with non-normal multiple endpoint data. 4.Explain a power curve and how to estimate Type I and Type II error by eye. 5.Describe O’Brien’s rank sum method as a strategy for multiple endpoints. 6.Compare O’Brien’s rank sum method to other strategies for multiple endpoints using power curves and multivariate normal multiple outcome data. 7.Evaluate multiple endpoint strategies using power curves and non-normal multiple outcome data. 8.Assess and predict situations when different multiple endpoint strategies are applicable to normal and non-normal multiple outcome data.

Keywords: Biostatistics, Clinical Trials

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am the person who conducted the statistical analysis. I will be the primary author when we submit this paper for publication.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.