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Activity Number:
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378
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Type:
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Contributed
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Date/Time:
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Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
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Sponsor:
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General Methodology
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| Abstract - #300638 |
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Title:
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A Completely Nonparametric Approach to the Analysis of Longitudinal Data via a Set of Level-crossing Problems with Application to the Analysis of Longitudinal Microarray Experiments
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Author(s):
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Cavan Reilly*+
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Companies:
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University of Minnesota
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Address:
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Division of Biostatistics, A460 Mayo Building, MMC 303, Minneapolis, MN, 55455,
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Keywords:
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level-crossing problems ; longitudinal analysis ; microarrays ; nonparametric tests ; survival analysis
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Abstract:
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We develop a completely nonparametric method for comparing two groups on a set of longitudinal measurements. No assumptions are made about the form of the mean response function, the covariance structure, or the distributional form of disturbances around the mean response function. The idea of the method is quite simple: fix a set of levels and, for each subject, determine the first time the subject has an upcrossing and a downcrossing of this level. For each level one then computes the log rank statistic and uses the maximum in absolute value of all these statistics as the test statistic. By permuting group labels we obtain a permutation test of the hypothesis that the joint distribution of the measurements over time doesn't depend on group membership. Simulations are performed to investigate the power and it is applied to the area that motivated the method-the analysis of microarrays. In this area small sample sizes, few time points, and far too many genes to consider genuine gene level longitudinal modeling have created a need for a simple, model-free test to screen for interesting features in the data.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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