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Activity Number: 279 - Nonparametric Tests and Estimations for Clustered Data: It Is Essential for Non-Normal Data
Type: Topic Contributed
Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
Sponsor: International Chinese Statistical Association
Abstract #322610 View Presentation
Title: Estimation of Rank Correlation for Clustered Data
Author(s): Bernard Rosner* and Robert Glynn
Companies: Harvard Medical School and Harvard Medical School
Keywords: clustered data ; rank correlation ; Pearson correlation ; partial correlation

It is well known that the sample correlation coefficient (R_xy) is the MLE of the Pearson correlation (?_xy) for i.i.d. bivariate normal data. However, this is not true for ophthalmologic data where X (e.g., visual acuity) and Y (e.g., visual field) are available for each eye and there is positive intraclass correlation for both X and Y in fellow eyes. In this paper, we provide a regression-based approach for obtaining the MLE of ?_xy for clustered data, which can be implemented using standard mixed effects model software. This method is extended to allow for estimation of partial correlation by controlling both X and Y for a vector ?U of other covariates. In addition, these methods can be extended to allow for estimation of rank correlation for clustered data by (a) converting ranks of both X and Y to the probit scale, (b) estimating the Pearson correlation between probit scores for X and Y, and (c) using the relationship between Pearson and rank correlation for bivariate normally distributed data. The validity of the methods in finite-sized samples is supported by simulation studies. Finally, 2 examples from ophthalmology & analgesic abuse are used to illustrate the methods.

Authors who are presenting talks have a * after their name.

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