One-Step Generalized Estimating Equations With Large Cluster Sizes. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: Lipsitz, Stuart; Fitzmaurice, Garrett; Sinha, Debajyoti; Hevelone, Nathanael; Hu, Jim; Nguyen, Louis L.
• Is Part Of: Journal of computational and graphical statistics. Volume 26:Issue 3(2017)
• Page Start: 734
• Page End: 737
• Publication Date: 2017-07-03
• Subjects: Clustered data -- Efficient estimation -- Exchangeable correlation -- Fully iterated -- Intra-cluster correlation; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2017.1321552
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: Medical studies increasingly involve a large sample of independent clusters, where the cluster sizes are also large. Our motivating example from the 2010 Nationwide Inpatient Sample (NIS) has 8, 001, 068 patients and 1049 clusters, with average cluster size of 7627. Consistent parameter estimates can be obtained naively assuming independence, which are inefficient when the intra-cluster correlation (ICC) is high. Efficient generalized estimating equations (GEE) incorporate the ICC and sum all pairs of observations within a cluster when estimating the ICC. For the 2010 NIS, there are 92.6 billion pairs of observations, making summation of pairs computationally prohibitive. We propose a one-step GEE estimator that (1) matches the asymptotic efficiency of the fully iterated GEE; (2) uses a simpler formula to estimate the ICC that avoids summing over all pairs; and (3) completely avoids matrix multiplications and inversions. These three features make the proposed estimator much less computationally intensive, especially with large cluster sizes. A unique contribution of this article is that it expresses the GEE estimating equations incorporating the ICC as a simple sum of vectors and scalars.