Online Program Home
My Program

Abstract Details

Activity Number: 402 - HPSS Student Paper Competition Winners: Statistics Advancing Policy
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract #328402 Presentation
Title: A Stochastic Second-Order Generalized Estimating Equations Approach for Estimating Association Parameters Under Informative Missingness
Author(s): Tom Chen* and Eric Tchetgen Tchetgen and Rui Wang
Companies: and Harvard University and Harvard Pilgrim HealthCare Institute
Keywords: Clustered data; doubly robust; GEE2; stochastic approximation

Design and analysis of cluster randomized trials must take into account correlation among outcomes from the same clusters. When applying generalized estimating equations (GEE), the first-order (e.g. treatment) effects can be estimated consistently even with a misspecified covariance structure. In settings for which the correlation is of interest, one could estimate this quantity via second-order generalized estimating equations (GEE2). However, this procedure is (1) biased under informative missing data and (2) computationally infeasible as cluster sizes grow. We first introduce a stochastic variant to fitting GEE2 which alleviates reliance on parameter starting values and provides substantially faster speeds and higher convergence rates than the standard Newton-Raphson method. Under certain conditions, the proposed method can reduce time complexity from O(n^2) to O(n), where n is the maximum cluster size. Secondly, we extend the missing data framework to GEE2, for which we incorporate a "second-order" inverse-probability weighting (IPW) and "second-order" doubly robust (DR) schemes. We highlight the need to model correlation among missingness indicators in such settings.

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

Back to the full JSM 2018 program