Title: A stochastic second-order generalized estimating equations approach for estimating association parameters.
Authors: Chen, Tom; Tchetgen, Eric J Tchetgen; Wang, Rui
Published In J Comput Graph Stat, (2020)
Abstract: Design and analysis of cluster randomized trials must take into account the intraclass correlation coefficient (ICC), which quantifies the correlation among outcomes from the same cluster. Second-order generalized estimating equations (GEE2) provides a statistically robust way in estimating this quantity and other association parameters. However, GEE2 becomes computationally infeasible as cluster sizes grow. This paper proposes a stochastic variant to fitting GEE2 which alleviates reliance on parameter starting values and provides substantially faster speeds and higher convergence rates than the widely used deterministic Newton-Raphson method. We also propose new estimators for the ICC which account for informative missing outcome data through the use of GEE2, for which we incorporate a "second-order" inverse probability weighting scheme and "second-order" doubly robust (DR) estimating equations that guard against partial model misspecification. Our proposed methods are evaluated through simulations and applied to data from a cluster randomized trial in Bangladesh evaluating the effect of different marketing interventions on the use of hygienic latrines.
PubMed ID: 33041613
MeSH Terms: No MeSH terms associated with this publication