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• W2796559070 abstract "Design and analysis of cluster randomized trials must take into account correlation among outcomes from the same clusters. When applying standard generalized estimating equations (GEE), the first-order (e.g. treatment) effects can be estimated consistently even with a misspecified correlation structure. In settings for which the correlation is of interest, one could estimate this quantity via generalized estimating equations (GEE2). We build upon GEE2 in the setting of missing data, for which we incorporate a second-order inverse-probability weighting (IPW) scheme and second-order doubly robust (DR) estimating equations that guard against partial model misspecification. We highlight the need to model correlation among missing indicators in such settings. In addition, the computational difficulties in solving these equations have motivated our development of more computationally efficient algorithms for solving GEE2, which alleviates reliance on parameter starting values and provides substantially faster and higher convergence rates than the more widely used deterministic root-solving methods." @default.
• W2796559070 created "2018-04-24" @default.
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• W2796559070 date "2018-04-16" @default.
• W2796559070 modified "2023-09-27" @default.
• W2796559070 title "A stochastic second-order generalized estimating equations approach for estimating intraclass correlation coefficient in the presence of informative missing data" @default.
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• W2796559070 hasPublicationYear "2018" @default.
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