ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2020
A stochastic second-order generalized estimating equations approach for estimating association parameters.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Network generalized estimating equations for complexly correlated data with applications to cluster randomized trials.Biostatistics (Oxford, England) · 2025Article
- Variable selection in modelling clustered data via within-cluster resampling.The Canadian journal of statistics = Revue canadienne de statistique · 2025Article
- Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes.Statistics in medicine · 2024Article
- CoCoA: conditional correlation models with association size.Biostatistics (Oxford, England) · 2023Article
- Sample size calculation for randomized trials via inverse probability of response weighting when outcome data are missing at random.Statistics in medicine · 2023Article
- Evaluating Association Between Two Event Times with Observations Subject to Informative Censoring.Journal of the American Statistical Association · 2023Article
- Stepped Wedge Cluster Randomized Trials: A Methodological Overview.World neurosurgery · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.