scholarly journals Review of methods for handling confounding by cluster and informative cluster size in clustered data

2014 ◽  
Vol 33 (30) ◽  
pp. 5371-5387 ◽  
Author(s):  
Shaun Seaman ◽  
Menelaos Pavlou ◽  
Andrew Copas
Keyword(s):  
Cluster Size ◽  
Clustered Data ◽  
Informative Cluster Size

scholarly journals Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Menelaos Pavlou ◽  
Gareth Ambler ◽  
Rumana Z. Omar
Keyword(s):  
Risk Prediction ◽  
Cluster Size ◽  
Linear Models ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Clustered Data ◽  
Predictor Variable ◽  
Simulated Data ◽  
Predictive Ability ◽  
Informative Cluster Size

Abstract Background Clustered data arise in research when patients are clustered within larger units. Generalised Estimating Equations (GEE) and Generalised Linear Models (GLMM) can be used to provide marginal and cluster-specific inference and predictions, respectively. Methods Confounding by Cluster (CBC) and Informative cluster size (ICS) are two complications that may arise when modelling clustered data. CBC can arise when the distribution of a predictor variable (termed ‘exposure’), varies between clusters causing confounding of the exposure-outcome relationship. ICS means that the cluster size conditional on covariates is not independent of the outcome. In both situations, standard GEE and GLMM may provide biased or misleading inference, and modifications have been proposed. However, both CBC and ICS are routinely overlooked in the context of risk prediction, and their impact on the predictive ability of the models has been little explored. We study the effect of CBC and ICS on the predictive ability of risk models for binary outcomes when GEE and GLMM are used. We examine whether two simple approaches to handle CBC and ICS, which involve adjusting for the cluster mean of the exposure and the cluster size, respectively, can improve the accuracy of predictions. Results Both CBC and ICS can be viewed as violations of the assumptions in the standard GLMM; the random effects are correlated with exposure for CBC and cluster size for ICS. Based on these principles, we simulated data subject to CBC/ICS. The simulation studies suggested that the predictive ability of models derived from using standard GLMM and GEE ignoring CBC/ICS was affected. Marginal predictions were found to be mis-calibrated. Adjusting for the cluster-mean of the exposure or the cluster size improved calibration, discrimination and the overall predictive accuracy of marginal predictions, by explaining part of the between cluster variability. The presence of CBC/ICS did not affect the accuracy of conditional predictions. We illustrate these concepts using real data from a multicentre study with potential CBC. Conclusion Ignoring CBC and ICS when developing prediction models for clustered data can affect the accuracy of marginal predictions. Adjusting for the cluster mean of the exposure or the cluster size can improve the predictive accuracy of marginal predictions.

Variance estimation in tests of clustered categorical data with informative cluster size

2020 ◽  
Vol 29 (11) ◽  
pp. 3396-3408 ◽  
Author(s):  
Mary Gregg ◽  
Somnath Datta ◽  
Doug Lorenz
Keyword(s):  
Categorical Data ◽  
Cluster Size ◽  
Goodness Of Fit ◽  
Spinal Cord Injuries ◽  
Variance Estimation ◽  
Clustered Data ◽  
Outcome Variable ◽  
Data Set ◽  
Variance Estimators ◽  
Informative Cluster Size

In the analysis of clustered data, inverse cluster size weighting has been shown to be resistant to the potentially biasing effects of informative cluster size, where the number of observations within a cluster is associated with the outcome variable of interest. The method of inverse cluster size reweighting has been implemented to establish clustered data analogues of common tests for independent data, but the method has yet to be extended to tests of categorical data. Many variance estimators have been implemented across established cluster-weighted tests, but potential effects of differing methods on test performance has not previously been explored. Here, we develop cluster-weighted estimators of marginal proportions that remain unbiased under informativeness, and derive analogues of three popular tests for clustered categorical data, the one-sample proportion, goodness of fit, and independence chi square tests. We construct these tests using several variance estimators and show substantial differences in the performance of cluster-weighted tests based on variance estimation technique, with variance estimators constructed under the null hypothesis maintaining size closest to nominal. We illustrate the proposed tests through an application to a data set of functional measures from patients with spinal cord injuries participating in a rehabilitation program.

scholarly journals Within-cluster resampling for multilevel models under informative cluster size

Biometrika ◽  
2019 ◽  
Vol 106 (4) ◽  
pp. 965-972
Author(s):  
D Lee ◽  
J K Kim ◽  
C J Skinner
Keyword(s):  
Maximum Likelihood ◽  
Cluster Size ◽  
Multilevel Model ◽  
Multilevel Models ◽  
Fixed Number ◽  
Regression Coefficients ◽  
Likelihood Estimator ◽  
Correct Model ◽  
Resampling Method ◽  
Informative Cluster Size

Summary A within-cluster resampling method is proposed for fitting a multilevel model in the presence of informative cluster size. Our method is based on the idea of removing the information in the cluster sizes by drawing bootstrap samples which contain a fixed number of observations from each cluster. We then estimate the parameters by maximizing an average, over the bootstrap samples, of a suitable composite loglikelihood. The consistency of the proposed estimator is shown and does not require that the correct model for cluster size is specified. We give an estimator of the covariance matrix of the proposed estimator, and a test for the noninformativeness of the cluster sizes. A simulation study shows, as in Neuhaus & McCulloch (2011), that the standard maximum likelihood estimator exhibits little bias for some regression coefficients. However, for those parameters which exhibit nonnegligible bias, the proposed method is successful in correcting for this bias.

Marginal Analyses of Clustered Data When Cluster Size Is Informative

Biometrics ◽  
2003 ◽  
Vol 59 (1) ◽  
pp. 36-42 ◽  
Author(s):  
John M. Williamson ◽  
Somnath Datta ◽  
Glen A. Satten
Keyword(s):  
Cluster Size ◽  
Clustered Data

scholarly journals Marginal analysis of ordinal clustered longitudinal data with informative cluster size

Biometrics ◽  
2019 ◽  
Vol 75 (3) ◽  
pp. 938-949 ◽  
Author(s):  
Aya A. Mitani ◽  
Elizabeth K. Kaye ◽  
Kerrie P. Nelson
Keyword(s):  
Longitudinal Data ◽  
Cluster Size ◽  
Marginal Analysis ◽  
Informative Cluster Size
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