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Title: Unbalanced cluster sizes and rates of convergence in mixed-effects models for clustered data
Authors: Van der Elst, Wim
Hermans, Lisa
Verbeke, Geert
Kenward, Michael G.
Nassiri, Vahid
Molenberghs, Geert
Issue Date: 2015
Abstract: Convergence problems often arise when complex linear mixed-effects models are fitted. Previous simulation studies (see, e.g. [Buyse M, Molenberghs G, Burzykowski T, Renard D, Geys H. The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics. 2000;1:49–67, Renard D, Geys H, Molenberghs G, Burzykowski T, Buyse M. Validation of surrogate endpoints in multiple randomized clinical trials with discrete outcomes. Biom J. 2002;44:921–935]) have shown that model convergence rates were higher (i) when the number of available clusters in the data increased, and (ii) when the size of the between-cluster variability increased (relative to the size of the residual variability). The aim of the present simulation study is to further extend these findings by examining the effect of an additional factor that is hypothesized to affect model convergence, i.e. imbalance in cluster size. The results showed that divergence rates were substantially higher for data sets with unbalanced cluster sizes – in particular when the model at hand had a complex hierarchical structure. Furthermore, the use of multiple imputation to restore ‘balance’ in unbalanced data sets reduces model convergence problems.
Notes: Van der Elst, W (reprint author), Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. wim.vanderelst@gmail.com
URI: http://hdl.handle.net/1942/20862
DOI: 10.1080/00949655.2015.1103738
ISI #: 000375482300005
ISSN: 0094-9655
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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