For moderate to large sample sizes, all tests yielded pvalues close to the nominal, except when models were misspecified. The signed-rank test generally had the lowest power. Within the current context of count outcomes, the signed-rank test shows subpar power when compared with tests that are contrasted based on full data, such as the GEE. Parametric models for count outcomes such as the GLMM with a Poisson for marginal count outcomes are quite sensitive to departures from assumed parametric models. There is some small bias for all the asymptotic tests, that is,the signed-ranktest, GLMM and GEE, especially for small sample sizes. Resampling methods such as permutation can help alleviate this.
- statistics as topic
- epidemiologic methods
- investigative techniques
- analytical, diagnostic and therapeutic
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Contributors JAP directed all simulation studies, ran some of the simulation examples and helped edit and finalise the manuscript. TL helped run some of the simulation examples and drafted some parts of the manuscript. BW helped check some of the simulation study results and draft part of the simulation results. XMT helped draft and finalise the manuscript.
Funding The report was partially supported by the National Institutes of Health, Grant UL1TR001442 of CTSA funding.
Disclaimer The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Competing interests None declared.
Patient consent Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data statement No additional data are available.
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