Hello,



I have a question. I worked with a study about performance on basketball.

The final data employed for the analysis were composed of 2187 cases (27
players * 81 games), having followed a filtering process. I dealt with a
balanced study design with repeated measurements given that each player was
observed the same number of games, and therefore the player was considered
as a random effect. We carried out two mixed models, a Linear Mixed Model
(LMM) through the nlme library (Pinheiro) and a Poisson mixed model with
lme4 package (Bates) .



*The reviewer  has one query about the number of cases required to satisfy
the assumptions of the modelling technique.*

I don’t know if someone knows a study to justify it for LMM and Poisson
mixed models. I remembered this paper:



Austin, P. C. (2010). Estimating multilevel logistic regression models when
the number of clusters is low: A comparison of different statistical
software procedures. International Journal of Biostatistics, 6(1).
http://www.bepress.com/ijb/vol6/iss1/16/



Where :



*“ The **estimation methods based on adaptive Gauss-Hermite approximations
to the likelihood (glmer in R) tended to have superior performance for
estimating variance components when the number of clusters was small,
compared to software procedures based on penalized quasi-likelihood.
However, only Bayesian estimation with BUGS allowed for accurate estimation
of variance components when there were fewer than 10 clusters. For all
statistica lsoftware procedures, estimation of variance components tended
to be poor when there were only five subjects per cluster, regardless of
the number of clusters.”*



However, this doesn`t applied to my example.



Do you have any suggestions to reply?



Thanks in advance.



Martí Casals.



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