I am a bioinformatistician, with my strongest background in molecular
biology. I have been trying to learn about mixed-effects to improve the
analysis of my experiments, which certainly contain random effects. I will
admit to being totally lost in the discussions regarding lack of p-value
reporting in the current versions of lmer. Furthermore, I suspect those that
need to publish to non-statistical journals will face reviewers who are
equally in the dark. Where can I find a biologist-level explanation of the
current controversy, how can I learn how to properly judge signicance from
my lmer results, and what peer-reviewed references can I steer reviewers
towards?

I understand, from other threads, that some believe a paradigm shift away
from p-values may be necessary, but I it is not clear to me what paradigm
will replace this entrenced view. I can appreciate the fact that there may
be conflicting opinions about the best equations/algorithms for determining
significance, but is there any agreement on the goal we are heading towards?

As an example, to deal with the problems of high-dimensionality of genomic
data-sets, the concept of the False Discovery Rate has proved a valuable
concept that is replacing p-values for long lists of genes. I can understand
the concept, if not some of the nuances of the tens of ways to calculate it.
What are we heading towards with lmer?

Perhaps some suggested review articles would be helpful.

Thanks,
Mark

-- 
Mark W. Kimpel MD ** Neuroinformatics ** Dept. of Psychiatry
Indiana University School of Medicine

15032 Hunter Court, Westfield, IN 46074

(317) 490-5129 Work, & Mobile & VoiceMail
(317) 663-0513 Home (no voice mail please)

******************************************************************

	[[alternative HTML version deleted]]


