same old question - lme4 and p-values

Here is a section that worked in Kliegl, Risse, & Laubrock (2007, J
 Exp Psychol:Human Perception and Performance, 33, 1250-1251).

 "Analysis
     Inferential statistics are based on a linear mixed-effects model
 (lme) specifying participants and items as crossed random effects.
 This analysis takes into account differences between participants and
 differences between items in a single sweep and has been shown to
 suffer substantially less loss of statistical power in unbalanced
 designs than traditional ANOVAs over participants (F1) and items (F2;
 see Baayen, in press, Pinheiro & Bates, 2000; Quen? & van den Bergh,
 2004, for simulations).
    We used the lmer program (lme4 package; Bates & Sarkar, 2006) in
 the R system for statistical computing (R Development Core Team, 2006)
 and report regression coefficients (b; absolute effect size in ms),
 standard errors (SE), and p-values for an upper-bound n of denominator
 degrees of freedom computed as n of observations minus n of fixed
 effects. As these p-values are potentially anti-conservative, we
 generated confidence intervals from the posterior distribution of
 parameter estimates with Markov Chain Monte Carlo methods, using the
 mcmcsamp program in the lme4 package with default specifications
 (e.g., n=1000 samples; locally uniform priors for fixed effects;
 locally non-informative priors for random effects). Both procedures
 yielded the same results.
    Finally, we also computed post-hoc power statistics for the
 preview and lexical status main effects and for the interaction effect
 on first fixation durations (with effect sizes similar to those
 reported earlier, e.g., Kliegl, 2007), and using lme estimates of
 between-participant, between-item, and residual variances (Gelman &
 Hill, in press). For the observed proportion of random loss of items,
 power estimates based on 1000 simulations each were around .85 for
 word n and n+2 and .59 for word n+1 (due to the higher skipping
 rate)."  (page 1251)

 Power statistics were included in response to a reviewer request. I am
 not much in favor of post-hoc power statistics; but note that here
 they are restricted to the use of estimates of random effects. For
 reviewers, we also included traditional F1- and F2-ANOVA tables; they
 are not part of the article. In other articles, it has also been
 acceptable to report coefficients, their standard errors, and their
 ratio, and to say that coefficients larger than 2 SE are interpreted
 as significant (e.g., Kliegl, 2007, J Exp Psychol: General, 136,
 530-537), that is, it is possible to leave out p-values completely.

 Corrections and improvements of the above sentences are highly welcome
 for future articles. In perspective, I think the p-value problem will
 simply go away.

 Best
 Reinhold

 PS: Would it be useful to have a site where peer-reviewed articles
 using lme4 for statistical inference are listed and, possibly,
 retrievable versions are provided?