Reviewer asked me why I use meta-regression as a way how to deal with heterogeneity among effect sizes instead of conducting stratified meta-analysis.
I tried to google "stratified meta-analysis" and probably the most useful explanation was:
Stratification is an effective way to deal with inherent differences
among studies and to improve the quality and usefulness of the
conclusions. An added advantage to stratification is that insight can
be gained by investigating discrepancies among strata. There are many
ways to create coherent subgroups of studies. For example, studies can
be stratified according to their “quality,” assigned by certain
scoring systems. Commonly used systems award points on the basis of
how patients were selected and randomized, the type of blinding, the
dropout rate, the outcome measurement, and the type of analysis (eg,
intention-to-treat).
Walker, E., Hernandez, A. V., & Kattan, M. W. (2008). Meta-analysis: Its strengths and limitations. Cleveland Clinic Journal of Medicine, 75(6), 431–439.
From what I understand, the I should make some scoring system for my sample of studies, and use that score as a "weight" in my meta-analytic model?
I do not like this idea. It seems to my more less objective than meta-regression
mainly because I have no criteria in my studies to make the score. (I am doing meta-analysis of ecological studies.)
May I use this as an argument in response that stratified meta-analysis will be less objective in my case?
Best Answer
Here are some suggestions for how you could respond:
I actually discuss these issues in this article:
Viechtbauer, W. (2007). Accounting for heterogeneity via random-effects models and moderator analyses in meta-analysis. Zeitschrift für Psychologie / Journal of Psychology, 215(2), 104-121.
If you are interested and cannot get hold of a copy of the article (it's in a German journal; but the article is in English), feel free to send me an e-mail (you'll find my website linked to from my profile; e-mail address can be found there).