I am considering doing a meta-analysis on the reported effects of a particular therapy. I've seen it suggested that the minimum number of studies (so far I identified k=7 of them) is not in itself a hard limitation.
However, what I am more concerned about is that most of the studies that were published with this particular therapy, mostly contain single cases, which brings the total sample size across studies to a number only a little higher than the number of studies per se.
I have several doubts/questions:
1) Is overall sample size something to be taken into consideration separately from the number of studies, or are they both just inputs to the same power calculation, as per the paper cited above?
2) How does the single-case nature of some of the to-be-reviewed studies change the methodology that I would have to adopt for the meta-analysis, if they are to be combined with multi-subject studies that do population-level inference? For instance, this older study claims single-case primary studies cannot be included because "mathematics for combining multiple-subject and single-subject outcomes do not exist", whereas Jackson et al's paper from Joe_74's answer below does provide such methods!
3) Is the fact that most of the studies available are carried out by a single lab as opposed to multiple labs, a problem that can be corrected for in the statistics, or does it bias the meta-analysis in ways that are difficult to measure quantitatively?
4) Is a study's lack of a control group reason-enough for exclusion? For instance, I've encountered Cochrane reviews that justify excluding some trials because they were not a randomised controlled trial (RCT) or a controlled clinical trial (CCT)!