Tuesday, April 12, 2016

A first look at meta-analyses

I’m going to confess that I had never given meta-analysis much thought before reading this section of Motulsky. I can also confess that I’m really, really glad this has never applied to my research so far.
The subject itself is conflicting. Motulsky introduces meta-analysis as a way to combine evidence from multiple studies, usually clinical trials that are used to determine the effectiveness of some therapeutic. At first read, it doesn’t sound like the worst idea—the fact is, not every study has the resources to follow and collect the thousands of patient samples necessary to determine efficacy of a treatment. Pooling many, well designed, smaller trials offers a solution for researchers interested in meta-analysis. It also offers a giant problem.
A quick google search of “meta-analysis and research bias” yields over one million results. It seems from reading this chapter, that there’s a good reason for this. Meta-analysis lends itself to publication bias, and not necessarily because people bloodthirsty, competitive, publish-or-perish, nightmare monsters. Not that this doesn’t happen, but honestly, performing a non-biased seems incredibly difficult. There’s a huge table of challenges in Motulsky’s chapter on it (seriously, check it out: p. 412 table 43.1). Some of the challenges involved in performing a meta-analysis include needing to seek out ALL relevant data, including unpublished studies and studies published in other languages.
One survey published in The BMJ states that of the 31 most recent met-analyses they examined only 9 included participant data from unpublished studies. Many of the studies included in this survey didn’t list any limitations to their analysis, which leads the authors of this particular survey to strongly caution reviewers when reading meta-analyses.
It seems like there are some ways to detect bias in your meta-analyses by the generation of funnel plots, where small sample bias can be seen as asymmetry on an axis:

As opposed to: 

But even this isn’t uncontentious.

At this point in my researching of meta-analysis, I’m genuinely glad that I have no interest in clinical efficacy of therapeutics. Honestly, I think I would stick to well written narrative review articles.  

1 comment:

  1. I recently encountered this problem in an epidemiological meta-analysis. The paper was discussing the incidence of dystonia, a rare movement disorder with some identified causal mutations but most incidences are idiopathic. The paper used about 8 clinical studies to estimate the incidence of dystonia. However, in the discussion they admitted that their estimate could be off by as much as 100-fold because incidence data derived from clinical studies are notoriously unreliable for rare, idiopathic diseases. I imagine some authors might be much less willing to acknowledge the limitations of their data, especially when the margin of error might be so high.