Tuesday, October 3, 2017

Free Tools for Meta-Analysis

My boss is attending a two-day course on meta-analysis, and shared these tools with me, available through Brown School of Health:
  • The Systematic Review Data Repository - as the name suggests, this is a repository of systematic review data, so you pull out data relevant to your own systematic review as well as contribute your own data for others to use. Systematic reviews are a lot of work, so a tool that lets you build off of the work of others can help systematic reviews be performed (and their findings disseminated and used to make data-driven decisions) much more quickly
  • Abstrackr - a free, open-source tool for the citation screening process. Conducting a systematic review or meta-analysis involves an exhaustive literature review, and those citations then have to be inspected to see if they qualify to be included in the study. It isn't unusual to review 100s of studies only to include a couple dozen (or fewer). This tool lets you upload abstracts, and invite reviewers to examine abstracts for inclusion. This tool is still in beta, but they're incorporating machine learning to automate some of the screening process in the future. Plus, they use "automagically" in the description, which is one of my favorite portmanteaus.
  • Open Meta-Analyst - another free, open-source tool for conducting meta-analysis. You can work with different types of data (binary, continuous, diagnostic), conduct fixed- or random-effects models, and even use different estimation methods, like maximum likelihood or Bayesian. 
  • Open MEE - a free, open-source tool based on Open Meta-Analyst, with extra tools for ecological and evolutionary meta-analysis. This might be the tool to use in general, because it has the ability to conduct meta-regression with multiple covariates. 
I think of all of these, I'm looking forward to trying out Abstrackr the most.

And of course, there are many great meta-analysis packages for R. I'm currently working on a methods article describing how to conduct a mini meta-analysis to inform a power analysis using R tools - something I did for my dissertation, but not something everyone knows how to do. (By working, I mean I have an outline and a few paragraphs written. But I'm hoping to have more time to dedicate to it in the near future. I'm toying with the idea of spending NaNoWriMo this year on scholarly pursuits, rather than a novel.)

BTW, if you like free stuff, check out these free data science and statistics resources (and let me know if you know of any not on the list).

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