Dan Quintana Profile picture
Feb 3, 2018 32 tweets 12 min read
I’ve peer reviewed A LOT of meta-analyses over the past few years. While I’ve noticed that the overall quality of meta-analyses are improving, many that I review suffer from the same issues. Here’s a thread listing the most common problems and how to avoid them
#1 Not using PRISMA reporting guidelines (or stating that PRISMA guidelines were followed, but not *actually* following them).

prisma-statement.org/Default.aspx
These guidelines provide a framework for the transparent reporting of meta-analysis, which can help the reader assess the strengths/weaknesses of a meta-analysis.
A checklist item that is rarely completed is #5: (protocol and registration). Notice the flexibility in the language, that you need to state *if* a review protocol exists, and where it can be accessed, and, if available, provide registration information.
In other words, you don’t have to pre-registered to adhere to PRISMA, but if you haven’t posted a protocol/registration you need to state this.
Obviously, meta-analyses should be pre-registered. There are HUNDREDS of potential decisions when it comes to inclusion/exclusion criteria, statistical analysis, and which moderators to include. I see a lot of meta-analyses that seem to hover around p = 0.04.
All it takes is a small tweak of inclusion/exclusion criteria to knock out a study which tips a p value to fall under 0.05…
Pre-registration guards against both conscious and unconscious biases. Another option is reporting a meta-analysis using a registered report format. Here’s a list of journals that offer this docs.google.com/spreadsheets/d…
If you’re unfamiliar with the registered report format, have a listen to the latest @hertzpodcast episode with @chrisdc77, where Chris outlines the benefits of registered reports (here’s a preview) soundcloud.com/everything-her…
PROSPERO can be used to register biomedical outcomes, but you can also post a protocol on @OSFramework or @thePeerJ preprints. Check out the PRISMA-P guidelines for reporting a protocol prisma-statement.org/Extensions/Pro…
Remember, it’s ok to deviate from your original plan. In fact, I’d be surprised if you didn’t deviate as there are many things you can’t anticipate until you perform the meta-analysis. Just report which parts of the analysis deviated and why - the key is transparency
#2 Confusing publication bias with small study bias.

Asymmetry in funnel plots should not be used to make conclusions on the risk of publication bias. Check out what Peters and colleagues have to say on this ncbi.nlm.nih.gov/pubmed/18538991
To illustrate, let’s have a look at this contour-enhanced funnel plot. This is a variation of a typical funnel plot, which is centered on 0, with the light grey region capturing p values between .1 and .05, and the dark grey region capturing p values between .05 and .01
There’s some asymmetry in this plot. But notice that there are no studies in the left hand significance contours and that “missing studies” (pink dots) probably lie within the white “non-significant” white region. This suggests that there are other sources of asymmetry
#3 Using the trim and fill procedure to make inferences

Ah, this old chestnut. This method adjusts a meta-analysis by imputing “missing” studies to increase plot symmetry. Here’s the method in action, with the white studies added in the right panel to balance out the plot
A meta-analysis with imputed studies should not be used to form conclusions – these are not real studies and should only be used as a sensitivity analysis to assess the potential impact of these HYPOTHETICAL studies on the summary effect size.
#4 Not accounting for effect size dependencies

Two effect sizes extracted from the same study are statistically dependent, which can lead to inaccuracies in summary effect size calculation.
If effect size covariance is known this can be included in models to adjust for dependency. However, without access to original datasets, the covariance between effect size estimates of included studies is rarely available, as covariances are seldom reported.
Open data would solve this, but that’s another discussion… You can try and guess covariance, but this can be a shot in the dark. One alternative is to use cluster-robust variance estimators. Check out @jepusto's “clubSandwich" #Rstats package github.com/jepusto/clubSa…
#5 Not using transparent analysis

Unlike primary research, meta-analyses can be reproduced in many cases as summary effects sizes, and a corresponding measure of variance, are typically reported in a table.
However, the researcher is required to manually extract all these values and typically make a few guesses as to how the meta-analysis was performed (e.g., which estimator was used for residual heterogeneity - this often isn’t reported)
As the majority of meta-analysis use publicaly available data, there are no privacy concerns for reporting data. Thus, data should included with the paper or posted online on @OSFramework, or somewhere similar
Despite their website using stock photos from the 90s, Comprehensive Meta-Analysis (CMA) is a good point-and-click package (if you can shell out $495 PER YEAR for the pro version). However, researchers almost never provide enough information to reproduce CMA analyses
Performing your meta-analysis in #Rstats means that you can easily share the script. Check out @wviechtb's EXCELLENT metafor package metafor-project.org/doku.php/metaf… If you have don’t know how to use #Rstats, it’s worth it just for metafor alone.
If you want to perform a reproducible point-and-click meta-analysis, you can use @kylehamilton’s MAJOR meta-analysis module in JAMOVA, and then export the syntax github.com/kylehamilton/M…
I learnt metafor by working through all the analysis examples on the metafor website and reading tons of metafor posts on stackexchage and the R Special Interest Group for Meta-Analysis mailing list stat.ethz.ch/mailman/listin…
#6 Concluding that effects differ when one effect is significant (P < 0.05) but the other is not (P > 0.05).

This often happens when comparing two summary effect sizes. You need to make a formal comparison rather then just relying on p-values.
Small quibble #1: Not adjusting forest plot axes.

If these aren’t adjusted properly, then some confidence intervals can be chopped off, which can inadvertently (or not) ‘hide’ outliers.
Small quibble #2: Not assessing and/or addressing outliers.

As far as I know there’s no gold standard test for outliers in meta-analysis (but check out the outlier function in metafor). But check out your forest plot for anything out of the ordinary.
First, is there a typo? Have you mixed up SE and SD when extracting data? Once confirming that the effect is “real”, perform a leave one out sensitivity analysis to demonstrate your outcome doesn’t rely on one outlier
Ok, I better stop now. My wife, who is due to give birth any day now, is waiting for breakfast AND I haven’t taken the dog out to pee for awhile. Let me know other common problems that I’ve missed.
A few people have been asking if there’s a way to save this thread or to cite it. @scotmorrsn was nice enough to make it into a ‘moment’ twitter.com/i/moments/9602… I also mention a few of these ideas in this paper frontiersin.org/articles/10.33…

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More from @dsquintana

Jul 24, 2018
You’ve worked hard putting together a presentation so why limit it to the people sitting in your talk?

Here are a few tips for repurposing your talk for sharing on social media
1. Add your twitter handle on your intro slide and encourage your audience to tweet. Limiting the text on your slides will also encourage tweets. If your text isn’t tweetable then there’s too much text.

Even if people are reading it, they’re not listening to you
What’s better than big blocks of text?

Images!

Use @unsplash for a huge library of free and high quality images that don’t require distracting attribution text at the bottom of your slide
Read 11 tweets
May 7, 2018
If you’re an academic you need a website so that people can easily find info about your research and publications. Here’s how to make your own website for free in an under an hour using the blogdown package in #Rstats [THREAD]
So why use blogdown? Sure, there are several free options available to start your own blog (e.g., Medium). However, you generally can’t list your publications or other information easily on these services. Also, who knows where these services will be in a few years?
There are also some great point-and-click services available (e.g., Squarespace). However, you need to pay about $10 a month for these services, and they’re generally not well suited for academic webpages.
Read 23 tweets
May 4, 2018
Every paper has open data if they present a scatterplot.

1) Download WebPlotDigitizer automeris.io/WebPlotDigitiz…
2) Load a scatterplot screenshot
3) Select each datapoint
4) Download the .CSV file with each datapoint
We used this tool in a recent meta-analysis to extract correlation coefficients from papers that didn't report coefficients (only scatterplots), which is a common issue in meta-analysis sciencedirect.com/science/articl…
We validated the use of WebPlotDigitizer in our sample by looking at studies in our meta-analysis that reported BOTH correlation coefficients and scatterplots, finding high precision
Read 6 tweets
May 3, 2018
Funnel plots are often used to assess publication bias in meta-analysis, but these plots only visualise *small study* bias, which may or may not include publication bias. Here's a guide on making contour-enhanced funnel plots in #Rstats, which better visualise publication bias
First, some background.... Publication bias is a well-known source of bias. For instance, researchers might shelve studies that aren’t statistically significant, as journals are unfortunately less likely to publish these kind of results.
Researchers might also use questionable research practices — also known as p-hacking — to nudge an effect across the line to statistical significance
Read 20 tweets
Apr 26, 2018
Here are ten things that I HAVE NOT changed my mind about in the past few years of being a scientist (thanks to @mareberl for the suggestion)
1. Meta-analysis is a useful means of synthesizing research

Meta-analysis cops a lot of flak. But like ANY statistical tool, meta-analysis needs to be correctly applied. It still sits on top of the evidence pyramid osf.io/yq59d/
2. Presentation skills are undervalued

It’s likely that one day your chances of landing a job/grant will ride on a presentation, so take EVERY OPPORTUNITY to practice. Your research won’t “speak for itself”, no matter how good it is.
Read 11 tweets
Apr 24, 2018
Here are ten things I’ve changed my mind about in the last few years of being a scientist
1. P-values are bad.

Nope. P-values are good for what they’re designed to do. Just because they’re (often) misused doesn’t mean that we should abandon them.
2. Bayes factors will save us from the misuse of p-values

No. Bayes factors *can* be useful, but they’re not always the solution to p-value limitations.
Read 13 tweets

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