So Why ARE Most Published Research Findings False? A Way Forward

Welcome to “So Why ARE Most Published Research Findings False?”, a step by step walk through of the John Ioannidis paper “Why Most Published Research Findings Are False”. It probably makes more sense if you read this in order, so check out the intro here , Part 1  here ,Part 2  here,  Part 3 here, and Part 4 here.

Alright guys, we made it! After all sorts of math and bad news, we’re finally at the end. While the situation Ioannidis has laid out up until now sounds pretty bleak, he doesn’t let us end there. No, in this section “How Can We Improve the Situation” he ends with both hope and suggestions.  Thank goodness.

Ioannidis starts off with the acknowledgement that we will never really know which research findings are true and which are false. If we had a perfect test, we wouldn’t be in this mess to begin with. Therefore, anything we do to improve the research situation will be guessing at best. However, there are things that it seems would likely do some good. Essentially they are to improve the values of each of the “forgotten” variables in the equation that determines the positive predictive value of findings. These are:

  1. Beta/study power: Use larger studies or meta-analyses aimed at testing broad hypotheses
  2. n/multiple teams: Consider a totality of evidence or work done before concluding any one finding is true
  3. u/Bias: Register your study ahead of time, or work with other teams to register your data to reduce bias
  4. R/Pre-study Odds: Determine the pre-study odds prior to your experiment, and publish your assessment with your results

If you’ve been following along so far, none of those suggestions should be surprising to you. Let’s dive in to each though:

First, we should be using larger studies or meta-analyses that aggregate smaller studies. As we saw earlier, large sample size = higher study power -> blunts the impact of bias.  That’s a good thing. This isn’t fool proof though, as bias can still slip through and a large sample size means very tiny effect sizes can be ruled “statistically significant”. These studies are also hard to do because they are so resource intensive. Ioannidis suggests that large studies be reserved for large questions, though without a lot of guidance on how to do that.

Second, the totality of the evidence. We’ve covered a lot about false positives here, and Ioannidis of course reiterates that we should always keep them in mind. One striking finding should almost never be considered definitive, but rather compared to other similar research.

Third, steps must be taken to reduce bias. We talked about this a lot with the corollaries, but Ioannidis advocates hard that groups should tell someone else up front what they’re trying to do. This would (hopefully) reduce the tendency to say “hey, we didn’t find an effect for just the color red, but if you include pink and orange as a type of red, there’s an effect!”. Trial pre-registration gets a lot of attention in the medical world, but may not be feasible in other fields. At the very least, Ioannidis suggests that research teams share their strategy with each other up front, as a sort of “insta peer review” type thing. This would allow researchers some leeway to report interesting findings they weren’t expecting (ie “red wasn’t a factor, but good golly look at green!”) while reducing the aforementioned “well if you tweak the definition of red a bit, you totally get a significant result”.

Finally, the pre-study odds. This would be a moment up front for researchers to really assess how likely they are to find anything, and a number for others to use later to judge the research team by. Almost every field has a professional conference, and one would imagine determining pre-study odds for different lines of inquiry would be an interesting topic for many of them. Encouraging researchers to think up front about their odds of finding something interesting would be an interesting framing for everything yet to come.

None of this would fix everything, but it would certainly inject some humility and context in to the process from the get go. Science in general is supposed to be a way of objectively viewing the world and describing what you find. Turning that lens inward should be something researchers welcome, though obviously that is not always the case.

In that vein, next week I’ll be rounding up some criticisms of this paper along with my wrap up to make sure you hear the other side. Stay tuned!


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