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, Part 4 here, and Part 5 here.

Okay people, we made it! All the way through one of the most cited research papers of all time, and we’ve all lost our faith in everything in the process. So what do we do now? Well, let’s turn the lens around on Ioannidis. What, if anything, did he miss and how do we digest this paper? I poked around for a few critiques of him, just to give a flavor. This is obviously not a comprehensive list, but it hits the major criticisms I could find.

The Title While quite a few people had no problem with the contents of Ioannidis’s paper, some took real umbrage with the title, essentially accusing it of being clickbait before clickbait had really gotten going. Additionally, since many people never read anything more than the title of a paper, a title that blunt is easily used as a mallet by anyone trying to disprove any study they chose. Interestingly, there’s apparently some question regarding whether or not Ioannidis actually wrote the title or if it was the editors at Plos Medicine, but the point stands. Given that misleading headlines and reporting are hugely blamed by many (including yours truly) for popular misunderstanding of science, that would be a fascinating irony.

Failing to reject the null hypothesis does not mean accepting the null hypothesis This is not so much a criticism of Ioannidis as it is of those who use his work to promote their own causes. There is a rather strange line of thought out there that seems to believe that life, or science, is a courtroom. Under this way of thinking, when you undermine a scientist and their hypothesis, your client is de facto not guilty. This in not true. If you somehow prove that chemotherapy is less effective than previously stated, that doesn’t actually mean that crystals cure cancer. You never prove the null hypothesis, you only fail to reject it.

The definition of bias contained more nuance In a paper written in response to the Ioannidis paper, some researchers from Johns Hopkins took umbrage with the presentation of “bias” in the paper. Their primary grouse seemed to be intent vs consequence. Ioannidis presents bias as a factor based on consequence, i.e. the way it skews the final results. They disliked this and believed bias should be based on intent, pointing out numerous ways in which things Ioannidis calls “bias” could creep in innocently. For example, if you are looking for a drug that reduces cardiac symptoms but you also find that mortality goes down for patients who take the medication, are you really not going to report that because it’s not what you were originally looking for? By the strictest definition this is “data dredging”, but is it really? Humans aren’t robots. They’re going to report interesting findings where they see them.

The effect of multiple teams This is one of the more interesting quibbles with the initial paper. Mathematically, Ioannidis proved that having multiple teams working on the same research question would increase the chances of a false result. In the same Hopkins paper, the researchers question the math behind the “multiple teams lead to more false positives” assertion. They mention that for any one study, the odds stay the same as they always have been. Ioannidis counters with an argument that boils down to “yes, if you assume those in competition don’t get more biased”.  Interestingly, later research has shown that this effect does exist and is much worse in fields where the R factor (pre-study odds) is low.

So overall, what would I say are the major criticisms or cautions around this paper that I personally will employ?

1. If you’re citing science, use scientific terms precisely. Don’t get sloppy with verbage just to make your life easier.
2. Remember, scientific best practices all feed off each other Getting a good sample size and promoting caution can reduce both overall bias and the effect of bias that does exist. The effect of multiple team testing can be partially negated by high pre-study odds. If a team or researcher employs most best practices but misses one, that may not be a death blow to their research. Look at the whole picture before dismissing the research.
3. New is exciting, but not always reliable We all like new and quirky findings, but we need to let that go. New findings are the least likely to play out later, and that’s okay. We want to cast a broad net, but for real progress we need a longer attention span.
4. Bias takes many forms When we mention “bias” we often jump right to financial motivations. But intellectual and social pressure can be bias, competing for tenure can cause bias, and confirming ones own findings can cause bias.
5. There are more ways of being wrong than there are ways of being right Every researcher wants a true finding. They really do. No one wants their life’s work undone. While some researchers may be motivated by results they like, I do truly believe that the majority of problems are caused by the whole “needle in a haystack” thing more than the “convenient truth” thing.

Alright, that wraps us up! I enjoyed this series, and may do more going forward. If you see a paper that piques your interest, let me know and I’ll look in to it.  Happy holidays everyone!

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!

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, and Part 3 here.

Alright folks, we’re almost there. We covered a lot of mathematical ground here and last week ended with a few corollaries. We’ve seen the effects of sample size, study power, effect size, pre-study odds, bias and the work of multiple teams. We’ve gotten thoroughly depressed, and we’re not done yet. There’s one more conclusion we can draw, and it’s a scary one. Ioannidis holds nothing back, and he just flat out calls this section “Claimed Research Findings May Often Be Simply Accurate Measures of the Prevailing Bias“. Okay then.

To get to this point, Ioannidis lays out a couple of things:

1. Throughout history, there have been scientific fields of inquiry that later proved to have no basis….like phrenology for example. He calls these “null fields”.
2. Many of these null fields had positive findings at some point, and in a number great enough to sustain the field.
3. Given the math around positive findings, the effect sizes in false positives due to random chance should be fairly small.
4. Therefore, large effect sizes discovered in null fields pretty much just measure the bias present in those fields….aka that “u” value we talked about earlier.

You can think about this like a coin flip. If you flip a fair coin 100 times, you know you should get about 50 heads and 50 tails. Given random fluctuations, you probably wouldn’t be too surprised if you ended up with a 47-53 split or even a 40-60 split. If you ended up with an 80-20 split however, you’d get uneasy. Was the coin really fair?

The same goes for scientific studies. Typically we look at large effect sizes as a good thing. After all, where there’s smoke there’s fire, right? However, Ioannidis points out that large effect sizes are actually an early warning sign for bias. For example, lets say you think that your coin is weighted a bit, and that you will actually get heads 55% of the time you flip it. You flip it 100 times and get 90 heads. You can react in one of 3 ways:

1. Awesome, 90 is way more than 55 so I was right that heads comes up more often!
2. Gee, there’s a 1 in 73 quadrillion chance that 90 heads would come up if this coin were fairly weighted. With the slight bias I thought was there, the chances of getting the results I did is still about 1 in 5 trillion. I must have underestimated how biased that coin was.
3. Crap. I did something wrong.

You can guess which ones most people go with. Spoiler alert: it’s not #3.

The whole “an unexpectedly large effect size should make you nervous” phenomena is counterintuitive, but I’ve actually blogged about it before. It’s what got Andrew Gelman upset about that study that found that 20% of women were changing their vote around their menstrual cycle, and it’s something I’ve pointed out about the whole 25% of men vote for Trump if they’re primed to think about how much money their wives make.  Effect sizes of that magnitude shouldn’t be cause for excitement, they should be cause for concern. Unless you are truly discovering a previously unknown and overwhelmingly large phenomena, there’s a good chance some of that number was driven by bias.

Now of course, if your findings replicate, this is all fine, you’re off the hook. However if they don’t, the largeness of your effect size is really just a measure of your own bias. Put another way, you can accidentally find a 5% vote swing that doesn’t exist just because random chance is annoying like that, but to get numbers in the 20-25% range you had to put some effort in.

As Ioannidis points out, this isn’t even a problem with individual researchers, but in how we all view science. Big splashy new results are given a lot of attention, and there is very little criticism if the findings fail to replicate at the same magnitude. This means that a researchers have nothing but incentives to make sure the effect sizes they’re seeing as big as possible. In fact Ioannidis has found (in a different paper) that about half the time the first paper published on a topic shows the most extreme value ever found. That is way more than what we would expect to see if it were up to random chance. Ioannidis argues that by figuring out exactly how far these effect sizes deviate from chance, we can actually measure the level of bias.

Again, not a problem for those researchers who replicate, but something to consider for those who don’t. We’ll get in to that next week, in our final segment: “So What Can Be Done?”.

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 and Part 2  here.

Okay, first a quick recap: Up until now, Ioannidis has spent most of the paper providing a statistical justification for considering not just study power and p values, but also made a case for including pre-study odds, bias measures, and the number of teams working on a problem as items to look at when trying to figure out if a published finding is true or not. Because he was writing a scientific paper and not a blog post, he did a lot less editorializing than I did when I was breaking down what he did. In this section he changes all that, and he goes through a point by point breakdown of what this all means with a set of 7 6 corollaries. The words here in bold are his, but I’ve simplified the explanations. Some of this is a repeat from the previous posts, but hey, it’s worth repeating.

Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. In part 1 and part 2, we saw a lot of graphs that showed good study power had a huge effect on result reliability. Larger sample sizes = better study power.

Corollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true. This is partially just intuitive, but also part of the calculation for study power. Larger effect sizes = better study power. Interestingly, Ioannidis points out here that given all the math involved, any field looking for effect sizes smaller than 5% is pretty much never going to be able to confirm their results.

Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true. That R value we talked about in part 1 is behind this one. Pre-study odds matter, and fields that are generating new hypotheses or exploring new relationships are always going to have more false positives than studies that replicate others or meta-analyses.

Corollary 4: The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true. This should be intuitive, but it’s often forgotten. I work in oncology, and we tend to use a pretty clear cut end point for many of our studies: death. Our standards around this are so strict that if you die in a car crash less than 100 days after your transplant, you get counted in our mortality statistics. Other fields have more wiggle room. If you are looking for mortality OR quality of life OR reduced cost OR patient satisfaction, you’ve quadrupled your chance of a false positive.

Corollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true. This one’s pretty obvious. Worth noting: he points out “trying to get tenure” and “trying to preserve ones previous findings” are both sources of potential bias.

Corollary 6: The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true. This was part of our discussion last week. Essentially it’s saying that if you have 10 people with tickets to a raffle, the chances that one of you wins is higher than the chances that you personally win. If we assume 5% of positive findings happen due to chance, having multiple teams work on a question will inevitably lead to more false positives.

Both before and after listing these 6 things out, Ioannidis reminds us that none of these factors are independent or isolated. He gives some specific examples from genomics research, but then also gives this helpful table.  To refresh your memory, the 1-beta column is study power (influenced by sample size and effect size), R is the pre-study odds (varies by field), u is bias, and the “PPV” column over on the side there is the chance that a paper with a positive finding is actually true. Oh, and “RCT” is “Randomized Control Trial”:

I feel a table of this sort should hang over the desk of every researcher and/or science enthusiast.

Now all this is a little bleak, but we’re still not entirely at the bottom. We’ll get to that next week.

Part 4 is up! Click here to read it.