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This post is about why screening healthy people is generally a bad idea. It is the first in a series of posts on the hazards of statistics.

There is nothing new about it: Graeme Archer recently wrote a similar piece in his Telegraph blog. But the problems are consistently ignored by people who suggest screening tests, and by journals that promote their work. It seems that it can’t be said often enough.

The reason is that most screening tests give a large number of false positives. If your test comes out positive, your chance of actually having the disease is almost always quite small. False positive tests cause alarm, and they may do real harm, when they lead to unnecessary surgery or other treatments.

Tests for Alzheimer’s disease have been in the news a lot recently. They make a good example, if only because it’s hard to see what good comes of being told early on that you might get Alzheimer’s later when there are no good treatments that can help with that news. But worse still, the news you are given is usually wrong anyway.

Consider a recent paper that described a test for "mild cognitive impairment" (MCI), a condition that may, but often isn’t, a precursor of Alzheimer’s disease. The 15-minute test was published in the Journal of Neuropsychiatry and Clinical Neurosciences by Scharre et al (2014). The test sounded pretty good. It had a specificity of 95% and a sensitivity of 80%.

Specificity (95%) means that 95% of people who are healthy will get the correct diagnosis: the test will be negative.

Sensitivity (80%) means that 80% of people who have MCI will get the correct diagnosis: the test will be positive.

To understand the implication of these numbers we need to know also the prevalence of MCI in the population that’s being tested. That was estimated as 1% of people have MCI. Or, for over-60s only, 5% of people have MCI. Now the calculation is easy. Suppose 10.000 people are tested. 1% (100 people) will have MCI, of which 80% (80 people) will be diagnosed correctly. And 9,900 do not have MCI, of which 95% will test negative (correctly). The numbers can be laid out in a tree diagram.

sig fig1

The total number of positive tests is 80 + 495 = 575, of which 495 are false positives. The fraction of tests that are false positives is 495/575= 86%.

Thus there is a 14% chance that if you test positive, you actually have MCI. 86% of people will be alarmed unnecessarily.

Even for people over 60. among whom 5% of the population have MC!, the test is gives the wrong result (54%) more often than it gives the right result (46%).

The test is clearly worse than useless. That was not made clear by the authors, or by the journal. It was not even made clear by NHS Choices.

It should have been.

It’s easy to put the tree diagram in the form of an equation. Denote sensitivity as sens, specificity as spec and prevalence as prev.

The probability that a positive test means that you actually have the condition is given by

\[\frac{sens.prev}{sens.prev + \left(1-spec\right)\left(1-prev\right) }\; \]

In the example above, sens = 0.8, spec = 0.95 and prev = 0.01, so the fraction of positive tests that give the right result is

\[\frac{0.8 \times 0.01}{0.8 \times 0.01 + \left(1 – 0.95 \right)\left(1 – 0.01\right) }\; = 0.139 \]

So 13.9% of positive tests are right, and 86% are wrong, as found in the tree diagram.

The lipid test for Alzheimers’

Another Alzheimers’ test has been in the headlines very recently. It performs even worse than the 15-minute test, but nobody seems to have noticed. It was published in Nature Medicine, by Mapstone et al. (2014). According to the paper, the sensitivity is 90% and the specificity is 90%, so, by constructing a tree, or by using the equation, the probability that you are ill, given that you test positive is a mere 8% (for a prevalence of 1%). And even for over-60s (prevalence 5%), the value is only 32%, so two-thirds of positive tests are still wrong. Again this was not pointed out by the authors. Nor was it mentioned by Nature Medicine in its commentary on the paper. And once again, NHS Choices missed the point.

Why does there seem to be a conspiracy of silence about the deficiencies of screening tests? It has been explained very clearly by people like Margaret McCartney who understand the problems very well. Is it that people are incapable of doing the calculations? Surely not. Is it that it’s better for funding to pretend you’ve invented a good test, when you haven’t? Do journals know that anything to do with Alzheimers’ will get into the headlines, and don’t want to pour cold water on a good story?

Whatever the explanation, it’s bad science that can harm people.

Follow-up

March 12 2014. This post was quickly picked up by the ampp3d blog, run by the Daily Mirror. Conrad Quilty-Harper showed some nice animations under the heading How a “90% accurate” Alzheimer’s test can be wrong 92% of the time.

March 12 2014.

As so often, the journal promoted the paper in a way that wasn’t totally accurate. Hype is more important than accuracy, I guess.

nm tweets

June 12 2014.

The empirical evidence shows that “general health checks” (a euphemism for mass screening of the healthy) simply don’t help. See review by Gøtzsche, Jørgensen & Krogsbøll (2014) in BMJ. They conclude

“Doctors should not offer general health checks to their patients,and governments should abstain from introducing health check programmes, as the Danish minister of health did when she learnt about the results of the Cochrane review and the Inter99 trial. Current programmes, like the one in the United Kingdom,should be abandoned.”

8 July 2014

Yet another over-hyped screening test for Alzheimer’s in the media. And once again. the hype originated in the press release, from Kings College London this time. The press release says

"They identified a combination of 10 proteins capable of predicting whether individuals with MCI would develop Alzheimer’s disease within a year, with an accuracy of 87 percent"

The term “accuracy” is not defined in the press release. And it isn’t defined in the original paper either. I’ve written to senior author, Simon Lovestone to try to find out what it means. The original paper says

"Sixteen proteins correlated with disease severity and cognitive decline. Strongest associations were in the MCI group with a panel of 10 proteins predicting progression to AD (accuracy 87%, sensitivity 85% and specificity 88%)."

A simple calculation, as shown above, tells us that with sensitivity 85% and specificity 88%. the fraction of people who have a positive test who are diagnosed correctly is 44%. So 56% of positive results are false alarms. These numbers assume that the prevalence of the condition in the population being tested is 10%, a higher value than assumed in other studies. If the prevalence were only 5% the results would be still worse: 73% of positive tests would be wrong. Either way, that’s not good enough to be useful as a diagnostic method.

In one of the other recent cases of Alzheimer’s tests, six months ago, NHS Choices fell into the same trap. They changed it a bit after I pointed out the problem in the comments. They seem to have learned their lesson because their post on this study was titled “Blood test for Alzheimer’s ‘no better than coin toss’ “. That’s based on the 56% of false alarms mention above.

The reports on BBC News and other media totally missed the point. But, as so often, their misleading reports were based on a misleading press release. That means that the university, and ultimately the authors, are to blame.

I do hope that the hype has no connection with the fact that Conflicts if Interest section of the paper says

"SL has patents filed jointly with Proteome Sciences plc related to these findings"

What it doesn’t mention is that, according to Google patents, Kings College London is also a patent holder, and so has a vested interest in promoting the product.

Is it really too much to expect that hard-pressed journalists might do a simple calculation, or phone someone who can do it for them? Until that happens, misleading reports will persist.

9 July 2014

It was disappointing to see that the usually excellent Sarah Boseley in the Guardian didn’t spot the problem either. And still more worrying that she quotes Dr James Pickett, head of research at the Alzheimer’s Society, as saying

These 10 proteins can predict conversion to dementia with less than 90% accuracy, meaning one in 10 people would get an incorrect result.

That number is quite wrong. It isn’t 1 in 10, it’s rather more than 1 in 2.

A resolution

After corresponding with the author, I now see what is going on more clearly.

The word "accuracy" was not defined in the paper, but was used in the press release and widely cited in the media. What it means is the ratio of the total number of true results (true positives + true negatives) to the total number of all results. This doesn’t seem to me to be useful number to give at all, because it conflates false negatives and false positives into a single number. If a condition is rare, the number of true negatives will be large (as shown above), but this does not make it a good test. What matters most to patients is not accuracy, defined in this way, but the false discovery rate.

The author makes it clear that the results are not intended to be a screening test for Alzheimer’s. It’s obvious from what’s been said that it would be a lousy test. Rather, the paper was intended to identify patients who would eventually (well, within only 18 months) get dementia. The denominator (always the key to statistical problems) in this case is the highly atypical patients that who come to memory clinics in trials centres (the potential trials population). The prevalence in this very restricted population may indeed be higher that the 10 percent that I used above.

Reading between the lines of the press release, you might have been able to infer some of thus (though not the meaning of “accuracy”). The fact that the media almost universally wrote up the story as a “breakthrough” in Alzeimer’s detection, is a consequence of the press release and of not reading the original paper.

I wonder whether it is proper for press releases to be issued at all for papers like this, which address a narrow technical question (selection of patients for trials). That us not a topic of great public interest. It’s asking for misinterpretation and that’s what it got.

I don’t suppose that it escaped the attention of the PR people at Kings that anything that refers to dementia is front page news, whether it’s of public interest or not. When we had an article in Nature in 2008, I remember long discussions about a press release with the arts graduate who wrote it (not at our request). In the end we decides that the topic was not of sufficient public interest to merit a press release and insisted that none was issued. Perhaps that’s what should have happened in this case too.

This discussion has certainly illustrated the value of post-publication peer review. See, especially, the perceptive comments, below, from Humphrey Rang and from Dr Aston and from Dr Kline.

14 July 2014. Sense about Science asked me to write a guest blog to explain more fully the meaning of "accuracy", as used in the paper and press release. It’s appeared on their site and will be reposted on this blog soon.

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