Stats Chat

The “It’s not paranoia if..” issue

• A new initiative, Data Justice, concerned with widespread commercial data collection and analysis as a threat to privacy and equality.

• Some of the reasons that data-based decision making goes bad

• Trying to get “open” data in New Jersey: “initially refused to answer The Jersey Journal’s OPRA request because it didn’t make it on the agency’s standardized OPRA form, which wasn’t available on the NBHA website. Even after a reporter noted that in 2009 the state Supreme Court ruled standardized forms aren’t necessary, Earl wouldn’t accept a request on anything but the agency’s form.”

• Reidentification of ‘anonymised’ data: ‘When data is released after applying current ad-hoc de-identification methods, the privacy risks of re-identification are not just unknown but unknowable.’

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

• Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday March 27 2015.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of March 21 – 27 2015 inclusive.
• Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

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One way medical statisticians are trained into skepticism over their careers is seeing all the exciting ideas from excited scientists and clinicians that don’t turn out to work. Looking at old hypotheses is a good way to start. This graph is from a 1986 paper in the journal Medical Hypotheses, and the authors are suggesting pork consumption is important in multiple sclerosis, because there’s a strong correlation between rates of multiple sclerosis and pork consumption across countries:

This wasn’t a completely silly idea, but it was never anything but suggestive, for two main reasons. First, it’s just a correlation. Second, it’s not even a correlation at the level of individual people — the graph is just as strong support for the idea that having neighbours who eat pork causes multiple sclerosis. Still, dietary correlations across countries have been useful in research.

If you wanted to push this idea today, as a Twitter account claiming to be from a US medical practice did, you’d want to look carefully at the graph rather than just repeating the correlation. There are some countries missing, and other countries that might have changed over the past three decades.

In particular, the graph does not have data for Korea, Taiwan, or China. These have high per-capita pork consumption, and very low rates of multiple sclerosis — and that’s even more true of Hong Kong, and specifically of Chinese people in Hong Kong.  In the other direction, the hypothesis would imply very low levels of multiple sclerosis among US and European Jews. I don’t have data there, but in people born in Israel the rate of multiple sclerosis is moderate among those of Ashkenazi heritage and low in others, which would also mess up the correlations.

You might also notice that the journal is (or was) a little non-standard, or as it said  “intended as a forum for unconventional ideas without the traditional filter of scientific peer review”.

Most of this information doesn’t even need a university’s access to scientific journals — it’s just out on the web.  It’s a nice example of how an interesting and apparently strong correlation can break down completely with a bit more data.

I posted a version of this graph with ten years of weekly data, and Mark Stockdale pointed out there are quarterly data back to 1983 (isn’t official data wonderful?). You’ll need to click the graph to embiggen for easy viewing.

The horizontal axis is the import cost plus freight and insurance (with CPI adjustments to 2013 NZ dollars), and the vertical axis is the importer margin, which covers transport and sale costs within New Zealand, and profit. The idea is that local costs are typically slowly varying, so that short-term variation in margin tracks short-term variation in profit. The label for each year is on the data point for June.

The import cost plummeted in the early 1980s, soon followed by a drop in the importer margin. That’s presumably Rogernomics and its consequences. The cost stayed fairly stable and low in the 1990s and the margin drifted down.  Then the cost increased after 1999, with the margin staying stable. We’ve recently entered a new pattern, with margin drifting upwards.

A final note: the import cost is about the same as in 1983, and so is the retail price (in real terms). The reduction in importer margin since 1983 has been almost exactly matched by an increase in taxes, though the taxes would probably be higher under a realistic world carbon price.

The flame retardant chemicals in your phone made zebra fish “chubby”, says the caption on this photo at news.com.au. Zebra fish, as it explains, are a common model organism for medical research, so this could be relevant to people

On the other hand, as @LewSOS points out on Twitter, it doesn’t seem to be having the same effect on the model organisms in the photo.

What’s notable about the story is how much better it is than the press release, which starts out

Could your electronics be making you fat? According to University of Houston researchers, a common flame retardant used to keep electronics from overheating may be to blame.

The news.com.au story carefully avoids repeating this unsupported claim.  Also, the press release doesn’t link to the research paper, or even say where it was published (or even that it was published). That’s irritating in the media but unforgivable in a university press release.   When you read the paper it turns out the main research finding was that looking at fat accumulation in embryonic zebrafish (which is easy because they are transparent, one of their other advantages over mice) was a good indication of weight gain later in life, and might be a useful first step in deciding which chemicals were worth testing in mice.

So, given all that, does your phone or computer actually expose you to any meaningful amount of this stuff?

The compounds in question, Tetrabromobisphoneol A (TBBPA) and tetrachlorobisphenol A (TCBPA) can leach out of the devices and often end up settling on dust particles in the air we breathe, the study found.

That’s one of the few mistakes in the story: this isn’t what the study found, it’s part of the background information. In any case, the question is how much leaches out. Is it enough to matter?

The European Union doesn’t think so

The highest inhalation exposures to TBBP-A were found in the production (loading and mixing) of plastics, with 8-hour time-weighted-averages (TWAs) up to 12,216 μg/m³ . At the other end of the range, offices containing computers showed TBBP-A air concentrations of less than 0.001 μg/m³ . TBBP-A exposures at sites where computers were shredded, or where laminates were manufactured ranged from 0.1 to 75 μg/m³ .

You might worry about the exposures from plastics production, and about long-term environmental accumulations, but it looks like TBBP-A from being around a phone isn’t going to be a big contributor to obesity. That’s also what the international comparisons would suggest — South Korea and Singapore have quite a lot more smartphone ownership than Australia, and Norway and Sweden are comparable, all with much less obesity.

Q: Did you see diet soda isn’t healthier than the stuff with sugar?

A: What now?

Q: In Stuff: “If you thought diet soft drink was a healthy alternative to the regular, sugar-laden stuff, it might be time to reconsider.”

A: They didn’t compare diet soft drink to ‘the regular, sugar-laden stuff’.

Q: Oh. What did they do?

A: They compared people who drank a lot of diet soft drink to people who drank little or none, and found the people who drank a lot of it gained more weight.

Q: What did the other people drink?

A: The story doesn’t say. Nor does the research paper, except that it wasn’t ‘regular, sugar-laden’ soft drink, because that wasn’t consumed much in their study.

Q: So this is just looking at correlations. Could there have been other differences, on average, between the diet soft drink drinkers and the others?

A: Sure. For a start, there was a gender difference and an ethnicity difference. And BMI differences at the start of the study.

Q: Isn’t that a problem?

A: Up to a point. They tried to adjust these specific differences away, which will work at least to some extent. It’s other potential differences, eg in diet, that might be a problem.

Q: So the headline “What diet drinks do to your waistline” is a bit over the top?

A: Yes. Especially as this is a study only in people over 65, and there weren’t big differences in waistline at the start of the study, so it really doesn’t provide much information for younger people.

Q: Still, there’s some evidence diet soft drink is less healthy than, perhaps, water?

A: Some.

Q: Has anyone even claimed diet soft drink is healthier than water?

A: Yes — what’s more, based on a randomised trial. I think it’s fair to say there’s a degree of skepticism.

Q: Are there any randomised trials of diet vs sugary soft drinks, since that’s what the story claimed to be about?

A: Not quite. There was one trial in teenagers who drank a lot of sugar-based soft drinks. The treatment group got free diet drinks and intensive nagging for a year; the control group were left in peace.

Q: Did it work?

A: A bit. After one year the treatment group  had lower weight gain, by nearly 2kg on average, but the effect wore off after the free drinks + nagging ended. After two years, the two groups were basically the same.

Q: Aren’t dietary randomised trials depressing?

A: Sure are.

• Large-scale data cleaning: the US Social Security Administration has social security records but no death records for 6.5 million people over 112, ie, about 6.5 million more than the number of people over 112 in the world. Nearly 4000 of these people are trying to get jobs “During Calendar Years 2008 through 2011, employers made 4,024 E-Verify inquiries using 3,873 SSNs belonging to numberholders born before June 16, 1901.”

• Data mining to see whether an ordinary taxi or Uber is cheaper for a specific trip — so far just in New York, and ignoring surge pricing.

• First FDA approval of a ‘biosimilar’ drug — the analogue of ‘generic’ for biologicals. Copying a biologic treatment  such as a protein hormone or an antibody is much harder than copying a small molecule (where the patent gives the necessary details), so the makers can charge more for it: in this case, only a 30% discount relative to the brand-name version. Biosimilars will be an important issue for Pharmac in the future: its second and third biggest medication expenses are for two biologicals.

• Census at School (or, in this context, Tatauranga Ki Te Kura) was on Māori TV’s news programme Te Kāea yesterday, with StatsChat contributor Julie Middleton explaining.

Team Ratings for Round 3

The basic method is described on my Department home page.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 16 matches played, 10 of which were correctly predicted, a success rate of 62.5%.

Here are the predictions for last week’s games.

Predictions for Round 3

Here are the predictions for Round 3. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Round 6

The basic method is described on my Department home page.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 34 matches played, 21 of which were correctly predicted, a success rate of 61.8%.

Here are the predictions for last week’s games.

Predictions for Round 6

Here are the predictions for Round 6. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Today in “awful graphs about interesting data” we have this effort that I saw on Twitter, from a paper in one of the Nature Reviews journals.

As with some other recent social media examples, the first problem is that the caption isn’t part of the image and so doesn’t get tweeted. The numbers are the average number of drug candidates at each stage of research to end up with one actual drug at the end. The percentage at the bottom is the reciprocal of the number at the top, multiplied by 60%.

A lot of news coverage of research is at the ‘preclinical’ stage, or is even earlier, at the stage of identifying a promising place to look.  Most of these never get anywhere. Sometimes you see coverage of a successful new cancer drug candidate in Phase I — first human studies. Most of these never get anywhere.  There’s also a lot of variation in how successful the ‘successes’ are: the new drugs for Hepatitis C (the first column) are a cure for many people; the new Alzheimer’s drugs just give a modest improvement in symptoms.  It looks as those drugs from MRSA (antibiotic-resistant Staph. aureus) are easier, but that’s because there aren’t many really novel preclinical candidates.

It’s an interesting table of numbers, but as a graph it’s pretty dreadful. The 3-d effect is purely decorative — it has nothing to do with the represntation of the numbers. Effectively, it’s a bar chart, except that the bars are aligned at the centre and have differently-shaped weird decorative bits at the ends, so they are harder to read.

At the top of the chart,  the width of the pale blue region where it crosses the dashed line is the actual data value. Towards the bottom of the chart even that fails, because the visual metaphor of a deformed funnel requires the ‘Launch’ bar to be noticeably narrower than the ‘Registration’ bar. If they’d gone with the more usual metaphor of a pipeline, the graph could have been less inaccurate.

In the end, it’s yet another illustration of two graphical principles. The first: no 3-d graphics. The second: if you have to write all the numbers on the graph, it’s a sign the graph isn’t doing its job.