Posts written by Thomas Lumley (2645)

The Herald story starts off

Commonly bought over-the-counter painkillers including ibuprofen have been linked to a significant increased risk of cardiac arrest.

The research paper is here (but paywalled).

First, it’s important to remember that “significant” in this context means “detectable” rather than “important.” The risk was higher by about 30%, but cardiac arrest is fairly rare.  With ten years of complete data from Denmark (about 5.5 million people) the researchers accumulated 30,000 cardiac arrests: that’s about five cases per ten thousand people per year.

As usual, this is observational data looking at correlations; the harmful effect, if it’s real, is too small to see reliably in clinical trials.  The researchers used a clever study design where they compared use of painkillers in a cardiac-arrest patient both with the same patient at times in the past and with different patients at the same time.  Differences between people that are constant over time (like smoking) will cancel out of the analysis; differences over time that are constant between people (like season) will also cancel out.  The design doesn’t cancel out non-constant factors like starting an exercise programme that leaves your muscles and joints sore.  It’s not unreasonable that a risk difference this small could be explained by confounding factors.

There’s something more important wrong with the story, though. You might wonder how people who have cardiac arrest get asked about their painkiller use. They didn’t; the study used prescription data.  For many of the painkillers, prescription is the only source; in particular, that’s the case for diclofenac (Voltaren), where the apparent risk increase in the study was a bit larger.

Ibuprofen, however, is available over the counter in Denmark, just as it is here. It’s available in fairly small packages, and is labelled for short-term use, just as it is here. Over-the-counter sale is what the story is basically about, but the study didn’t look at over-the-counter use at all.

From Stuff

3/2017: “New Zealand homeowners are being told to fix their interest rates now if they want to avoid a looming increase.”

Also from Stuff, all either headline or lead:

12/2016: Mortgage holders urged to fix as US interest rates rise

7/2016: Warning interest rates may not have much further to fall

3/2014: Time to fix loan on your house

1/2014: Rush to fix home loan rates before Reserve Bank acts

9/2013: “Economists say homeowners have officially missed the boat on locking in cheap fixed mortgage rates.”

4/2011: Now’s the time to fix mortgages: Tower

8/2009: Fix your mortgage before rates rise

There are good reasons to believe today’s story, but presumably there were for the past stories, too.

A new research paper from Alastair Woodward and co-workers at the University of Auckland looks at the the risks of cycling in New Zealand. Jamie Morton at the Herald has written about it.  Basically, cycling isn’t as dangerous as you probably thought: the risk of an injury severe enough to report to ACC or to go to the emergency department is about one incident per 10,000 half-hour trips.  Or, for me, about once in 25-30 years.

There are two caveats for this as a pro-cycling message.  First, there’s some selection bias: the people who currently cycle are more likely to have safe routes available than those who currently don’t cycle — bike paths really work.  So if more people started cycling with the current infrastructure the ‘safety in numbers’ effect would be reduced by the increased use of dangerous roads.

Second,  it isn’t just actual injury that’s a problem.  The research paper talks about the social context of risk perception, and how the fact that cycling is regarded as weird makes the risks seem higher, which is true and an important factor. But. One morning recently, I stopped at the traffic lights coming off Grafton Bridge, and the bus behind me didn’t.  I didn’t come that close to being hit; It’s still not a fun way to start the day.  Russell Brown, who can actually write, covers this aspect better than I can.  He concludes

Cycling is much safer than people think. But until things change, fear of cycling will keep many reasonable people off the roads.

It’s not a piechart, because the wedges don’t add up to anything, which is the only possible justification for a pie chart.  On the other hand, unlike the pizzachart it is trying to display numerical data.

Also, “51% of Americans have tried marijuana today” is presumably not the intended reading, but the graphic doesn’t make that as clear as it might.

And the source for the data isn’t a guy named Moe. That’s an abbreviation for Margin of Error.  Google suggests the source is a CBS News Poll (PDF report), but that’s from last year.

(via @seanjtaylor)

• False positives: many people who think they are allergic to penicillin actually aren’t, and so don’t need to be given broader-spectrum antibiotics (which have more impact on resistance). Ars Technica, the research paper.

• Cancer genomics researcher accused of data falsification. Long NY Times story, including very clever animation of Western blot duplication.

• A bill in the US House of Representatives wouldn’t quite let employers demand genetic data from employees, but it would let employers make employees pay not to give it. (STAT news)

• President Trump described good employment numbers under the previous government as ‘phony’.  After the first month of his government, the White House press secretary said “They may have been phony in the past, but it’s very real now”.  (via Vox)

• “Cause of death” is complicated: the BBC has a story “The biggest killer you may not know” about sepsis. The story says it “kills more people in the UK each year than bowel, breast and prostate cancer combined.” But it’s not either/or. A substantial number of sepsis deaths are due to cancer or cancer treatment.

• Cathy O’Neil on how looking harder for crimes by any group (such as immigrants) is bound to increase the crime rate — if a spurious increase wasn’t the aim, you’d need to be careful about interpreting the data.

• “There are no news stories about Obama not planning a coup, just as web pages about the Holocaust tend to take as a given that it happened.” NYmag on Google’s featured snippets

• An animated tour of data science at online clothes retailer StichFix

• Some lovely 1920s maps of Australia

It’s often hard to establish whether a correlation between two variables is cause and effect, or whether it’s due to other factors.  One technique that’s helpful for structuring one’s thinking about the problem is a causal graph: bubbles for variables, and arrows for effects.

I’ve written about the correlation between chocolate consumption and number of Nobel prizes for countries.  The ‘chocolate leads to Nobel Prizes’ hypothesis would be drawn like this:

One of several more-reasonable alternatives is that variations in wealth explain the correlation, which looks like

As another example, there’s a negative correlation between the number of pirates operating in the world’s oceans and atmospheric CO₂ concentration.  It could be that pirates directly reduce atmospheric CO₂ concentration:

but it’s perhaps more likely that both technology and wealth have changed over time, leading to greater CO₂ emissions and also to nations with the ability and motivation to suppress piracy:

The pictures are oversimplified, but they still show enough of the key relationships to help with reasoning.  In particular, in these alternative explanations, there are arrows pointing into both the putative cause and the effect. There are arrows from the same origin into both ‘chocolate’ and ‘Nobel Prizes’; there are arrows from the same origins into both ‘pirates’ and ‘CO₂‘.  Confounding — the confusion of relationships that leads to causes not matching correlations — requires arrows into both variables (or selection based on arrows out of both variables).

So, when we see a causal hypothesis like this one:

and ask if there’s “really” a gender pay gap, the answer “No” requires finding a variable with arrows into both gender and pay.  Which in your case you have not got. The pay gap really is caused by gender.

There are still interesting and important questions to be asked about mechanisms. For example, consider this graph

We’d like to know how much of the pay gap is direct underpayment, how much goes through the mechanism of women doing more childcare, and how much goes through the mechanism of occupations with more women being  paid less.  Information about mechanisms helps us think about how to reduce the gap, and what the other costs of reducing it might be.  The studies I’ve seen suggest that all three of these mechanisms do contribute, so even if you think only the direct effects matter there’s still a problem.

You can also think of all sorts of things and stuff I’ve left out of that graph, and you could put some of them back in

But you’re still going to end up with a graph where there are only arrows out of gender.  Women earn less, on average, and this is causation, not mere correlation.

• “Exploding boxplots”: although a boxplot is a lot better than just showing a mean, it’s usually worse than showing the data

• The US state of Michigan used an automated system to detect unemployment benefit fraud. Late last year, an audit of 22427 cases of fraud overturned 93% of them! Now, a class-action lawsuit has been filed (PDF), giving (a one-sided view of) more of the details.

• StatsChat has been saying for quite some time that people shouldn’t be making generalisations about road crash rates without evaluating the statistical evidence for increases or decreases.  It’s good to see someone doing the analysis: the Ministry of Transport has a big long report (PDF, from here) including (p37)[updated link]
  

  110. However, since 2013 the fatality rate has injury rate has begun to increase. We conducted statistical tests (Poisson) to see whether this increase was more than natural variation, and found strong evidence that the fatality and injury rates are actually rising.

• Fascinating blog by John Grimwade, an infographics (as opposed to data visualisation) expert (via Kieran Healy)

• “Not only does Google, the world’s preeminent index of information, tell its users that caramelizing onions takes “about 5 minutes”—it pulls that information from an article whose entire point was to tell people exactly the opposite.”  Another problem with Google’s new answer box, less serious than the claims about a communist coup in the US, but likely to be believed by more people.

The graph is from a Google Trends search for  “International Men’s Day“.

There are two peaks. In the majority of years, the larger peak is on International Women’s Day, and the smaller peak is on the day itself.

From YouGov (who seem to already be regretting it).

This obviously isn’t a pie chart, because the pieces are the same size but the numbers are different. It’s not really a graph at all; it’s an idiosyncratically organised, illustrated table.  It gets worse, though. The pizza picture itself isn’t doing any productive work in this graphic: the only information it conveys is misleading. There’s a clear impression given that particular ingredients go together, when that’s not how the questions were asked. And as the footnote says, there are a lot of popular ingredients that didn’t even make it on to the graphic.

In medical research we distinguish ‘hard’ outcomes that can be reliably and objectively measured (such as death, blood pressure, activated protein C concentrations) from ‘soft’ outcomes that depend on subjective reporting.  We also distinguish ‘patient-centered’ or ‘clinical’ or ‘real’ outcomes that matter directly to patients (such as death, pain, or dependency) from ‘surrogate’  or ‘intermediate’ outcomes that are biologically meaningful but don’t directly matter to patients.  ‘Death’ is one of the few things we can measure that’s on both lists.

Cause of death, however, is a much less ideal thing to measure.  If some form of cancer screening makes it less likely that you die of that particular type of cancer but doesn’t increase how long you live, it’s going to be less popular than if it genuinely postpones death.  What’s more surprising is that cause of death is hard to measure objectively and reliably. But it is.

Suppose someone smokes heavily for many years and as a result develops chronic lung disease, and as a result develops pneumonia, and as a result is in hospital, has a cardiac arrest due to a medical error, and dies. Is the cause of death ‘cardiac arrest’ or ‘medical error’ or ‘pneumonia’ or ‘COPD’ or ‘smoking’?  The best choice is a subjective matter of convention: what’s the most useful way to record the primary cause of death? But even with a convention in place, there’s a lot of work to make sure it is followed.  For example, a series of research papers in Thailand estimated that more than half the deaths from the main causes (eg stroke, HIV/AIDs, road traffic accidents, types of heart disease) were misclassified as less-specific causes, and came up with a way to at least correct the national totals.

In Western countries, things are better on average. However, as Radio NZ described today, in Australia (and probably NZ) deaths of people with intellectual disability tend to be reported as due to their intellectual disability rather than to whatever specific illness or injury they had.  You can see why this happens, but you should also be able to see why it’s not ideal in improving healthcare for these people.  Listen to the Radio NZ story; it’s good. If you want a reference to the open-access research paper, though, you won’t find it at Radio NZ. It’s here