Posts from April 2012 (50)

Stuff has the  headline “Crime doubles close to liquor outlets”, based on an analysis from the University of Canterbury.  Now, can we think of possible non-headline explanations for this?  Indeed we can. As the story admits, near the end

The areas with the most serious violent crime had more Maori and young males, over-represented in crime statistics, and the highest population densities.

The three spikes with the highest numbers of liquor outlets were Auckland central (447 alcohol licences), Wellington central (423) and Christchurch central (394), all of which had high crime rates.

These numbers raise the question of what sort of alcohol licenses were included.  I’d be surprised if there were 447 liquor stores in Auckland Central, but if you include pubs and licensed restaurants the numbers look more plausible. If so, we’re not talking about liquor stores at all.  The fact that the three CBD areas (all places with bans on alcohol consumption in the street)  top the list also suggests that there’s a problem with denominators: since many of the people in the CBD don’t live there, rates of crime per 1000 population  will tend to be inflated.

What is really infuriating is that the researchers actually did a better version of the analysis, but we don’t get to see it. In the last paragraph of the story, we get

Day said the correlation was weaker, but still held, when those factors were statistically removed from the equation.

So why don’t we get told the numbers that at least have a chance of meaning something, rather than the “crime doubles”?

Updated to add:  A commenter on a later post gave a link to the published paper,  and the adjustment brings relative rates of 2.4, 2.0, and 2.4 for any license, on-license and off-license, respectively, to 1.5, 1.6, and 1.4.   Also, without adjustment there is a much higher rate in for the areas closest to off-license stores, but after adjustment the elevated rate is constant out to 5km, which seems much less plausible.

Stuff is reporting on new drug tests being pushed by NZDDA

Hardy said hair testing was more accurate and effective method of detecting drug use, and it gave a history of drug or alcohol use over the previous 90 days….With urine tests more drugs were undetectable if urinalysis was carried out more than three days after use.

Since the advertised purpose of employee drug testing is to catch people who are impaired on the job, expanding the history from 3 days to 90 days surely makes the test less accurate, not more accurate.  It’s more accurate only if you don’t distinguish between on-the-job and off-the-job drug and alcohol use — like the drunk looking for his keys under the lamppost because he could see better there.

One of the key contributions of statistics to evidence-based medicine has been in forcing medical researchers to measure what they really want to affect, not what is convenient and plausibly related to it.  Drug use in the past 90 days is not the same as on-the-job impairment, and it’s probably a pretty lousy surrogate.

The Assistant Privacy Commissioner is quoted in the article as saying

“Employers should only use it where there is a genuine business need. For example, drug testing has been allowed where there are safety issues with operating machinery.

An interesting approach used by some US companies is drug testing after accidents.  A study from Princeton (PDF) found that this did reduce accidents by about 10%, though some of the reduction may have been due to under-reporting of accidents — an important tradeoff to consider.

There isn’t going to be a quick technological fix, however, and we do need some sort of regulation. As Stanford’s Keith Humphreys puts it

…we use public policy to pick the particular sort of drug problem society will have. For example, different policy environments can make it a human rights problem, an addiction problem, a crime problem, an AIDS problem, a public disorder problem etc., but no policy will produce a true ending of all of society’s problems with drugs. There are some policies that ameliorate multiple aspects of the problem, but in most cases we are faced with hard choices about what sort of problem we will have rather than a problem-free alternative.

This week, John Key announced a package of mental-health funding, including some new treatment initiatives.  For example, Whanau Ora will be piloting a whanau-based approach, initially on 40 Maori and Pacific young people.

It’s a pity that the opportunity wasn’t taken to get reliable evidence of whether the new approaches are beneficial, and by how much.  For example, there must be a lot more than 40 Maori and Pacific youth who could potentially benefit from Whanau Ora’s approach, if it is indeed better.  Rather than picking the 40 test patients by hand from the many potential participants, a lottery system would ensure that the 40 were initially comparable to those receiving the current treatment strategies.  If the youth in whanau-based care did better we would then know for sure that the approach worked, and could compare its cost and effectiveness, and decide how far to expand it.   Without a random allocation, we won’t ever be sure, and it will be a lot easier for future government cuts to remove expensive but genuinely useful programs, and leave ones that are cheaper but don’t actually work.

In some cases it’s hard to argue for randomisation, because it seems better at least to try to treat everyone.  But if we can’t treat everyone and have to ration a new treatment approach in some way, a fair and random selection is no worse than other rationing approaches and has the enormous benefit of telling us whether the treatment works.

Admittedly, statisticians are just as bad as everyone else on this issue.   As Andrew Gelman points out in the American Statistical Association’s magazine “Chance”, when we have good ideas about teaching we typically just start using them on an ad hoc selection of courses. We have, over fifty years, convinced the medical community that it is possible, and therefore important, to know whether things really work.  It would be nice if the idea spread a bit further.

Team Ratings for Round 6

Here are the team ratings prior to Round 6, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

Performance So Far

So far there have been 40 matches played, 19 of which were correctly predicted, a success rate of 47.5%.

Here are the predictions for last week’s games.

Predictions for Round 6

Here are the predictions for Round 6

Team Ratings for Week 7

Here are the team ratings prior to Week 7, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

Performance So Far

So far there have been 41 matches played, 26 of which were correctly predicted, a success rate of 63.4%.
Here are the predictions for last week’s games.

Predictions for Week 7

Here are the predictions for Week 7. The prediction is my estimated points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

The hit British TV series Downton Abbey has drawn some fire for alleged anachronisms: phrases that just don’t fit Georgian-era Britain.

Ben Schmidt has unleashed gigabytes of data on this problem, with the Google Books n-grams.  When Google digitized lots of books, it also tabulated the frequencies of words, pairs of words, triples of words, and so on, by year of publication. In two posts, Ben compares word pairs from the TV script with the Google frequencies for books published in the 1910s and the 1990s.   The comparison shows up several two-word phrases that were much less common in Downton Abbey’s historical period than they are now, but still appear in the script.  In some cases these phrases were not observed at all in written English until much later; in other cases they existed but were rare.

As a check on the process, he also looks at a genuine play from the period, George Bernard Shaw’s Heartbreak House, which passes the phrase test with flying colors.

Congratulations to Miranda Devlin for winning the Stat of the Week competition with her nomination of the Dominion Post’s “Wairarapa has best odds for tonight’s $22m Powerball”.

Her nomination has been followed by four posts on Stats Chat relating to Lotto:

• Powerball and the Kelly criterion
• Lotto silliness
• Very like a whale
• Why isn’t this libel?

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 April 6 2012.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of March 31-April 6 2012 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.

(more…)

If you’d like to comment on or debate any of this week’s Stat of the Week nominations, please do so below!

Prime News tonight (I don’t see how to link to an individual story there) reported on a ‘vaccine for heart disease’.  This is really exciting research from the Karolinska Insitute in Sweden, studying the role of the immune system in coronary artery plaque.  The previous belief was that damaged (oxidised) LDL cholesterol, which is a consequence of plaque, triggered the immune responses; the researchers showed that the immune response was to normal LDL cholesterol.  They also showed that a vaccine blocking the immune response led to reduction of white blood cell involvement and shrinkage of plaques in transgenic mice.

Prime News went on to say that a vaccine might be available within five years.  I hope this isn’t realistic. The initial human studies to show an effect on plaque could easily be done in that time, but not a trial that actually demonstrates reductions in heart attack rates.

There is lots of depressing experience in cardiovascular research with good ideas for treatments that affect a biological measurement related to heart disease, but don’t actually reduce the risk of heart attack or death, because something goes wrong.   The US FDA, who will be the primary group that researchers have in mind when designing trials, is fairly insistent on having actual evidence of clinical benefit from new treatments.  Their Cardiovascular & Renal advisory committee is one of the most tough-minded, ever since it relied on mere biological surrogates of benefit to make what was probably the worst drug approval decision in history: approving drugs to regulate heart rhythm without evidence that they actually prevented cardiac arrest.  They didn’t.