September 30, 2015

Currie Cup Predictions for Round 9

Team Ratings for Round 9

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.

Current Rating Rating at Season Start Difference
Lions 5.79 3.04 2.70
Western Province 5.32 4.93 0.40
Blue Bulls 1.91 0.17 1.70
Sharks 1.09 3.43 -2.30
Cheetahs -2.37 -1.75 -0.60
Pumas -6.95 -6.47 -0.50
Griquas -9.19 -7.81 -1.40
Kings -9.50 -9.44 -0.10

 

Performance So Far

So far there have been 32 matches played, 23 of which were correctly predicted, a success rate of 71.9%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Sharks vs. Blue Bulls Sep 25 13 – 17 3.40 FALSE
2 Cheetahs vs. Lions Sep 26 31 – 73 -3.10 TRUE
3 Western Province vs. Pumas Sep 26 50 – 19 15.00 TRUE
4 Kings vs. Griquas Sep 26 40 – 37 3.20 TRUE

 

Predictions for Round 9

Here are the predictions for Round 9. 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.

Game Date Winner Prediction
1 Lions vs. Western Province Oct 02 Lions 4.00
2 Blue Bulls vs. Kings Oct 02 Blue Bulls 14.90
3 Pumas vs. Cheetahs Oct 03 Cheetahs -1.10
4 Griquas vs. Sharks Oct 03 Sharks -6.80

 

September 29, 2015

When variation is the story

A familiar trope of alternative cancer therapy is the patients who were given just months to live and are still alive years later.  The implication is that their survival is surprising and the cancer therapy was responsible.  Falling foul of the post hoc ergo propter hoc fallacy isn’t the big problem here. The big problem is that it’s not surprising that some people live a lot longer than the median.

Our intuition for variation is developed on measurements that aren’t like cancer survival. Most adults are pretty close to the average height — very few are more than a foot taller or shorter. Most people in Western countries die at close to the average age: for example, by the NZ life tables, the median life expectancy for NZ men born now is 81 years, and the tables predict half will die between 73 and 88 years.

For many types of cancer, survival isn’t like that. Here’s a graph from a big Canadian study of breast cancer

F1.large

The median survival for women with stage IV cancer in this group is about a year; half of them are still alive after a year. About half of those are still alive after two years; about half of those are still alive after three years, and some live much longer.

Variation in cancer survival — the long tail — should be welcome, but not surprising. Some people will be alive three, four, five or more years after ‘being given a year to live’.  We should be just as cautious about crediting them with finding a cure as we should be about blaming those who died sooner.

 

September 28, 2015

Rugby World Cup Predictions, 29 September 2015 to 03 October 2015

It is a little difficult to choose a period for the predictions. I have predicted up to October 3 because Tonga plays a further game on October 4, and I want to have updated their rating based on the result of the Tonga-Namibia game before predicting their game against Argentina.

Team Ratings at 29 September

The basic method is described on my Department home page.

Here are the team ratings prior to 29 September along with the ratings at the start of the Rugby World Cup.

Rating at 29 September Rating at RWC Start Difference
New Zealand 27.38 29.01 -1.60
South Africa 21.98 22.73 -0.70
Australia 20.47 20.36 0.10
Ireland 17.72 17.48 0.20
England 17.29 18.51 -1.20
Wales 14.10 13.93 0.20
France 11.53 11.70 -0.20
Argentina 9.03 7.38 1.60
Scotland 6.00 4.84 1.20
Fiji -3.23 -4.23 1.00
Samoa -3.54 -2.28 -1.30
Italy -6.93 -5.86 -1.10
Tonga -7.23 -6.31 -0.90
Japan -10.61 -11.18 0.60
USA -15.69 -15.97 0.30
Georgia -17.57 -17.48 -0.10
Canada -18.06 -18.06 -0.00
Romania -20.20 -21.20 1.00
Uruguay -31.10 -31.04 -0.10
Namibia -34.63 -35.62 1.00

 

Performance So Far

So far there have been 19 matches played, 16 of which were correctly predicted, a success rate of 84.2%.
Here are the predictions for previous games.

Game Date Score Prediction Correct
1 England vs. Fiji Sep 18 35 – 11 29.20 TRUE
2 Tonga vs. Georgia Sep 19 10 – 17 11.20 FALSE
3 Ireland vs. Canada Sep 19 50 – 7 35.50 TRUE
4 South Africa vs. Japan Sep 19 32 – 34 33.90 FALSE
5 France vs. Italy Sep 19 32 – 10 17.60 TRUE
6 Samoa vs. USA Sep 20 25 – 16 13.70 TRUE
7 Wales vs. Uruguay Sep 20 54 – 9 51.50 TRUE
8 New Zealand vs. Argentina Sep 20 26 – 16 21.60 TRUE
9 Scotland vs. Japan Sep 23 45 – 10 14.40 TRUE
10 Australia vs. Fiji Sep 23 28 – 13 24.10 TRUE
11 France vs. Romania Sep 23 38 – 11 33.30 TRUE
12 New Zealand vs. Namibia Sep 24 58 – 14 64.00 TRUE
13 Argentina vs. Georgia Sep 25 54 – 9 24.60 TRUE
14 Italy vs. Canada Sep 26 23 – 18 12.50 TRUE
15 South Africa vs. Samoa Sep 26 46 – 6 23.90 TRUE
16 England vs. Wales Sep 26 25 – 28 11.20 FALSE
17 Australia vs. Uruguay Sep 27 65 – 3 50.30 TRUE
18 Scotland vs. USA Sep 27 39 – 16 21.40 TRUE
19 Ireland vs. Romania Sep 27 44 – 10 38.80 TRUE

 

Predictions for 29 September to 03 October

The prediction is my estimated expected points difference with a positive margin being a win to the first-named team, and a negative margin a win to the second-named team.

Game Date Winner Prediction
1 Tonga vs. Namibia Sep 29 Tonga 27.40
2 Wales vs. Fiji Oct 01 Wales 23.80
3 France vs. Canada Oct 01 France 29.60
4 New Zealand vs. Georgia Oct 02 New Zealand 44.90
5 Samoa vs. Japan Oct 03 Samoa 7.10
6 South Africa vs. Scotland Oct 03 South Africa 16.00
7 England vs. Australia Oct 03 England 3.30

 

Popeye, mānuka, and keeping healthy

  • Good story in the Herald: A survey from Southern Cross Health Insurance estimates that about 750,000 people in NZ regularly take vitamins and dietary supplements. Contrary to what some supporters say, there have been quite a lot of randomised trials of vitamin supplements — it’s just that they tend to give disappointing results.  There are a few exceptions (such as folate in pregnancy) and a few arguably open questions (such as higher-dose vitamin D) but not many.
  • Entertaining legal report in NBR: NZ Honey is fighting the Ministry for Primary Industries over whether it makes unsupported health claims for mānuka honey. The fun part is that the controversy isn’t whether there’s evidence for health benefits, it’s over whether brand names such as “Manuka Doctor” and “Manuka Pharm” are so clearly just puffery that no-one would mistake them for health claims
  • People often claim spinach is very high in iron. Other people often claim this is due to a misplaced decimal point in early German research, and that Popeye didn’t know what he was talking about and spinach is a terrible source of iron. Still other people look into this more carefully and find that the error was not German, or a misplaced decimal point, and that Popeye’s health claims weren’t iron-related. In the end, spinach actually is a moderately good source of iron. (Karl Kruszelnicki, ABC Science)

Seeing the margin of error

A detail from Andrew Chen’s visualisation of all the election polls in NZ:

polls

His full graph is somewhat interactive: you can zoom in on times, select parties, etc. What I like about this format is how clear it makes the poll-to-poll variability.  The poll result for, say, National isn’t a line, it’s a cloud of uncertainty.

The cloud of uncertainty gets narrower for minor parties (as detailed in my cheatsheet), but for the major parties you can see it span an entire 10-percentage-point grid cell or more.

Stat of the Week Competition: September 26 – October 2 2015

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 October 2 2015.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of September 26 – October 2 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.

(more…)

September 26, 2015

US:China graph of the day

This (via @albertocairo) is from the Guardian, two years ago.

china

At first it looks like a pie chart, but it isn’t. It’s a set of bar charts warped into a circle, so that the ratio of blue and red areas in a wedge is the square of the ratio of the numbers. Also, the circle format means the longest wedge in each pair must be the same length: 8.6% unemployment rate is the same as 4.6% military expenditure, 104% market capitalisation, and 46 Olympic gold medals.

Many of these are proportions or per-capita figures, but not all. Carbon emissions are national totals, making China look worse. Film industry revenues and exports are totals; they are also gross revenues — because the whole visual metaphor falls apart completely for numbers that can be negative. That’s why the current-year budget surplus/deficit isn’t treated like the other numbers.

There are also some unusual definitions. “Social media”, the bar where China is furthest behind, is defined just by the proportion who use Facebook, which obviously underestimates the social-media activity of the US (and also, perhaps, of China).

The post has some discussion of the difficulties — for example, the measurement and even the definition of unemployment in the two counties — and is much better than the graph.

Here’s a different take on the same countries, in the same format, from the World Economic Forum

uschina-949x1024

They have similar problems with total vs proportion/mean variables. They solve the y-axis problem by working with international ranks, which at least gives a common scale. However, having 1 as the largest rank and some unspecified large number as the smallest rank does make the relationship between area and number fairly weird.  It also means that the actual numbers for each wedge aren’t fractions of a total in any sensible way.

If the main point is to be an eye-catching hook for the story, the Guardian graph is more successul

September 25, 2015

Rugby World Cup Predictions, 25 September 2015 to 27 September 2015

I decided updated ratings and predictions would be a good idea at this time. I can then post new predictions before the next games after September 27, which are on September 29.

Team Ratings at 25 September

The basic method is described on my Department home page.

Here are the team ratings prior to 25 September along with the ratings at the start of the Rugby World Cup.

Rating at 25 September Rating at RWC Start Difference
New Zealand 27.38 29.01 -1.60
South Africa 21.15 22.73 -1.60
Australia 19.83 20.36 -0.50
Ireland 18.15 17.48 0.70
England 18.04 18.51 -0.50
Wales 13.35 13.93 -0.60
France 11.53 11.70 -0.20
Argentina 8.02 7.38 0.60
Scotland 5.85 4.84 1.00
Samoa -2.71 -2.28 -0.40
Fiji -3.23 -4.23 1.00
Italy -6.26 -5.86 -0.40
Tonga -7.23 -6.31 -0.90
Japan -10.61 -11.18 0.60
USA -15.54 -15.97 0.40
Georgia -16.56 -17.48 0.90
Canada -18.73 -18.06 -0.70
Romania -20.63 -21.20 0.60
Uruguay -30.46 -31.04 0.60
Namibia -34.63 -35.62 1.00

 

Performance So Far

So far there have been 12 matches played, 10 of which were correctly predicted, a success rate of 83.3%.
Here are the predictions for previous games.

Game Date Score Prediction Correct
1 England vs. Fiji Sep 18 35 – 11 29.20 TRUE
2 Tonga vs. Georgia Sep 19 10 – 17 11.20 FALSE
3 Ireland vs. Canada Sep 19 50 – 7 35.50 TRUE
4 South Africa vs. Japan Sep 19 32 – 34 33.90 FALSE
5 France vs. Italy Sep 19 32 – 10 17.60 TRUE
6 Samoa vs. USA Sep 20 25 – 16 13.70 TRUE
7 Wales vs. Uruguay Sep 20 54 – 9 51.50 TRUE
8 New Zealand vs. Argentina Sep 20 26 – 16 21.60 TRUE
9 Scotland vs. Japan Sep 23 45 – 10 14.40 TRUE
10 Australia vs. Fiji Sep 23 28 – 13 24.10 TRUE
11 France vs. Romania Sep 23 38 – 11 33.30 TRUE
12 New Zealand vs. Namibia Sep 24 58 – 14 64.00 TRUE

 

Predictions for 25 September to 27 September

The prediction is my estimated expected points difference with a positive margin being a win to the first-named team, and a negative margin a win to the second-named team.

Game Date Winner Prediction
1 Argentina vs. Georgia Sep 25 Argentina 24.60
2 Italy vs. Canada Sep 26 Italy 12.50
3 South Africa vs. Samoa Sep 26 South Africa 23.90
4 England vs. Wales Sep 26 England 11.20
5 Australia vs. Uruguay Sep 27 Australia 50.30
6 Scotland vs. USA Sep 27 Scotland 21.40
7 Ireland vs. Romania Sep 27 Ireland 38.80

 

September 23, 2015

Briefly

  • Properly conducted web-based surveys aren’t necessarily that bad (from Pew Research) “Of 406 separate estimates taken from nine waves of the American Trends Panel, just nine of them differed by 5 percentage points or more. Perhaps not surprisingly, all nine are related to internet or digital technology use. A Web-only survey estimated that 82% of the public uses the internet on a daily basis, while the full sample (including non-internet users) finds 69% go online daily.”
  • Aardwolf Research is doing a flag-preference poll (mentioned in Stuff).  On the good side, they have sensible ways of looking at lots of possible flags. On the bad side, we don’t have lots of possible flags any more. On the good side they collect demographic data that could be used to get fairly representative weighted results from their self-selected internet sample. On the bad side, their results from the first wave don’t seem to use the demographic data at all.

Expensive drugs for a different reason

Usually, when there’s a very expensive medication in the news it’s because some company has just invented it and is trying to make as much money as possible before there’s competition– either from other similar drugs or from generic versions.   This is (presumably) the issue that Hillary Clinton is planning to address. The manufacturer is charging all the market will bear, but it’s not precisely a case of the uncaring free market. The drugs can only be that expensive because the government deliberately gives one company a monopoly, which we do as a strategy by society to bribe companies to invent drugs that work.  Like lots of people, I think the details could be improved but the basic idea is sensible.

Yesterday’s story (Stuff, Herald) is somewhat different. An existing, off-patent, treatment is having its price jacked up enormously. It’s about 50 times what it was recently, and 750 times what it was in 2010 when the drug was owned by a huge multinational, GSK. Derek Lowe (a drug company chemist) has some good posts about this. I’m mostly summarising.

There have been a few of these cases over the past few years, with different mechanisms.  The first is a well-meaning but poorly-designed idea of the FDA to collect evidence about drugs that were already in use when effectiveness testing was brought in. For some of these drugs, knowing whether (or how well) they actually work would be valuable. In return for doing the clinical trials to modern standards, a company can get a period of ‘marketing exclusivity’ on an old off-patent drug. Unfortunately, a company can pick up a drug where there isn’t any real doubt about effectiveness, so the trials provide little benefit, and then raise the price through the roof.

The second approach is to pick a drug that has no alternatives but where the total market is small enough that getting through the FDA approval process even for a generic is enough of an obstacle to keep out competitors.  One of the recent stories was about cycloserine, a last-ditch treatment for drug-resistant tuberculosis. There are still very few cases of this in the US — about 90 per year — so even a twenty-fold price increase doesn’t open up much of a market opportunity. The regulatory problem here is the impact of high standards for demonstrating manufacturing safety. Ordinarily that’s something you want, but for very rare diseases it provides a barrier to competition.

The third mechanism really looks like a regulatory loophole, and that’s what just happened with Daraprim for treating toxoplasmosis. The active ingredient of Daraprim, pyrimethamine, is off patent. There isn’t any FDA marketing exclusivity, either. But you can’t sell it as a drug unless you show that your formulation of pyrimethamine delivers a sufficiently-similar dose with sufficiently-similar timing to the formulation that was originally approved.

Toxoplasmosis isn’t as rare as drug-resistant TB, and historically the idea was that  an attempt to charge extortionate prices couldn’t work because someone would make a generic competitor. The trick is that you would need a supply of Daraprim to show that your formulation is close enough. You can’t do that if they won’t sell it to you.

As a concept, this goes back to a lawsuit over thalidomide (which now has a couple of genuine medical uses). One US company, Celgene, had the patent. Another company, Lannett, wanted to buy some of their drug to do bioequivalence studies, and claimed Celgene was refusing only to block competition. Celgene claimed they were just worried about Lannett’s safety procedures — which, in the case of thalidomide, could be fair enough.  They settled the case and it doesn’t really matter who was right; whether Lannett was paranoid or Celgene was cheating the system, the idea was out.