Stats Chat

There’s an ‘interactive’ at Stuff about the drug rebates that Pharmac negotiates. The most obvious issue with it is the graphics, for example

and

The first of these is a really dramatic illustration of a well-known way graphs can mislead: using just one dimension of a two-dimensional or three-dimensional thing to represent a number. The 2016/7 capsule looks much more than twice as big as the puny little 2014/15 one, because it’s twice as high and twice as wide (and by implication from shading, twice as deep).  The first graph also commits the accounting sin of displaying a trend from total, nominal expenditures rather than real (ie, inflation-adjusted) per-capita expenditures.

The second one is not as bad, but the descending line to the left of the data points is a bit dodgy, as is the fact that the x-axis is different from the first graph even though the information should all be available.  Also, given that rebates are precisely not a component of Pharmac’s drug spend, the percentage is a bit ambiguous.  The graph shows total rebates divided by what would have been Pharmac’s “drug spend” in the improbable scenario that the same drugs had been bought without rebates. That is, in the most recent year, Pharmac spent $849 million on drugs. If rebates were $400m as shown in the first graph, the percentage in the second graph is something like ($400 million)/($400 million+$849 million)=32%.

More striking when you listen to the whole thing, though,  is how negative it is about New Zealand getting these non-public discounts on expensive drugs.  In particular, the primary issue raised is whether we’re getting better or worse discounts than other countries (which, indeed, we don’t know), rather than whether we’re getting good value for what we pay — which we basically do know, because that’s exactly what Pharmac assesses.  

Now, since the drug companies do want to keep their prices secret there must be some financial advantage to them in doing so, thus there is probably some financial disadvantage to someone other than them.   It’s possible that we’re in that group; that other comparable countries are getting better prices than we are. It’s also possible that we’re getting better prices than them.  Given Pharmac’s relatively small budget and their demonstrated and unusual willingness not to subsidise overpriced new drugs, I know which way I’d guess.

There are two refreshing aspects to the interactive, though.  First, it’s good to see explicit consideration of the fact that drug prices are primarily not a rich-country problem.   Second, it’s good to see something in the NZ mass media in favour of the principle that Pharmac can and should walk away from bad offers. That’s a definite change from most coverage of new miracle drugs and Pharmac.

Team Ratings for Round 8

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 54 matches played, 36 of which were correctly predicted, a success rate of 66.7%.
Here are the predictions for last week’s games.

Predictions for Round 8

Here are the predictions for Round 8. 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 8

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.

Note that Cheetahs2 refers the Cheetahs team when there is a Pro14 match. The assumption is that the team playing in the Pro14 is the top team and the Currie Cup team is essentially a second team.

Performance So Far

So far there have been 18 matches played, 15 of which were correctly predicted, a success rate of 83.3%.
Here are the predictions for last week’s games.

Predictions for Round 8

Here are the predictions for Round 8. 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.

Q: Did you see women who own horses live 15 longer than those who don’t?

A: Fifteen what?   _{[thanks, David Hood]}

Q: Years.

A:  There’s an obvious reasons why women who own horses would live longer than those who don’t. Horses are expensive; women who can afford them will be more affluent than average. There could easily be other confounding factors, too

Q: But 15 years?!

A: Ok, that’s a lot. But remember, this is just an observational study — and it might not even be a representative sample. It could be some sort of clicky bogus poll

Q: Where they ask people if they own a horse and how old they were when they died? Yeah right.

A: Um. Ok. Maybe not a bogus poll. But 15 years just isn’t plausible, and it’s obviously not a randomised trial.

Q: “The double blind study followed women in different age groups over a forty year time frame to capture this objective data.”

A: Double blind?

Q: What it says.

A: How could you possibly have a double blind study of horse ownership?

Q: They could get alpacas instead. Or virtual reality games about horses. Or something.

A:  That’s not double blind. That’s active-control. Double blind would mean you got an alpaca instead of a horse AND YOU COULDN’T TELL! Is there a link to the research?

Q: I hoped you’d find it, like you usually do.

A:

Q:

A: Really?

Q:

A: Ok. One of the other copies of this story says the lead scientist is Gary Cockburn.  There are three papers on the PubMed database with a “G Cockburn” as author. None is even slightly related to this story.  There are seven papers with a “Cockburn” as author and some reference to “horse”. None is even slightly related to this story. AND YOU CAN’T HAVE A DOUBLE BLIND STUDY OF HORSES!

Q: They made it up?

A:  This is why you shouldn’t follow those links at the bottom of the page

• The Apple Watch will now diagnose atrial fibrillation. As this story at Ars Technica explains, that’s a good problem to target: it’s under-diagnosed, and the risk of stroke that it presents is treatable. But there could be a lot of people looking for cardiologist appointments.
• A blog post on medical AI safety more generally.  And an example
• Fake maps as a new ‘fake news’ threat. People who have retweeted the bogus ‘BSE map vs Brexit map’, I’m looking at you.
• Harkanwal Singh, formerly of the NZ Herald, now has a website of his own
• A new data-centered journalism organisation, The Markup,  via Nieman Lab
• Because “Listen to music your parents like” would be too obviously dumb, “Spotify Teams Up With Ancestry to Create DNA-Curated Playlists”
• Cloudy with a chance of bats: a twitter thread about flying things that regularly show up on weather radar

Team Ratings for the Grand Final

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 200 matches played, 126 of which were correctly predicted, a success rate of 63%.
Here are the predictions for last week’s games.

Predictions for the Grand Final

Here are the predictions for the Grand Final. 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 7

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 46 matches played, 33 of which were correctly predicted, a success rate of 71.7%.
Here are the predictions for last week’s games.

Predictions for Round 7

Here are the predictions for Round 7. 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 7

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.

Note that Cheetahs2 refers the Cheetahs team when there is a Pro14 match. The assumption is that the team playing in the Pro14 is the top team and the Currie Cup team is essentially a second team.

Performance So Far

So far there have been 17 matches played, 14 of which were correctly predicted, a success rate of 82.4%.
Here are the predictions for last week’s games.

Predictions for Round 7

Here are the predictions for Round 7. 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.

Our very own Liza Bolton was summoned to TV show The Project last week to reveal how to minimise the chance of sharing a First Division Lotto win with heaps of other people.

The invite came after last Wednesday’s Lotto draw, where 40 people shared the first division prize, getting only $25,000 each rather than something with  one or two extra zeroes.

Liza had 90 seconds to share her top five tips – the first one is on the image.

Watch the clip here.

There’s a story about Lotto on Stuff that starts off promisingly

Forty Kiwis took out Lotto First Division on Wednesday night – the most first division winners in a single draw in the game’s 30-year-history.

With that many winners sharing the $1 million prize, they’re only getting $25,000 each.

This is one of the big reasons that you can’t just divide the prize by number of possible combinations and get the expected value of a ticket.

Further down, though we get this

Despite these overwhelming odds there are times when it makes mathematical sense to buy a Lotto ticket.

That’s when Powerball jackpots get so large the value of the prize pool is greater than the amount spent on tickets.

Technically, this is true. The problem is you don’t know the amount spent on the tickets, because NZ Lotto doesn’t tell anyone. So as a strategy, it’s useless.  The link goes to another story headlined Why professors of statistics play Lotto too, when the prize is big enough.  That surprised me, so I read on to see who these professors of statistics were.

There are two professors mentioned in the story, Martin Hazelton of Massey and Peter Donelan of the university currently known as Vic.  You should definitely pay attention to their opinions: Martin, in particular, is probably the country’s top statistical theorist.

They don’t, however, say they “play Lotto too, when the prize is big enough”.  Professor Hazelton doesn’t say anything on that issue. Professor Donelan is quoted right at the end of the story

“In my household, if it was up to me, I wouldn’t bother to buy one,” Donelan said.

But he suspects some stats professors do: “I expect some do regardless of what they know.”

And that’s probably true. Nothing wrong with Lotto as an entertainment — the monetary return on investment is low, but the same is true for beer, movies, rugby, or twilight walks on the beach — but it will very rarely “make mathematical sense.”