Stats Chat

Two and a half important things today.

First, a reasonably-sized, well-conducted trial of chloroquine after exposure to prevent disease found no real evidence of benefit (NYTimes) The  original research paper is supposed to be here, but isn’t up yet.

Second, Melissa Davey, Stephanie Kirchgaessner, and Sarah Boseley, in the Guardian have followed up on the concerns about that large multinational observational study.   If you missed it so far, the paper claimed to have data on over 90,000 hospitalised patients in six continents, and found patients getting chloroquine had substantially mortality. The data were supposed to have been collected from the hospitals electronically, more or less in real time, and sent to a server in the USA, but data use agreements supposedly meant that the hospitals (or even the countries they were in) couldn’t be identified — only the continents.  The Guardian journalists had the same idea as I had, that the Australian data (5 hospitals, 63 patients) was the weak point for checking: Australia is a small continent, with a small COVID outbreak, and a highly concentrated population, so you’d only have to call a fairly small number of hospitals and get them to deny sending their data to Surgisphere.  Unlike me, they actually did it:

The Guardian has since contacted five hospitals in Melbourne and two in Sydney, whose cooperation would have been essential for the Australian patient numbers in the database to be reached. All denied any role in such a database, and said they had never heard of Surgisphere. Desai did not respond to requests to comment on their statements.

So, the Australian data, after being obviously impossible and being corrected, are impossible again.  Australian patients make up only a tiny fraction of the claimed database — less than one in one thousand — but as Mies van der Rohe said, “God is in the details”

The additional half topic is that Surgisphere says they are having the data audited 

We believe that an independent academic audit that validates those three functions as it relates to our papers in the New England Journal of Medicine and The Lancet will bring further transparency to our work, further highlight the quality of our work, and also continue to deserve the confidence of our work by our colleagues.

This process will follow strict boundaries as it relates to our data use agreements, among other considerations. We are pursuing such an independent audit with all due haste while ensuring compliance with various legal and regulatory concerns.

That’s good, but they don’t say by whom. Also “This process will follow strict boundaries as it relates to our data use agreements, among other considerations” is potentially a problem. Surgisphere have claimed that the data use agreements do not allow the hospitals to be identified. An audit that does not verify the participation of at least an auditor-determined sample of the hospitals is not worth much — and ideally there would be some checks of data against records in those hospitals. A proper audit would be a lot of work, but Surgisphere claim to have carried out some such checks themselves: they say in the research paper

Collection of a 100% sample from each health-care entity is validated against financial records and external databases to minimise selection bias.

Auditing, and data access in general, are expensive and annoying if you have to do them, but so is getting the wrong answer on treatment effectiveness in COVID-19.

Team Ratings for Round 4

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 24 matches played, 14 of which were correctly predicted, a success rate of 58.3%.
Here are the predictions for last week’s games.

Predictions for Round 4

Here are the predictions for Round 4. 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.

Not for the first time in history, but it hasn’t been happening nearly enough this year.

Here’s the state of Auckland’s water storage: the width of the bars is proportional to the capacity of the dam

I got the data from aucklandwatersupply.co.nz, which uses data from Watercare to produce graphics over time.  Auckland’s total stored water today is basically the same as it was on Friday.

[Auckland also has real-time water from the Waikato and from the Onehunga spring, but having to rely on those would be bad.]

The Lancet published a paper recently claiming that chloroquine and hydroxychloroquine, with or without azithromycin and similar antibotics didn’t help COVID-19 but did cause heart problems, in 96000 people hospitalised for the disease across six continents.Since that’s what most people who would pay attention to a Lancet paper already believed,  it was basically a confirmation. I argued that it made future randomised trials a bit unnecessary.

There is increasing concern about whether the paper accurately represented the source and quality of the data that went into the analysis. More precisely, sober and responsible people are even suggesting it might have been made up.

The Guardian has a headline:  Covid-19 study on hydroxychloroquine use questioned by 120 researchers and medical professionals. Which, by now, is a big underestimate.

Probably the biggest concern is that idea that a software system released last year, could already be getting real-time clinical data from 671 hospitals in six continents, apparently with the rest of the healthcare informatics industry not noticing.

Also, there’s no data about the hospitals except which continent they are in, which is unusual for research of this type.

On top of that, some of the data are clearly wrong.   I hadn’t read the paper carefully.  I looked at it this morning in a bit more detail. One thing that jumped out was the claim that 609 of the hospitalised COVID cases were at five hospitals in Australia.  This is using data up to April 14. On April 10, the Herald talked with Tony Blakely, who said there were 263 hospitalised cases in all of Australia. There’s no way these numbers match up.

It turns out that someone had noticed this already: the Australian data don’t match other data sources in other ways. In a new correction, the researchers said they had listed one hospital in the wrong continent. There were actually only 63 patients from Australia whose data  was in the study, and one of the hospitals was in Asia:

“We have reviewed our Surgisphere database and discovered that a new hospital that joined the registry on April 1, and self-designated as belonging to the Australasia continental designation,”

They’d been mixed up because the hospital said it was in “Australasia”.  Now, I’d think if you were setting up data access and non-disclosure contracts with hospitals, you’d want to know what country’s laws the hospitals operate under, but maybe machine learning can get around these issues.

A further interesting question is where this hospital could possibly be. Remember, it is supposed to have had 609-63=546 hospitalised, PCR-positive, COVID cases by April 14, and to be in Asia,  and to have self-described as being in Australasia.  We can rule out the Pacific islands that would be regarded as in Australasia — they don’t have anywhere near that many cases. Papua New Guinea has eight confirmed cases, counting up to right now.   Timor-Leste has 24.

Could it be a hospital in Indonesia? Indonesia started testing very late, so 546 PCR-confirmed, hospitalised patients by April 14 is a fair chunk of the whole country. The special COVID hospital that’s now at the Asian Games’ athletes’ village near Jakarta has been in the news, and it’s got the right sort of patient numbers, and so there might be a couple of other Indonesian candidates. But it’s hard to see them self-describing as being in “Australasia”, or as having signed up to  this new database system in the middle of a pandemic.

Home Ground Advantage

Determining home ground advantage for this new draw has been very difficult. In some cases it has been straightforward: Storm at home, Cowboys at home, Broncos at home, Warriors always away despite their being the nominal home team. But Knights versus Dragons at Bankwest Stadium? Raiders versus Sea Eagles at Campbelltown? I made the ground neutral for those. I gave home ground advantage to a team if the opponent was from interstate (not counting Raiders playing in Sydney). Also the Eels at Bankwest Stadium have home ground advantage.

To deal with the Warriors always being the away team, I have swapped the order of the teams for some matches because the first team is always the team with home ground advantage in my code.

And yes, I know that a component of home ground advantage is thought to be the home crowd, but there are no crowds, …

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.

• Matt Nippert has two wonderful stories about the government response to the pandemic, in the NZ$ Herald.  He’s used the huge trove of documents released by the government about a week ago to show the government scrambling frantically, but still (by luck and skill and co-operation) managing to keep up.   We were so close to having the wheels come off the public-health response system.

• “You always expect some resistance but if I had known people’s mental anchor in that negotiation was 10 cases, I might not have been so courageous in my recommendations.      So I’m pleased I didn’t know that.” Ayesha Verrall, who audited the contact-tracing system, talking to Derek Cheng in the NZ$ Herald.

•  “the stark turning point, when the number of daily COVID-19 cases in the U.S. finally crested and began descending sharply, never happened. Instead, America spent much of April on a disquieting plateau”  Ed Yong’s brilliant ‘patchwork pandemic’ story in The Atlantic
• There’s still controversy over whether the infection fatality rate of COVID-19 is around 1 in 100 or around 1 in 1000. Hilda Bastian comments on the latest meta-analysis by John Ioannidis.
• Meanwhile, in New York City, excess deaths this year reached 20,569, which works out to one person in 400.
• Good news (via XKCD and HuffPost and Pew) is that most people in the US agree with the social-distancing programs.
• Less good news: only 52% even of Democrats will call ‘false’ the statement “Bill Gates wants to use a mass-vaccination campaign against COVID-19 to implant microchips that would be used to track people with a digital ID” (via @publicaddress).

For the first time, there’s data from a reasonably large randomised trial of a COVID-19 treatment: a drug called remdesivir. And it’s modestly good news.  People in hospital, with lower respiratory tract symptoms were randomly assigned to remdesivir or placebo, and those given remdesivir recovered faster, and more of them recovered in 30 days — in the sense that they were healthy enough to leave hospital.  There wasn’t definitive evidence on the proportion dying, though there were fewer deaths in the remdesivir group.  The treatment didn’t seem to do much for people who were already on ventilators, but it’s hard to be sure about subgroups when the whole trial is only just big enough to give convincing results.  The improvement wasn’t massive (25-30% better), but it does look real, which is encouraging for other drugs that work the same way, as well as for future patients.

It turns out that I know the statistician who did the main analysis (Hi, Lori!), and who must have had an incredibly stressful couple of months (especially after the basic trend was publicised by Dr Fauci a month ago).

Update: the following study is now looking really, really dubious, so perhaps don’t bother reading about it.

There’s also data from another large observational study of chloroquine.  It’s from a collection of hospitals around the world who kept registers of who got what treatment (even though it wasn’t randomly allocated). This paper looks at 96000 people who were treated early (less than two days after diagnosis) and who were not on ventilators. There’s no suggestion of a benefit of chloroquine or hydroxychloroquine: in fact, patients getting either drug, with or without an antibiotic like azithromycin, did worse.

The paper ends with a ritual invocation “These findings suggest that these drug regimens should not be used outside of clinical trials and urgent confirmation from randomised clinical trials is needed.”  It’s getting towards the point where confirmation from randomised trials really isn’t needed any more.

MMP is, in many ways, a beautiful voting system.  As implemented in New Zealand it’s got one feature that complicates voting slightly and complicates forecasting a lot: the threshold.

In TVNZ’s Colmar Brunton poll, the Greens got 4.7% of the vote. The threshold is 5%. Getting 4.7% of the vote in an election would mean you don’t get any seats.   The margin of error that TVNZ were stating was +/- 3%, so based just on that, the Greens were basically 50:50 on whether they make the threshold.

At that level it also potentially makes a big differences how you treat the undecided voters, who made up 16% of responses. The person who pointed this out to me thought that the 16% had been left as a separate group, which you might easily think from the TVNZ web post on the poll.  But if you do the arithmetic, the parties’ quoted percentages add up to 100 (give or take rounding error), so the percentages were of those who expressed a preference.  Only 4.1% of the respondents actually said “Green”.

Normally, you don’t have to worry about this because tiny changes in preferences will only produce small changes, if any, in the number of seats. But going from 4.99% to 5.00% takes you from no seats to several (I think six) seats. Predicting the make-up of Parliament gets hard when there are parties close to the threshold.

Given the sensitivity of the results to small changes, I think the website (if not the actual news segment) should be more explicit about how undecided voters are handled in the seat projection.  And making statements about whether a party is in or out of Parliament should have a bit more explicit uncertainty when it could be wrong by six seats with minute changes in voting.

On the TVNZ website, the report says “ACT, assuming it wins an electoral seat, would pull in three MPs with its 2.2% support”, and if National’s gift of Epsom to David Seymour is enough of an uncertainty to require an explicit caveat, so is being a few voters per thousand away from the threshold. 

One of the features of the COVID pandemic is the near-elimination of delays in making scientific data available.  Everyone is moving to releasing preprints, and they’re that more rapidly than they did in the past.  In many ways this is great, but it does mean things can get publicised faster than they can get evaluated.

Two examples: one from a preprint, one from a press release

The preprint: There’s a headline in the Herald: Landmark study: Virus didn’t come from animals in Wuhan market. That’s a pretty big claim. So where did the Herald get  it? The Daily Mail on Sunday.  The Mail got it from a preprint that was uploaded a couple of weeks ago.   Most readers of the Mail and the Herald (including me) won’t be in a position to evaluate the credibility of the work. Because it sounds like it feeds into the whole quagmire of conspiracy theories and hoaxes around COVID origin, you’d really want to see some independent expert comment on the story. Writing a story like this  with quotes from the authors but no independent comments seems a bit irresponsible.

The press release: The Herald headline is Covid 19 coronavirus ‘cure’? US biotech company claims it’s found antibody to block virus. The CEO of the company is quoted as saying

“We want to emphasise there is a cure,’ Sorrento’s CEO, Dr Henry Ji, told Fox News.

“There is a solution that works 100 per cent. If we have the neutralising antibody in your body, you don’t need the social distancing. You can open up a society without fear

This is not normal.  “Works 100 per cent” is not something biotech CEOs go around saying about a product when they’re still trying to get FDA approval — and they haven’t actually tested the product in any humans.  Presumably the idea now is that it’s worth the risk of annoying the FDA if you get enough public interest. And since human tests have a nasty habit of not working nearly as well as lab tests, being able to sell your product without them is attractive.

The basic idea is sensible: using well-chosen synthetic antibodies rather than getting your body to make its own. In particular, they start working right away, where vaccines only start the process of getting your immune system to make antibodies. Also, some antibodies can have harmful effects, and you can avoid using those.  They’ll need to be administered by injection, and repeated as your body gets rid of them — synthetic (‘monoclonal’) antibodies for other conditions seem to mostly be given every 2-4 weeks. But, again, publishing a claim of a cure when it has not yet been shown to have any benefit at all in real live people, without any independent commentary is not best practice.

There’s a new COVID case from the Marist College cluster today.  The person previously tested negative, but had been in isolation (perhaps, though the Herald doesn’t say, because of the combination of symptoms and being a contact).  Now that we have plenty of testing capacity there has been follow-up testing of some clusters as well as testing of some apparently healthy people in high-risk jobs.

From what I’ve seen on social media, this has led some more people to find out about the false negative rates of the current tests.  It’s not a secret that the swab+PCR test we use in NZ misses maybe a third of infections (because there isn’t enough virus on the swab), though it hasn’t exactly been emphasised.  So, how is this acceptable? Well, “acceptable” depends on the alternative. It’s the best test we have. Researchers (and companies) are working on better ones, and things are likely to improve over time, as they did with HIV testing.  If you’ve been in contact with a case and have COVID-like symptoms and test negative, you’re still going to need to isolate until you recover.  That, plus the fact that the testing does pick up the majority of cases, means a test/trace/isolate strategy, done right, should be nearly enough to control an outbreak that’s caught early.

The current tests give basically no false positives. That’s really helpful for a test/trace/isolate strategy — we’ve done 200,000 tests, and if, say, 5% of them were false positives, that would be another 10,000 cases. Before counting all the contacts of those 10,000 people.  The low false positive rate also means the health system can say, “yes, you need to get tested”,  and then after a positive results, “yes, you absolutely must stay home”, “yes, you need to tell us about all the places you’ve been, even if some of them are embarrassing or illegal”.

There’s another testing-accuracy story in the New York Times, unhelpfully headlined Coronavirus Testing Used by the White House Could Miss Infections. It turns out that they don’t mean that it could miss infections the way all the other tests do; they mean it could miss infections that other tests detect.  The test in question is a portable testing machine from Abbott that takes only 5 minutes to process a sample, quite a bit faster than the standard testing systems.  Researchers from a testing lab at New York University’s Langone Medical Center (who liked the idea of a faster test, given the number of tests they perform) did comparisons to the machines they are currently using and published a PDF about it– some tests on the same swabs and some on different swabs taken at the same time from the patient.  They say the Abbott machine missed 1/3 (out of 15) or 1/2 (out of 30) samples where the current machine found the virus.

Abbott, on the other hand, said their evaluations showed 0.02% false negative rate.

You might wonder how two evaluations could be so different: one false negative in 50,000 samples vs 5 in 15 samples?  As usual, when two numbers don’t fit, it’s probably because they don’t mean the same. Abbott will have been referring to an estimated false negative rate in viral samples with a known virus concentration — an evaluation of the assay itself.  The NYU researchers are talking about live clinical use in samples where they know the virus is present in the swab.  And when we talk about false negatives in the NZ sampling system, we’re talking about samples where the virus probably isn’t present in the swab.

Abbott argue that the NYU researchers were using the machine incorrectly. That could be true, but it’s only reassuring to the extent that you think the White House will be better at it than a pretty highly regarded New York hospital and research centre.