Alas, not in New Zealand, but close … our Australian counterparts in medical research appear on the face of it to have scored big in what appears otherwise to be a grim Australian budget. An AUD$20bn medical research “future fund” is to be established. This effectively means that by 2022-3 there will be twice the current budget available for medical research per annum (i.e. about $1bn). How this will be divided up remains to be seen, but I note that Prof Mike Daub of Curtin University is suspicious that it is “Medical Research” not “Health and Medical Research.”
If this truly is a massive boost to medical research in Australia, what could it mean to New Zealand?
A negative possibility is that because there are already issues with recruiting medical specialists who wish to undertake research in New Zealand and because the Australian NHMRC already has successful contestable grant funding rates about twice that of New Zealand’s HRC (~16% cf ~7%), I expect there would be more one-way traffic of scientists to Australia. It is imperative that this be avoided, for all our health’s sake.
If, though, the funding recognises the value of collaborative research then it may be possible for New Zealand scientists to work more closely with their Australian counterparts on projects of mutual interest. To that end, the New Zealand Government has (now) a great opportunity under CER to facilitate collaboration. Perhaps, a dedicated fund that would support New Zealand researchers financially to play a role in Australian led research. Apart from the high quality of NZ researchers (!), New Zealand should appeal to Australia because of the better integration of our health systems, especially with respect to tracing patient hospital events nationally, and because of the lower costs of doing research here. Furthermore, health consumers in New Zealand demand the best (I know I do!) and the best is only available through research – ultimately more research across the ditch will benefit us here. Thanks Tony.
ps. Catching the early flight to Sydney tomorrow to share some Trans-Tasman love and collaborate with my medical research colleagues at the Prince of Wales Hospital and the Royal Brisbane & Women’s Hospital.
“Only as good as your last match” goes the cliché. This is true for Ricky Ponting and here is why. I recently published an article1 (Open Access :)) on some new techniques being used in medical research which determine if making an additional measurement improves what we call “risk stratification.” In other words – does measuring substance X help us to rule in or rule out if someone had a disease or not. I got a bit board with talking about “biomarkers” and medical stuff, so when it came to presenting this at the Australian New Zealand Society of Nephrology’s annual conference I looked to answer the very important question: “Does Ricky Ponting’s last inning’s matter?”, or in Australian cricket jargon “Ponting, humph, he’s only as good as his last innings, mate.”
How did I do it?
I chose Australia winning a one-day international when chasing runs as an outcome (Win or Loss).
Using data available from Cricinfo I determined which of the following on its own predicts if Australia will win (ie which predicts the outcome better than just flipping a coin): (1) Who won the toss, (2) whether it is a day or night match, (3) whether it is a home or away match, (4) how many runs the opposition scored.
As it turned out if Australia lost the toss they were more likely to win (!), and, not surprisingly, the fewer runs the opposition scored the more likely they were to win. I then built a mathematical model. All this means is that I came up with an equation where the inputs were the winning or losing of the toss and the number of runs and the output was the probability of winning. This is called a “reference model.”
I added to this model Ricky Ponting’s last innings score and recalculatd the probability of Australia winning.
I then could calculate some numbers which told me that by adding Ricky Ponting’s last innings to the model I improved the model’s ability to predict a win and to predict a loss. Below is a graph which I came up with to illustrate this. I call this a Risk Assessment Plot.
So, when the shrimp hit the barbie, the beers are in the esky, and your mate sends down a flipper you can smack him over the fence for you now know that when Ricky Ponting scored well in his last innings, Australia are more likely to win.
The middle bit is the Risk Assessment Plot. The dotted lines tell us about the reference model. The solid lines tell us about the reference model + Ricky Ponting. The further apart the red and blue lines are the better. The red lines are derived from when Australia won, the blue lines from when the lost. If you follow the black lines with arrows you can see that by adding in Rick Ponting’s last innings the model the predicted probability (risk) of a win increases when Australia went on to win (a perfect model would have all these predictions equal to 1). Similarly the predicted probability of a loss gets smaller when Australia did lose (ideally all these predictions would equal 0).
Pickering JW, Endre ZH. New Metrics for Assessing Diagnostic Potential of Candidate Biomarkers. Clin J Am Soc Nephro 2012;7:1355–64.