Very surprising. When cardiologists are away from the hospital, deaths after heart failure or cardiac arrest declined. I’ll probably use this in my course this Spring. (Or perhaps in both courses: Big Data, and Operations Quality in Healthcare.)
And a candidate for worst graph of the year, appearing to show that deaths from a certain class of diseases grew in parallel with some farming trends. ! (Figure 16 in the article, which is at http://www.organic-systems.org/journal/92/JOS_Volume-9_Number-2_Nov_2014-Swanson-et-al.pdf ). Any steadily increasing time series can be plotted so that they lie approximately on top of each other, if you distort the scales enough. Other “causes” they could have plotted, with approximately the same results: cell-phone per capita, percentage of cars on the road with ABS brakes, and (for all I know) average campaign spending per Congressional race.
Under-reporting of clinical trials has been a problem for for decades (if not more). Only in the last few years has the medical community realized the pernicious effects this has on our knowledge about “what works” in medicine. If “bad” results don’t get permitted, all kinds of problems ensue, such as overly-optimistic views of new drugs, repeating of expensive and potentially dangerous research, and general waste of money. Since the NIH is such a big funder of medical research, this affects taxpayers too!
In any case, the NIH continues its slow (but steady?) crackdown on this issue. They are even threatening to cut off funding for researchers who don’t make their results available! (Of course a lot of research is funded by pharmaceutical companies, so this is hardly a comprehensive threat.)
I track this kind of thing because of my interest in “How societies learn” about technology. Forgetting and ignoring are powerful forces in retarding learning.
I agree 100% with the following discussion of big data learning methods, which is excerpted from an interview. Big Data is still in the ascending phase of the hype cycle, and its abilities are being way over-promised. In addition, there is a great shortage of expertise. Even people who take my course on the subject are only learning “enough to be dangerous.” It will take them months more of applied work to begin to develop reasonable instincts, and appropriate skepticism.
As we are now realizing, standard econometrics/regression analysis has many of the same problems, such as publication biases and excess re-use of data. And one can argue that it’s effects e.g. in health care have also been overblown to the point of being dangerous. (In particular, the randomized controlled trials approach to evaluating pharmaceuticals is much too optimistic about evaluating side effects. I’ve posted messages about this before.) The important difference is that now the popular press has adopted Big Data as its miracle du jour.
One result is excess credulity. On the NPR Marketplace program recently, they had a breathless story about The Weather Channel, and its ability to forecast amazing things using big data. The specific example was that certain weather conditions in Miami in January predict raspberry sales. What nonsense. How many Januaries of raspberry sales can they be basing that relationship on? 3? 10?
Why Big Data Could Be a Big Fail [this is the headline that the interviewee objected to – see below]
Spectrum: If we could turn now to the subject of big data, a theme that runs through your remarks is that there is a certain fool’s gold element to our current obsession with it. For example, you’ve predicted that society is about to experience an epidemic of false positives coming out of big-data projects.
Michael Jordan: When you have large amounts of data, your appetite for hypotheses tends to get even larger. And if it’s growing faster than the statistical strength of the data, then many of your inferences are likely to be false. They are likely to be white noise.
Spectrum: How so?
Many of my former students come back years later and ask my advice about getting a PhD. I generally tell them that a PhD program is like a monastery – you have to love the pursuit of knowledge, for its own sake, to make it bearable. If you are doing it only in pursuit of a post-graduation goal, it is too hard a life.
This article includes a startling graph on time-to-graduation. I graduated from MIT in 1982, after 4 years. According to the graph, the average time in social sciences then was 8 years?! I had a lot of breaks (NSF Fellowship, stipend from one of my thesis advisors, pregnant wife to provide emotional support and incentive!) but 4 to 5 years seemed like the norm in my program.
In any case, the second half of the article has some realistic advice about the stresses of protracted graduate programs, and about the importance of your particular advisor’s style.
This post is for students who want to take my course, Technology and Operations Management, IRGN438, but have not been able to register. Here is the syllabus. Take a careful look, and realize that it involves a considerable amount of work. If you want permission to take the course, please send me an email with: Continue reading
This page is for my colleagues who are using Ted for the first time in Fall 2014. The main difficulty you will encounter is that it is too flexible. There are many ways to do just about every operation. But they often look different to students. One result is that students don’t know where to find material. Another result (again, speaking from my experience last year) is that you will design a Ted setup that you want to change after a few weeks, potentially adding further confusion for students. This is just for IRPS faculty although other faculty new to Blackboard are welcome to look. Everyone else should ignore it.