andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-67 knowledge-graph by maker-knowledge-mining
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Introduction: Hank Aaron at the Brookings Institution, who knows a lot more about policy than I do, had some interesting comments on the recent New York Times article about problems with the Dartmouth health care atlas. which I discussed a few hours ago . Aaron writes that much of the criticism in that newspaper article was off-base, but that there are real difficulties in translating the Dartmouth results (finding little relation between spending and quality of care) to cost savings in the real world. Aaron writes: The Dartmouth research, showing huge variation in the use of various medical procedures and large variations in per patient spending under Medicare, has been a revelation and a useful one. There is no way to explain such variation on medical grounds and it is problematic. But readers, including my former colleague Orszag, have taken an oversimplistic view of what the numbers mean and what to do about them. There are three really big problems with the common interpreta
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1 Hank Aaron at the Brookings Institution, who knows a lot more about policy than I do, had some interesting comments on the recent New York Times article about problems with the Dartmouth health care atlas. [sent-1, score-0.182]
2 Aaron writes that much of the criticism in that newspaper article was off-base, but that there are real difficulties in translating the Dartmouth results (finding little relation between spending and quality of care) to cost savings in the real world. [sent-3, score-0.812]
3 Aaron writes: The Dartmouth research, showing huge variation in the use of various medical procedures and large variations in per patient spending under Medicare, has been a revelation and a useful one. [sent-4, score-1.149]
4 There is no way to explain such variation on medical grounds and it is problematic. [sent-5, score-0.18]
5 Spending variations on Medicare are inversely related to spending variations by private payers. [sent-9, score-1.011]
6 That means that what appear to be high spending areas are, in many cases, high cost-shifting areas. [sent-10, score-0.622]
7 Two sights in Wisconsin, in cluding one that includes the justifiabley praised Marshfield Clinic, have among the lowest per person Medicare spending in the nation, but were found to have among the highest private per person spending in the nation. [sent-11, score-1.408]
8 What counts is total medical spending if one wants to relate spending and quality. [sent-12, score-1.133]
9 Knowing that spending is high and poorly correlated with measures of quality (if such a correlation exists! [sent-14, score-0.623]
10 ) is not the same as knowing how to pare spending in a way that increases net social benefit from health care. [sent-15, score-0.758]
11 Repeated studies have shown that medical care spending ON THE AVERAGE produces benefits far greater than its cost, but that AT THE MARGIN there is a lot of waste. [sent-16, score-0.919]
12 The waste is observed, for the most part, ex post. [sent-17, score-0.259]
13 To be sure that cutting spending improves well- being one has to be able to predict ex ante who will derive benefits that are less than cost. [sent-18, score-0.985]
14 There are some egregious situations where ex ante waste can be demonstrated. [sent-19, score-0.435]
15 But for the most part, we just do not have good enough research to predict with sufficient accuracy who will benefit and who will not from doing more of some procedure. [sent-20, score-0.205]
16 Some research has shown that even the most glaring cost differences noted in the Dartmouth research are associated with improved outcomes for those conditions where something close to random assignment of patients to alternative patient regimes occurs. [sent-24, score-0.411]
17 He found that they did better in Florida counties that spent most, the very counties that the Dartmouth folks have held up for their poor average outcomes. [sent-29, score-0.25]
18 He also found that preemies who were a couple of grams under the threshold at which they are described as in need of intensive care did materially better than did preemies a couple of grams over that threshold and, hence, treated as normal births. [sent-30, score-0.993]
19 The weight difference was too small to matter medically and went in the wrong direction; the intensity of care mattered. [sent-31, score-0.189]
20 The Administration, and Orszag in particular, were wrong in neglecting the warning to forecasters of Scottish economist, Alec Cairncross, ‘Give a number. [sent-33, score-0.12]
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same-blog 1 1.0 67 andrew gelman stats-2010-06-03-More on that Dartmouth health care study
Introduction: Hank Aaron at the Brookings Institution, who knows a lot more about policy than I do, had some interesting comments on the recent New York Times article about problems with the Dartmouth health care atlas. which I discussed a few hours ago . Aaron writes that much of the criticism in that newspaper article was off-base, but that there are real difficulties in translating the Dartmouth results (finding little relation between spending and quality of care) to cost savings in the real world. Aaron writes: The Dartmouth research, showing huge variation in the use of various medical procedures and large variations in per patient spending under Medicare, has been a revelation and a useful one. There is no way to explain such variation on medical grounds and it is problematic. But readers, including my former colleague Orszag, have taken an oversimplistic view of what the numbers mean and what to do about them. There are three really big problems with the common interpreta
Introduction: Reed Abelson and Gardiner Harris report in the New York Times that some serious statistical questions have been raised about the Dartmouth Atlas of Health Care, an influential project that reports huge differences in health care costs and practices in different places in the United States, suggesting large potential cost savings if more efficient practices are used. (A claim that is certainly plausible to me, given this notorious graph ; see here for background.) Here’s an example of a claim from the Dartmouth Atlas (just picking something that happens to be featured on their webpage right now): Medicare beneficiaries who move to some regions receive many more diagnostic tests and new diagnoses than those who move to other regions. This study, published in the New England Journal of Medicine, raises important questions about whether being given more diagnoses is beneficial to patients and may help to explain recent controversies about regional differences in spending. A
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Introduction: Economist David Backus writes : A casual reader of economic news can’t help but get the impression that the way to get the economy moving is to have people spend more — consume more, in the language of macroeconomics. Seems obvious, doesn’t it? At the risk of making the obvious complicated, I’d say it’s not so obvious. It’s also not obvious that consumption has gone down since the crisis, or that saving has gone up. So what’s going on with the labor market? I’ll get to the rest of the explanation, but first some background. The other day, I posted posted this remark from Backus: This is from my area of work, macroeconomics. The suggestion here is that the economy is growing slowly because consumers aren’t spending money. But how do we know it’s not the reverse: that consumers are spending less because the economy isn’t doing well. As a teacher, I can tell you that it’s almost impossible to get students to understand that the first statement isn’t obviously true
Introduction: Aaron Carroll shoots down a politically-loaded claim about cancer survival. Lots of useful background from science reporter Sharon Begley: With the United States spending more on healthcare than any other country — $2.5 trillion, or just over $8,000 per capita, in 2009 — the question has long been, is it worth it? At least for spending on cancer, a controversial new study answers with an emphatic “yes.” . . . Experts shown an advance copy of the paper by Reuters argued that the tricky statistics of cancer outcomes tripped up the authors. “This study is pure folly,” said biostatistician Dr. Don Berry of MD Anderson Cancer Center in Houston. “It’s completely misguided and it’s dangerous. Not only are the authors’ analyses flawed but their conclusions are also wrong.” Ouch. Arguably the study shouldn’t be getting any coverage at all, but given that it’s in the news, it’s good to see it get shot down. I wonder if the authors will respond to Don Berry and say they’re sorr
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Introduction: Hank Aaron at the Brookings Institution, who knows a lot more about policy than I do, had some interesting comments on the recent New York Times article about problems with the Dartmouth health care atlas. which I discussed a few hours ago . Aaron writes that much of the criticism in that newspaper article was off-base, but that there are real difficulties in translating the Dartmouth results (finding little relation between spending and quality of care) to cost savings in the real world. Aaron writes: The Dartmouth research, showing huge variation in the use of various medical procedures and large variations in per patient spending under Medicare, has been a revelation and a useful one. There is no way to explain such variation on medical grounds and it is problematic. But readers, including my former colleague Orszag, have taken an oversimplistic view of what the numbers mean and what to do about them. There are three really big problems with the common interpreta
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Introduction: Michael Betancourt will be speaking at Google and at the University of California, Berkeley. The Google talk is closed to outsiders (but if you work at Google, you should go!); the Berkeley talk is open to all: Friday March 22, 12:10 pm, Evans Hall 1011. Title of talk: Stan : Practical Bayesian Inference with Hamiltonian Monte Carlo Abstract: Practical implementations of Bayesian inference are often limited to approximation methods that only slowly explore the posterior distribution. By taking advantage of the curvature of the posterior, however, Hamiltonian Monte Carlo (HMC) efficiently explores even the most highly contorted distributions. In this talk I will review the foundations of and recent developments within HMC, concluding with a discussion of Stan, a powerful inference engine that utilizes HMC, automatic differentiation, and adaptive methods to minimize user input. This is cool stuff. And he’ll be showing the whirlpool movie!
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Introduction: David Hogg pointed me to this news article by Angela Saini: It’s not often that the quiet world of mathematics is rocked by a murder case. But last summer saw a trial that sent academics into a tailspin, and has since swollen into a fevered clash between science and the law. At its heart, this is a story about chance. And it begins with a convicted killer, “T”, who took his case to the court of appeal in 2010. Among the evidence against him was a shoeprint from a pair of Nike trainers, which seemed to match a pair found at his home. While appeals often unmask shaky evidence, this was different. This time, a mathematical formula was thrown out of court. The footwear expert made what the judge believed were poor calculations about the likelihood of the match, compounded by a bad explanation of how he reached his opinion. The conviction was quashed. . . . “The impact will be quite shattering,” says Professor Norman Fenton, a mathematician at Queen Mary, University of London.
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