andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2316 knowledge-graph by maker-knowledge-mining
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Introduction: Paul Alper writes: You recently posted on graphs and how to convey information. I don’t believe you have ever posted anything on this dynamite randomized clinical trial of 90,000 (!!) 40-59 year-old women over a 25-year period (also !!). The graphs below are figures 2, 3 and 4 respectively, of http://www.bmj.com/content/348/bmj.g366 The control was physical exam only and the treatment was physical exam plus mammography. The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment). Note the superfluousness of the p-values. There is an accompanying editorial in the BMJ http://www.bmj.com/content/348/bmj.g1403 which refers to “vested interests” which can override any statistics, no matter how striking: We agree with Miller and colleagues that “the rationale for screening by mammography be urgently reassessed by policy makers.” As time goes
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1 Paul Alper writes: You recently posted on graphs and how to convey information. [sent-1, score-0.227]
2 I don’t believe you have ever posted anything on this dynamite randomized clinical trial of 90,000 (! [sent-2, score-0.315]
3 The graphs below are figures 2, 3 and 4 respectively, of http://www. [sent-7, score-0.076]
4 g366 The control was physical exam only and the treatment was physical exam plus mammography. [sent-10, score-0.827]
5 The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment). [sent-11, score-0.963]
6 There is an accompanying editorial in the BMJ http://www. [sent-13, score-0.155]
7 g1403 which refers to “vested interests” which can override any statistics, no matter how striking: We agree with Miller and colleagues that “the rationale for screening by mammography be urgently reassessed by policy makers. [sent-16, score-1.08]
8 ” As time goes by we do indeed need more efficient mechanisms to reconsider priorities and recommendations for mammography screening and other medical interventions. [sent-17, score-1.278]
9 This is not an easy task, because governments, research funders, scientists, and medical practitioners may have vested interests in continuing activities that are well established. [sent-18, score-0.573]
10 And for the aging males in your audience: Nevertheless, the UK National Screening Committee does recommend mammography screening for breast cancer but not prostate specific antigen screening for prostate cancer. [sent-19, score-2.364]
11 Because the scientific rationale to recommend screening or not does not differ noticeably between breast and prostate cancer, political pressure and beliefs might have a role. [sent-20, score-1.388]
12 Although I guess the point is that a thorough physical exam won’t miss much. [sent-23, score-0.513]
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same-blog 1 0.99999994 2316 andrew gelman stats-2014-05-03-“The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment)”
Introduction: Paul Alper writes: You recently posted on graphs and how to convey information. I don’t believe you have ever posted anything on this dynamite randomized clinical trial of 90,000 (!!) 40-59 year-old women over a 25-year period (also !!). The graphs below are figures 2, 3 and 4 respectively, of http://www.bmj.com/content/348/bmj.g366 The control was physical exam only and the treatment was physical exam plus mammography. The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment). Note the superfluousness of the p-values. There is an accompanying editorial in the BMJ http://www.bmj.com/content/348/bmj.g1403 which refers to “vested interests” which can override any statistics, no matter how striking: We agree with Miller and colleagues that “the rationale for screening by mammography be urgently reassessed by policy makers.” As time goes
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Introduction: Sharad had a survey sampling question: We’re trying to use mechanical turk to conduct some surveys, and have quickly discovered that turkers tend to be quite young. We’d really like a representative sample of the U.S., or at the least be able to recruit a diverse enough sample from turk that we can post-stratify to adjust the estimates. The approach we ended up taking is to pay turkers a small amount to answer a couple of screening questions (age & sex), and then probabilistically recruit individuals to complete the full survey (for more money) based on the estimated turk population parameters and our desired target distribution. We use rejection sampling, so the end result is that individuals who are invited to take the full survey look as if they came from a representative sample, at least in terms of age and sex. I’m wondering whether this sort of technique—a two step design in which participants are first screened and then probabilistically selected to mimic a target distributio
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Introduction: Mon : Crowdstorming a dataset Tues : Ken Rice presents a unifying approach to statistical inference and hypothesis testing Wed : The health policy innovation center: how best to move from pilot studies to large-scale practice? Thurs : Heller, Heller, and Gorfine on univariate and multivariate information measures Fri : Discovering general multidimensional associations Sat : “The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment)” Sun : Honored oldsters write about statistics
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Introduction: Paul Alper writes: You recently posted my moving and widening the goalposts contention. In it, I mentioned “how diagnoses increase markedly while deaths are flatlined” indicating that we are being overdiagnosed and overtreated. Above are 5 frightening graphs which illustrate the phenomenon. Defenders of the system might (ludicrously) contend that it is precisely the aggressive medical care that is responsible for keeping the cancers under control. The prostate cancer graph is particularly interesting because it shows the peaking of the PSA-driven cause of treatment in the 1990s which then falls off as the evidence accumulates that the PSA was far from a perfect indicator. In contrast is the thyroid cancer which zooms skyward even as the death rate is absolutely (dead) flat. And of course here’s the famous cross-country comparison that some find “ schlocky ” but which I (and many others) find compelling :
Introduction: Ken Rice writes: In the recent discussion on stopping rules I saw a comment that I wanted to chip in on, but thought it might get a bit lost, in the already long thread. Apologies in advance if I misinterpreted what you wrote, or am trying to tell you things you already know. The comment was: “In Bayesian decision making, there is a utility function and you choose the decision with highest expected utility. Making a decision based on statistical significance does not correspond to any utility function.” … which immediately suggests this little 2010 paper; A Decision-Theoretic Formulation of Fisher’s Approach to Testing, The American Statistician, 64(4) 345-349. It contains utilities that lead to decisions that very closely mimic classical Wald tests, and provides a rationale for why this utility is not totally unconnected from how some scientists think. Some (old) slides discussing it are here . A few notes, on things not in the paper: * I know you don’t like squared-
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Introduction: Paul Alper writes: You recently posted on graphs and how to convey information. I don’t believe you have ever posted anything on this dynamite randomized clinical trial of 90,000 (!!) 40-59 year-old women over a 25-year period (also !!). The graphs below are figures 2, 3 and 4 respectively, of http://www.bmj.com/content/348/bmj.g366 The control was physical exam only and the treatment was physical exam plus mammography. The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment). Note the superfluousness of the p-values. There is an accompanying editorial in the BMJ http://www.bmj.com/content/348/bmj.g1403 which refers to “vested interests” which can override any statistics, no matter how striking: We agree with Miller and colleagues that “the rationale for screening by mammography be urgently reassessed by policy makers.” As time goes
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Introduction: Paul Alper writes: You recently posted my moving and widening the goalposts contention. In it, I mentioned “how diagnoses increase markedly while deaths are flatlined” indicating that we are being overdiagnosed and overtreated. Above are 5 frightening graphs which illustrate the phenomenon. Defenders of the system might (ludicrously) contend that it is precisely the aggressive medical care that is responsible for keeping the cancers under control. The prostate cancer graph is particularly interesting because it shows the peaking of the PSA-driven cause of treatment in the 1990s which then falls off as the evidence accumulates that the PSA was far from a perfect indicator. In contrast is the thyroid cancer which zooms skyward even as the death rate is absolutely (dead) flat. And of course here’s the famous cross-country comparison that some find “ schlocky ” but which I (and many others) find compelling :
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Introduction: The (U.S.) “President’s Cancer Panel” has released its 2008-2009 annual report, which includes a cover letter that says “the true burden of environmentally induced cancer has been grossly underestimated.” The report itself discusses exposures to various types of industrial chemicals, some of which are known carcinogens, in some detail, but gives nearly no data or analysis to suggest that these exposures are contributing to significant numbers of cancers. In fact, there is pretty good evidence that they are not. The plot above shows age-adjusted cancer mortality for men, by cancer type, in the U.S. The plot below shows the same for women. In both cases, the cancers with the highest mortality rates are shown, but not all cancers (e.g. brain cancer is not shown). For what it’s worth, I’m not sure how trustworthy the rates are from the 1930s — it seems possible that reporting, autopsies, or both, were less careful during the Great Depression — so I suggest focusing on the r
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Introduction: Paul Alper writes: You recently posted on graphs and how to convey information. I don’t believe you have ever posted anything on this dynamite randomized clinical trial of 90,000 (!!) 40-59 year-old women over a 25-year period (also !!). The graphs below are figures 2, 3 and 4 respectively, of http://www.bmj.com/content/348/bmj.g366 The control was physical exam only and the treatment was physical exam plus mammography. The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment). Note the superfluousness of the p-values. There is an accompanying editorial in the BMJ http://www.bmj.com/content/348/bmj.g1403 which refers to “vested interests” which can override any statistics, no matter how striking: We agree with Miller and colleagues that “the rationale for screening by mammography be urgently reassessed by policy makers.” As time goes
Introduction: Marc Tanguay writes in with a specific question that has a very general answer. First, the question: I [Tanguay] am currently running a MCMC for which I have 3 parameters that are restricted to a specific space. 2 are bounded between 0 and 1 while the third is binary and updated by a Beta-Binomial. Since my priors are also bounded, I notice that, conditional on All the rest (which covers both data and other parameters), the density was not varying a lot within the space of the parameters. As a result, the acceptance rate is high, about 85%, and this despite the fact that all the parameter’s space is explore. Since in your book, the optimal acceptance rates prescribed are lower that 50% (in case of multiple parameters), do you think I should worry about getting 85%. Or is this normal given the restrictions on the parameters? First off: Yes, my guess is that you should be taking bigger jumps. 85% seems like too high an acceptance rate for Metropolis jumping. More generally, t
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Introduction: I’m just glad that universities don’t sanction professors for publishing false theorems. If the guy really is nailed by the feds for fraud, I hope they don’t throw him in prison. In general, prison time seems like a brutal, expensive, and inefficient way to punish people. I’d prefer if the government just took 95% of his salary for several years, made him do community service (cleaning equipment at the local sewage treatment plant, perhaps; a lab scientist should be good at this sort of thing, no?), etc. If restriction of this dude’s personal freedom is judged be part of the sentence, he could be given some sort of electronic tag that would send a message to the police if he were ever more than 3 miles from his home. But no need to bill the taxpayers for the cost of keeping him in prison.
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