andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1906 knowledge-graph by maker-knowledge-mining
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Introduction: Brett Keller points to a recent news article by Sharon Begley and Robin Respaut: A lot of doctors, hospitals and other healthcare providers in the United States decline to treat people who can’t pay, or have inadequate insurance, among other reasons. What sets CTCA [Cancer Treatment Centers of America] apart is that rejecting certain patients and, even more, culling some of its patients from its survival data lets the company tout in ads and post on its website patient outcomes that look dramatically better than they would if the company treated all comers. These are the rosy survival numbers . . . Details: CTCA reports on its website that the percentage of its patients who are alive after six months, a year, 18 months and longer regularly tops national figures. For instance, 60 percent of its non-small-cell lung cancer patients are alive at six months, CTCA says, compared to 38 percent nationally. And 64 percent of its prostate cancer patients are alive at three years, vers
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1 Brett Keller points to a recent news article by Sharon Begley and Robin Respaut: A lot of doctors, hospitals and other healthcare providers in the United States decline to treat people who can’t pay, or have inadequate insurance, among other reasons. [sent-1, score-0.139]
2 Details: CTCA reports on its website that the percentage of its patients who are alive after six months, a year, 18 months and longer regularly tops national figures. [sent-6, score-0.857]
3 For instance, 60 percent of its non-small-cell lung cancer patients are alive at six months, CTCA says, compared to 38 percent nationally. [sent-7, score-1.022]
4 And 64 percent of its prostate cancer patients are alive at three years, versus 38 percent nationally. [sent-8, score-0.974]
5 Such claims are misleading, according to nine experts in cancer and medical statistics whom Reuters asked to review CTCA’s survival numbers and its statistical methodology. [sent-9, score-0.648]
6 The experts were unanimous that CTCA’s patients are different from the patients the company compares them to, in a way that skews their survival data. [sent-10, score-1.315]
7 It has relatively few elderly patients, even though cancer is a disease of the aged. [sent-11, score-0.249]
8 It has almost none who are uninsured or covered by Medicaid – patients who tend to die sooner if they develop cancer and who are comparatively numerous in national statistics. [sent-12, score-0.834]
9 Accepting only selected patients and calculating survival outcomes from only some of them “is a huge bias and gives an enormous advantage to CTCA,” said biostatistician Donald Berry . [sent-16, score-0.907]
10 Holmes said she would try to “let those people down easy. [sent-20, score-0.059]
11 The ads also challenge viewers to “compare our treatment results to national averages. [sent-24, score-0.182]
12 ” Doing so, on the company’s website, shows that CTCA’s reported survival outcomes regularly beat those averages. [sent-25, score-0.401]
13 Experts in medical data who reviewed CTCA’s claims for Reuters say those claims are suspect because of what they called deviations from best practices in statistics – in particular, comparing its carefully selected patients to those nationwide. [sent-26, score-0.636]
14 “It makes their data look better than it is,” said Robert Strawderman, professor and chairman of biostatistics at the University of Rochester. [sent-27, score-0.104]
15 “So the comparisons used to suggest that CTCA has better survival rates are pretty meaningless. [sent-28, score-0.257]
16 In contrast, I doubt that there’s any particular reason for CTCA to restrict its cancer treatments to the least-sick patients. [sent-31, score-0.209]
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same-blog 1 0.99999988 1906 andrew gelman stats-2013-06-19-“Behind a cancer-treatment firm’s rosy survival claims”
Introduction: Brett Keller points to a recent news article by Sharon Begley and Robin Respaut: A lot of doctors, hospitals and other healthcare providers in the United States decline to treat people who can’t pay, or have inadequate insurance, among other reasons. What sets CTCA [Cancer Treatment Centers of America] apart is that rejecting certain patients and, even more, culling some of its patients from its survival data lets the company tout in ads and post on its website patient outcomes that look dramatically better than they would if the company treated all comers. These are the rosy survival numbers . . . Details: CTCA reports on its website that the percentage of its patients who are alive after six months, a year, 18 months and longer regularly tops national figures. For instance, 60 percent of its non-small-cell lung cancer patients are alive at six months, CTCA says, compared to 38 percent nationally. And 64 percent of its prostate cancer patients are alive at three years, vers
2 0.16399886 21 andrew gelman stats-2010-05-07-Environmentally induced cancer “grossly underestimated”? Doubtful.
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
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: People keep pointing me to this excellent news article by David Brown, about a scientist who was convicted of data manipulation: In all, 330 patients were randomly assigned to get either interferon gamma-1b or placebo injections. Disease progression or death occurred in 46 percent of those on the drug and 52 percent of those on placebo. That was not a significant difference, statistically speaking. When only survival was considered, however, the drug looked better: 10 percent of people getting the drug died, compared with 17 percent of those on placebo. However, that difference wasn’t “statistically significant,” either. Specifically, the so-called P value — a mathematical measure of the strength of the evidence that there’s a true difference between a treatment and placebo — was 0.08. . . . Technically, the study was a bust, although the results leaned toward a benefit from interferon gamma-1b. Was there a group of patients in which the results tipped? Harkonen asked the statis
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: Brett Keller points to a recent news article by Sharon Begley and Robin Respaut: A lot of doctors, hospitals and other healthcare providers in the United States decline to treat people who can’t pay, or have inadequate insurance, among other reasons. What sets CTCA [Cancer Treatment Centers of America] apart is that rejecting certain patients and, even more, culling some of its patients from its survival data lets the company tout in ads and post on its website patient outcomes that look dramatically better than they would if the company treated all comers. These are the rosy survival numbers . . . Details: CTCA reports on its website that the percentage of its patients who are alive after six months, a year, 18 months and longer regularly tops national figures. For instance, 60 percent of its non-small-cell lung cancer patients are alive at six months, CTCA says, compared to 38 percent nationally. And 64 percent of its prostate cancer patients are alive at three years, vers
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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: Zosia Chustecka writes : Much of the previous work on the link between cancer and nightshifts has focused on breast cancer . . . The latest report, focusing on ovarian cancer, was published in the April issue of Occupational and Environmental Medicine. This increase in the risk for ovarian cancer with nightshift work is consistent with, and of similar magnitude to, the risk for breast cancer, say lead author Parveen Bhatti, PhD, and colleagues from the epidemiology program at the Fred Hutchinson Cancer Research Center in Seattle, Washington. The researchers examined data from a local population-based cancer registry that is part of the Surveillance Epidemiology and End Results (SEER) Program. They identified 1101 women with advanced epithelial ovarian cancer, 389 with borderline disease, and 1832 without ovarian cancer (control group). The women, who were 35 to 74 years of age, were asked about the hours they worked, and specifically whether they had ever worked the nig
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Introduction: People keep pointing me to this excellent news article by David Brown, about a scientist who was convicted of data manipulation: In all, 330 patients were randomly assigned to get either interferon gamma-1b or placebo injections. Disease progression or death occurred in 46 percent of those on the drug and 52 percent of those on placebo. That was not a significant difference, statistically speaking. When only survival was considered, however, the drug looked better: 10 percent of people getting the drug died, compared with 17 percent of those on placebo. However, that difference wasn’t “statistically significant,” either. Specifically, the so-called P value — a mathematical measure of the strength of the evidence that there’s a true difference between a treatment and placebo — was 0.08. . . . Technically, the study was a bust, although the results leaned toward a benefit from interferon gamma-1b. Was there a group of patients in which the results tipped? Harkonen asked the statis
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Introduction: “Even as you get near the upper reaches of the normal weight range, you begin to see increases in chronic diseases,” said JoAnn Manson, chief of the Division of Preventive Medicine, Brigham and Women’s Hospital, HMS Michael and Lee Bell Professor of Women’s Health, and HSPH professor of epidemiology. “It’s a clear gradient of increase.” Yeah, she would say that. Thin people. And then there’s Frank Hu, professor of nutrition at Harvard: The studies that Flegal [the author of the original study finding a negative correlation between body mass index and mortality] did use included many samples of people who were chronically ill, current smokers and elderly, according to Hu. These factors are associated with weight loss and increased mortality. In other words, people are not dying because they are slim, he said. They are slim because they are dying—of cancer or old age, for example. By doing a meta-analysis of studies that did not properly control for this bias, Flegal amplif
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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
2 0.92128462 221 andrew gelman stats-2010-08-21-Busted!
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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Introduction: Brett Keller points to a recent news article by Sharon Begley and Robin Respaut: A lot of doctors, hospitals and other healthcare providers in the United States decline to treat people who can’t pay, or have inadequate insurance, among other reasons. What sets CTCA [Cancer Treatment Centers of America] apart is that rejecting certain patients and, even more, culling some of its patients from its survival data lets the company tout in ads and post on its website patient outcomes that look dramatically better than they would if the company treated all comers. These are the rosy survival numbers . . . Details: CTCA reports on its website that the percentage of its patients who are alive after six months, a year, 18 months and longer regularly tops national figures. For instance, 60 percent of its non-small-cell lung cancer patients are alive at six months, CTCA says, compared to 38 percent nationally. And 64 percent of its prostate cancer patients are alive at three years, vers
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Introduction: Bill Harris writes: I was re-reading your and Shalizi’s “Philosophy and the practice of Bayesian statistics” [see also the rejoinder ] and noticed a statement near the end of section 6 about paradigm shifts coming in different magnitudes over different time spans. That reminded me of the almost-mystical ideas surrounding 1/f (f being frequency”) noise in some areas — the notion that almost everything exhibits that effect, and that effect extends to arbitrarily low f. (I sense the idea only gets mystical when f gets low enough so that the event that may happen stochastically is really big—say, you model the height of waves in the Atlantic as 1/f and discover that, at some low frequency, Bermuda becomes submerged. In other words, does the same mechanism that accounts for physical vibrations in the range of Hertz also account for the creation and destruction of islands that may occur in the range of reciprocal centuries?) When I first encountered 1/f noise in the area of electr
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