andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-167 knowledge-graph by maker-knowledge-mining
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Introduction: Interesting article by Sharon Begley and Mary Carmichael. They discuss how there is tons of federal support for basic research but that there’s a big gap between research findings and medical applications–a gap that, according to them, arises not just from the inevitable problem that not all research hypotheses pan out, but because actual promising potential cures don’t get researched because of the cost. I have two thoughts on this. First, in my experience, research at any level requires a continuing forward momentum, a push from somebody to keep it going. I’ve worked on some great projects (some of which had Federal research funding) that ground to a halt because the original motivation died. I expect this is true with medical research also. One of the projects that I’m thinking of, which I’ve made almost no progress on for several years, I’m sure would make a useful contribution. I pretty much know it would work–it just takes work to make it work, and it’s hard to do this
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1 First, in my experience, research at any level requires a continuing forward momentum, a push from somebody to keep it going. [sent-4, score-0.604]
2 I’ve worked on some great projects (some of which had Federal research funding) that ground to a halt because the original motivation died. [sent-5, score-0.803]
3 I expect this is true with medical research also. [sent-6, score-0.482]
4 One of the projects that I’m thinking of, which I’ve made almost no progress on for several years, I’m sure would make a useful contribution. [sent-7, score-0.265]
5 I pretty much know it would work–it just takes work to make it work, and it’s hard to do this without the motivation of it being connected to other projects. [sent-8, score-0.419]
6 Begley and Carmichael discuss how various potential cures are not being developed because of the expense of animal and then human testing. [sent-10, score-0.867]
7 medical system, that simple experiments cost millions of dollars. [sent-13, score-0.526]
8 But I’m also confused: if these drugs are really “worth it” and would save lots of lives, wouldn’t it be worth it for the drug and medical device companies to expend the dollars to test them? [sent-14, score-1.146]
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same-blog 1 1.0000001 167 andrew gelman stats-2010-07-27-Why don’t more medical discoveries become cures?
Introduction: Interesting article by Sharon Begley and Mary Carmichael. They discuss how there is tons of federal support for basic research but that there’s a big gap between research findings and medical applications–a gap that, according to them, arises not just from the inevitable problem that not all research hypotheses pan out, but because actual promising potential cures don’t get researched because of the cost. I have two thoughts on this. First, in my experience, research at any level requires a continuing forward momentum, a push from somebody to keep it going. I’ve worked on some great projects (some of which had Federal research funding) that ground to a halt because the original motivation died. I expect this is true with medical research also. One of the projects that I’m thinking of, which I’ve made almost no progress on for several years, I’m sure would make a useful contribution. I pretty much know it would work–it just takes work to make it work, and it’s hard to do this
2 0.13553424 411 andrew gelman stats-2010-11-13-Ethical concerns in medical trials
Introduction: I just read this article on the treatment of medical volunteers, written by doctor and bioethicist Carl Ellliott. As a statistician who has done a small amount of consulting for pharmaceutical companies, I have a slightly different perspective. As a doctor, Elliott focuses on individual patients, whereas, as a statistician, I’ve been trained to focus on the goal of accurately estimate treatment effects. I’ll go through Elliott’s article and give my reactions. Elliott: In Miami, investigative reporters for Bloomberg Markets magazine discovered that a contract research organisation called SFBC International was testing drugs on undocumented immigrants in a rundown motel; since that report, the motel has been demolished for fire and safety violations. . . . SFBC had recently been named one of the best small businesses in America by Forbes magazine. The Holiday Inn testing facility was the largest in North America, and had been operating for nearly ten years before inspecto
3 0.13456298 322 andrew gelman stats-2010-10-06-More on the differences between drugs and medical devices
Introduction: Someone who works in statistics in the pharmaceutical industry (but prefers to remain anonymous) sent me this update to our discussion on the differences between approvals of drugs and medical devices: The ‘substantial equivalence’ threshold is a very outdated. Basically the FDA has to follow federal law and the law is antiquated and leads to two extraordinarily different paths for device approval. You could have a very simple but first-in-kind device with an easy to understand physiological mechanism of action (e.g. the FDA approved a simple tiny stent that would relieve pressure from a glaucoma patient’s eye this summer). This device would require a standard (likely controlled) trial at the one-sided 0.025 level. Even after the trial it would likely go to a panel where outside experts (e.g.practicing & academic MDs and statisticians) hear evidence from the company and FDA and vote on its safety and efficacy. FDA would then rule, consider the panel’s vote, on whether to appro
Introduction: Sander Wagner writes: I just read the post on ethical concerns in medical trials. As there seems to be a lot more pressure on private researchers i thought it might be a nice little exercise to compare p-values from privately funded medical trials with those reported from publicly funded research, to see if confirmation pressure is higher in private research (i.e. p-values are closer to the cutoff levels for significance for the privately funded research). Do you think this is a decent idea or are you sceptical? Also are you aware of any sources listing a large number of representative medical studies and their type of funding? My reply: This sounds like something worth studying. I don’t know where to get data about this sort of thing, but now that it’s been blogged, maybe someone will follow up.
Introduction: I had a brief email exchange with Jeff Leek regarding our recent discussions of replication, criticism, and the self-correcting process of science. Jeff writes: (1) I can see the problem with serious, evidence-based criticisms not being published in the same journal (and linked to) studies that are shown to be incorrect. I have been mostly seeing these sorts of things show up in blogs. But I’m not sure that is a bad thing. I think people read blogs more than they read the literature. I wonder if this means that blogs will eventually be a sort of “shadow literature”? (2) I think there is a ton of bad literature out there, just like there is a ton of bad stuff on Google. If we focus too much on the bad stuff we will be paralyzed. I still manage to find good papers despite all the bad papers. (3) I think one positive solution to this problem is to incentivize/publish referee reports and give people credit for a good referee report just like they get credit for a good paper. T
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Introduction: Interesting article by Sharon Begley and Mary Carmichael. They discuss how there is tons of federal support for basic research but that there’s a big gap between research findings and medical applications–a gap that, according to them, arises not just from the inevitable problem that not all research hypotheses pan out, but because actual promising potential cures don’t get researched because of the cost. I have two thoughts on this. First, in my experience, research at any level requires a continuing forward momentum, a push from somebody to keep it going. I’ve worked on some great projects (some of which had Federal research funding) that ground to a halt because the original motivation died. I expect this is true with medical research also. One of the projects that I’m thinking of, which I’ve made almost no progress on for several years, I’m sure would make a useful contribution. I pretty much know it would work–it just takes work to make it work, and it’s hard to do this
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Introduction: I just read this article on the treatment of medical volunteers, written by doctor and bioethicist Carl Ellliott. As a statistician who has done a small amount of consulting for pharmaceutical companies, I have a slightly different perspective. As a doctor, Elliott focuses on individual patients, whereas, as a statistician, I’ve been trained to focus on the goal of accurately estimate treatment effects. I’ll go through Elliott’s article and give my reactions. Elliott: In Miami, investigative reporters for Bloomberg Markets magazine discovered that a contract research organisation called SFBC International was testing drugs on undocumented immigrants in a rundown motel; since that report, the motel has been demolished for fire and safety violations. . . . SFBC had recently been named one of the best small businesses in America by Forbes magazine. The Holiday Inn testing facility was the largest in North America, and had been operating for nearly ten years before inspecto
Introduction: Sander Wagner writes: I just read the post on ethical concerns in medical trials. As there seems to be a lot more pressure on private researchers i thought it might be a nice little exercise to compare p-values from privately funded medical trials with those reported from publicly funded research, to see if confirmation pressure is higher in private research (i.e. p-values are closer to the cutoff levels for significance for the privately funded research). Do you think this is a decent idea or are you sceptical? Also are you aware of any sources listing a large number of representative medical studies and their type of funding? My reply: This sounds like something worth studying. I don’t know where to get data about this sort of thing, but now that it’s been blogged, maybe someone will follow up.
Introduction: After learning of a news article by Amy Harmon on problems with medical trials–sometimes people are stuck getting the placebo when they could really use the experimental treatment, and it can be a life-or-death difference, John Langford discusses some fifteen-year-old work on optimal design in machine learning and makes the following completely reasonable point: With reasonable record keeping of existing outcomes for the standard treatments, there is no need to explicitly assign people to a control group with the standard treatment, as that approach is effectively explored with great certainty. Asserting otherwise would imply that the nature of effective treatments for cancer has changed between now and a year ago, which denies the value of any clinical trial. . . . Done the right way, the clinical trial for a successful treatment would start with some initial small pool (equivalent to “phase 1″ in the article) and then simply expanded the pool of participants over time as it
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Introduction: I worked at the U.S. Naval Research Laboratory for four summers during high school and college. I spent much of my time writing a computer program to do thermal analysis for an experiment that we put on the space shuttle. The facility I developed with the finite-element method came in handy in my job at Bell Labs the following summers. I was working for C. H. Tsao and Jim Adams in the Laboratory for Cosmic Ray Physics. We were estimating the distribution of isotopes in cosmic rays using a pile of track detectors. To get accurate measurements, you want these plastic disks to be as close as possible to a constant temperature, so we designed an elaborate wrapping of thermal blankets. My program computed the temperature of the detectors during the year that the Long Duration Exposure Facility (including our experiment and a bunch of others) was scheduled to be in orbit. The input is the heat from solar radiation (easy enough to compute given the trajectory). On the computer I tr
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Introduction: Interesting article by Sharon Begley and Mary Carmichael. They discuss how there is tons of federal support for basic research but that there’s a big gap between research findings and medical applications–a gap that, according to them, arises not just from the inevitable problem that not all research hypotheses pan out, but because actual promising potential cures don’t get researched because of the cost. I have two thoughts on this. First, in my experience, research at any level requires a continuing forward momentum, a push from somebody to keep it going. I’ve worked on some great projects (some of which had Federal research funding) that ground to a halt because the original motivation died. I expect this is true with medical research also. One of the projects that I’m thinking of, which I’ve made almost no progress on for several years, I’m sure would make a useful contribution. I pretty much know it would work–it just takes work to make it work, and it’s hard to do this
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Introduction: Becky Passonneau and colleagues at the Center for Computational Learning Systems (CCLS) at Columbia have been working on a project for ConEd (New York’s major electric utility) to rank structures based on vulnerability to secondary events (e.g., transformer explosions, cable meltdowns, electrical fires). They’ve been using the R implementation BayesTree of Chipman, George and McCulloch’s Bayesian Additive Regression Trees (BART). BART is a Bayesian non-parametric method that is non-identifiable in two ways. Firstly, it is an additive tree model with a fixed number of trees, the indexes of which aren’t identified (you get the same predictions in a model swapping the order of the trees). This is the same kind of non-identifiability you get with any mixture model (additive or interpolated) with an exchangeable prior on the mixture components. Secondly, the trees themselves have varying structure over samples in terms of number of nodes and their topology (depth, branching, etc
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Introduction: Chris Blattman writes : Matching is not an identification strategy a solution to your endogeneity problem; it is a weighting scheme. Saying matching will reduce endogeneity bias is like saying that the best way to get thin is to weigh yourself in kilos. The statement makes no sense. It confuses technique with substance. . . . When you run a regression, you control for the X you can observe. When you match, you are simply matching based on those same X. . . . I see what Chris is getting at–matching, like regression, won’t help for the variables you’re not controlling for–but I disagree with his characterization of matching as a weighting scheme. I see matching as a way to restrict your analysis to comparable cases. The statistical motivation: robustness. If you had a good enough model, you wouldn’t neet to match, you’d just fit the model to the data. But in common practice we often use simple regression models and so it can be helpful to do some matching first before regress
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Introduction: Ooooooh, I never ever thought I’d have a legitimate excuse to tell this story, and now I do! The story took place many years ago, but first I have to tell you what made me think of it: Rasmus Bååth posted the following comment last month: On airplane tickets a Swedish “å” is written as “aa” resulting in Rasmus Baaaath. Once I bought a ticket online and five minutes later a guy from Lufthansa calls me and asks if I misspelled my name… OK, now here’s my story (which is not nearly as good). A long time ago (but when I was already an adult), I was in England for some reason, and I thought I’d take a day trip from London to Bath. So here I am on line, trying to think of what to say at the ticket counter. I remember that in England, they call Bath, Bahth. So, should I ask for “a ticket to Bahth”? I’m not sure, I’m afraid that it will sound silly, like I’m trying to fake an English accent. So, when I get to the front of the line, I say, hesitantly, “I’d like a ticket to Bath?
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Introduction: Bob told me the other day (the other week, actually, as I’m stacking up posts here with a roughly one-month delay) that I shouldn’t try to compete with the electrical engineers when it comes to length of C.V.: according to Bob, these dudes can have over two thousand publications! How do they do it? First, an EE prof will have tons of graduate students and postdocs, they’re all writing papers and presenting at conferences, and they all stick his name on the author list. Second, these students and postdocs write up and publish every experiment they do . Including (especially!) computer experiments. And . . . all these people writing paper cite each other, so they quickly rack up thousands of citations. Upon hearing this, my first reaction to this was fear, plain and simple. One of the distinguishing characteristics of my own research record is that I have so many publications and citations. Those electrical engineers . . . how dare they go around devaluing my currency!
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