Posts Tagged ‘ Bayesian statistics ’

When do statistical rules affect drug approval?

July 22, 2016
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When do statistical rules affect drug approval?

Someone writes in: I have MS and take a disease-modifying drug called Copaxone. Sandoz developed a generic version​ of Copaxone​ and filed for FDA approval. Teva, the manufacturer of Copaxone, filed a petition opposing that approval (surprise!). FDA rejected Teva’s petitions and approved the generic. My insurance company encouraged me to switch to the generic. […] The post When do statistical rules affect drug approval? appeared first on Statistical Modeling,…

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Going beyond confidence intervals

July 20, 2016
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Anders Lamberg writes: In an article by Tom Sigfried, Science News, July 3 2014, “Scientists’ grasp of confidence intervals doesn’t inspire confidence” you are cited: “Gelman himself makes the point most clearly, though, that a 95 percent probability that a confidence interval contains the mean refers to repeated sampling, not any one individual interval.” I […] The post Going beyond confidence intervals appeared first on Statistical Modeling, Causal Inference, and…

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Bayesian Linear Mixed Models using Stan: A tutorial for psychologists, linguists, and cognitive scientists

July 19, 2016
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This article by Tanner Sorensen, Sven Hohenstein, and Shravan Vasishth might be of interest to some of you. The post Bayesian Linear Mixed Models using Stan: A tutorial for psychologists, linguists, and cognitive scientists appeared first on Statistica...

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Moving statistical theory from a “discovery” framework to a “measurement” framework

July 18, 2016
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Avi Adler points to this post by Felix Schönbrodt on “What’s the probability that a significant p-value indicates a true effect?” I’m sympathetic to the goal of better understanding what’s in a p-value (see for example my paper with John Carlin on type M and type S errors) but I really don’t like the framing […] The post Moving statistical theory from a “discovery” framework to a “measurement” framework appeared…

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“Pointwise mutual information as test statistics”

July 17, 2016
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Christian Bartels writes: Most of us will probably agree that making good decisions under uncertainty based on limited data is highly important but remains challenging. We have decision theory that provides a framework to reduce risks of decisions under uncertainty with typical frequentist test statistics being examples for controlling errors in absence of prior knowledge. […] The post “Pointwise mutual information as test statistics” appeared first on Statistical Modeling, Causal…

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One-day workshop on causal inference (NYC, Sat. 16 July)

July 15, 2016
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James Savage is teaching a one-day workshop on causal inference this coming Saturday (16 July) in New York using RStanArm. Here’s a link to the details: One-day workshop on causal inference Here’s the course outline: How do prices affect sales? What is the uplift from a marketing decision? By how much will studying for an […] The post One-day workshop on causal inference (NYC, Sat. 16 July) appeared first on…

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Finish line (nearly)

July 15, 2016
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Finish line (nearly)

We are very close to the finish line $-$ that's being able to finally submit the BCEA book to the editor (Springer).This has been a rather long journey, but I think the current version (I dread using the word "final" just yet...) is very good, I think....

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Causal and predictive inference in policy research

July 9, 2016
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Todd Rogers pointed me to a paper by Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer that begins: Empirical policy research often focuses on causal inference. Since policy choices seem to depend on understanding the counterfactual—what happens with and without a policy—this tight link of causality and policy seems natural. While this link holds […] The post Causal and predictive inference in policy research appeared first on Statistical Modeling,…

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Reproducible Research with Stan, R, knitr, Docker, and Git (with free GitLab hosting)

July 7, 2016
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Reproducible Research with Stan, R, knitr, Docker, and Git (with free GitLab hosting)

Jon Zelner recently developed a neat Docker packaging of Stan, R, and knitr for fully reproducible research. The first in his series of posts (with links to the next parts) is here: * Reproducibility, part 1 The post on making changes online and auto-updating results using GitLab’s continuous integration service is here: * GitLab continuous […] The post Reproducible Research with Stan, R, knitr, Docker, and Git (with free GitLab…

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Causal mediation

July 7, 2016
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Causal mediation

Judea Pearl points me to this discussion with Kosuke Imai at a conference on causal mediation. I continue to think that the most useful way to think about mediation is in terms of a joint or multivariate outcome, and I continue to think that if we want to understand mediation, we need to think about […] The post Causal mediation appeared first on Statistical Modeling, Causal Inference, and Social Science.

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