Blog Archives

Managing biological data

Jon Udell’s latest Interviews with Innovators podcast features Randall Julian of Indigo BioSystems. I found this episode particularly interesting because it deals with issues I have some experience with. The problems in managing biological data begin with how to store

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Posted in Clinical trials

A case for robust Bayesian priors

A paper I wrote with Jairo Fúquene and Luis Pericchi is now available online. A Case for Robust Bayesian Priors with Applications to Clinical Trials Jairo Fúquene, John Cook, and Luis Pericchi Bayesian Analysis (2009) 4, Number 4, pp. 817–846.

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Posted in Clinical trials

Bayesian clinical trials in one zip code

I recently ran across this quote from Mithat Gönen of Memorial Sloan-Kettering Cancer Center: While there are certainly some at other centers, the bulk of applied Bayesian clinical trial design in this country is largely confined to a single zip

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Posted in Clinical trials

Off to Puerto Rico

I’m leaving today for San Juan. I’m giving a couple talks at a conference on clinical trials. Puerto Rico is beautiful. (I want to say a “lovely island,” but then the song America from West Side Story gets stuck in

Posted in Clinical trials

R package for robust priors

Jairo Fuquene has released an R package on CRAN to accompany our paper A Case for Robust Bayesian priors with Applications to Binary Clinical Trials Jairo A. Fuquene P., John D. Cook, Luis Raul Pericchi

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Posted in Clinical trials, Statistics

Science versus medicine

Before I started working for a cancer center, I was not aware of the tension between science and medicine. Popular perception is that the two go together hand and glove, but that’s not always true. Physicians are trained to use

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Posted in Clinical trials

Probability that a study result is true

Suppose a new study comes out saying a drug or a food or a habit lowers your risk of some disease. What is the probability that the study’s result is correct? Obviously this is a very important question, but one

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Posted in Clinical trials, Science, Statistics

Sometimes it's right under your nose

Neptune was discovered in 1846. But Galileo’s notebooks describe a “star” he saw on 28 December 1612 and 2 January 1613 that we now know was Neptune. Galileo even noticed that his star was in a slightly different location for

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Posted in Clinical trials, Creativity, Science

How to pick simulation scenarios

People new to simulation start by picking scenarios based on what they hope will happen. That’s OK, but it’s more important to pick scenarios that you expect are likely to happen or fear might happen.

Posted in Clinical trials

Drug looks promising, come back in 30 years

The most recent 60-Second Science podcast summarizes a paper in Science magazine reporting that the average interval between a drug being deemed “promising” and the first paper appearing showing clinical effectiveness is 24 years. Note that the publication of a

Posted in Clinical trials

Random inequalities VII: three or more variables

The previous posts in this series have looked at P(X > Y), the probability that a sample from a random variable X is greater than a sample from an independent random variable Y. In applications, X and Y have different

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Posted in Clinical trials, Math

Random inequalities VI: gamma distributions

This post looks at computing P(X > Y) where X and Y are gamma random variables. These inequalities are central to the Thall-Wooten method of monitoring single-arm clinical trials with time-to-event outcomes. They also are central to adaptively randomized clinical

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Posted in Clinical trials, Statistics

Stopping trials of ineffective drugs earlier

Valen Johnson and I recently posted a working paper on a method for stopping trials of ineffective drugs earlier. For Bayesians, we argue that our method is more consistently Bayesian than other methods in common use. For frequentists, we show

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Posted in Clinical trials

Random inequalities V: beta distributions

I’ve put a lot of effort into writing software for evaluating random inequality probabilities with beta distributions because such inequalities come up quite often in application. For example, beta inequalities are at the heart of the Thall-Simon method for monitoring

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Posted in Clinical trials, Math, Statistics

Statistically significant but incorrect

The Decision Science News blog has an article highlighting a tool to illustrate how often experiments with significant p-values draw false conclusions. Here’s the web site they refer to. See also Most published research results are false.

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Posted in Clinical trials, Statistics