Quiz time: what does the following R code do?

Prize is brownie points.

y <- 7
z <- 1
y.last <- 0
while (abs(y.last-y)>1e-5) {
  y.last <- y
  y.temp <- 0.5*(y+1/z)
  z <- 0.5*(z+1/y)
  y <- y.temp
  print(y)
}

Belated world statistics day ruminations

Yesterday, my wedding anniversary took precedence over World Statistics Day. So here are my belated thoughts on the subject.

First, I'm very happy that statistical reasoning is getting more airtime in the news. It's about time. While not everyone needs to be a statistician, I think it is within everyone's capability of learning enough about statistics to understand the increasing number of statistical arguments (and lack thereof) in the world around us. For example, the chart in this image was made by my 4 year old son. Certainly, his father is a statistician, but there is no reason why first and second graders can't make similar charts, and start to draw conclusions from them. Later grades can build on this exercise so that a basic understanding of statistics is achievable by the end of high school. The alternative is that too many people (even in scientific disciplines) fall vulnerable to anecdotal or even superstitious arguments. (Case in point: Jenny McCarthy's anti-vaccine campaign.)

I am pleased that SAS is pushing their educational agenda for mathematics and technology at the secondary school level, and Revolution Analytics has made their premium Revolution R product free for all academics. I, as these companies, have been displeased with the state of statistical and technological education in the grade schools and even undergraduate schools. Let's all work together to make this important tool accessible to everybody, as statistical reasoning is set to become an essential part of civil participation.

Trials with bolted on adaptive components

All too often, I get a request to make a trial adaptive. In a lot of cases, adaptations were considered but rejected, but the sample size was too large given considerations such as dropout. Of course, this is a delicate time in sponsor-CRO relations, because emotions are already running high due to the frustration in spending the time considering a lot of alternatives that are already rejected. There is further danger in that the sponsor is, in fact, asking for a fundamental change to a trial that has already been designed.




Adaptive trials are best designed with the adaptation already in mind. This is because the adaptive component affects many aspects of the trial. In addition, the additional planning required for an additive trial can be more easily done if it is worked in from the beginning.

In the case where adaptation is used to rescue a trial, it's probably best to take the time to effectively start from the beginning, at least in making sure the time and events table makes sense. Barring that, I will often recommend one futility analysis be performed. The reason I do this are as follows:

* no adjustment to stated Type 1 error rate is required

* it's relatively easy to "bolt on" to an existing trial

* under the most common circumstances under which this late consideration is done (late Phase 1 or Phase 2 trial) this strategy will prevent wasting too much money on a worthless compound

Of course, not all trials benefit from a futility analysis, but I recommend this strategy almost categorically in cases where a sponsor wants to add one interim analysis to an otherwise designed trial.

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