randomization

S. Senn: The Power of Negative Thinking (guest post)

Posted on February 12, 2021 by Mayo

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Stephen Senn
Consultant Statistician
Edinburgh, Scotland

Sepsis sceptic

During an exchange on Twitter, Lawrence Lynn drew my attention to a paper by Laffey and Kavanagh[1]. This makes an interesting, useful and very depressing assessment of the situation as regards clinical trials in critical care. The authors make various claims that RCTs in this field are not useful as currently conducted. I don’t agree with the authors’ logic here although, perhaps, surprisingly, I consider that their conclusion might be true. I propose to discuss this here. Continue reading →

Categories: power, randomization

Phil Stat Forum: November 19: Stephen Senn, “Randomisation and Control in the Age of Coronavirus?”

Posted on November 9, 2020 by Mayo

For information about the Phil Stat Wars forum and how to join, see this post and this pdf. 

Continue reading →

Categories: Error Statistics, randomization

Stephen Senn: Losing Control (guest post)

Posted on July 20, 2020 by Mayo

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Stephen Senn
Consultant Statistician
Edinburgh

Losing Control

Match points

The idea of local control is fundamental to the design and analysis of experiments and contributes greatly to a design’s efficiency. In clinical trials such control is often accompanied by randomisation and the way that the randomisation is carried out has a close relationship to how the analysis should proceed. For example, if a parallel group trial is carried out in different centres, but randomisation is ‘blocked’ by centre then, logically, centre should be in the model (Senn, S. J. & Lewis, R. J., 2019). On the other hand if all the patients in a given centre are allocated the same treatment at random, as in a so-called cluster randomised trial, then the fundamental unit of inference becomes the centre and patients are regarded as repeated measures on it. In other words, the way in which the allocation has been carried out effects the degree of matching that has been achieved and this, in turn, is related to the analysis that should be employed. A previous blog of mine, To Infinity and Beyond,  discusses the point. Continue reading →

Categories: covid-19, randomization, RCTs, S. Senn

Stephen Senn: Being Just about Adjustment (Guest Post)

Posted on March 16, 2020 by Mayo

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Stephen Senn
Consultant Statistician
Edinburgh

Correcting errors about corrected estimates

Randomised clinical trials are a powerful tool for investigating the effects of treatments. Given appropriate design, conduct and analysis they can deliver good estimates of effects. The key feature is concurrent control. Without concurrent control, randomisation is impossible. Randomisation is necessary, although not sufficient, for effective blinding. It also is an appropriate way to deal with unmeasured predictors, that is to say suspected but unobserved factors that might also affect outcome. It does this by ensuring that, in the absence of any treatment effect, the expected value of variation between and within groups is the same. Furthermore, probabilities regarding the relative variation can be delivered and this is what is necessary for valid inference. Continue reading →

Categories: randomization, S. Senn

S. Senn: “Error point: The importance of knowing how much you don’t know” (guest post)

Posted on January 20, 2020 by Mayo

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Stephen Senn
Consultant Statistician
Edinburgh

‘The term “point estimation” made Fisher nervous, because he associated it with estimation without regard to accuracy, which he regarded as ridiculous.’ Jimmy Savage [1, p. 453] 

First things second

The classic text by David Cox and David Hinkley, Theoretical Statistics (1974), has two extremely interesting features as regards estimation. The first is in the form of an indirect, implicit, message and the second explicit and both teach that point estimation is far from being an obvious goal of statistical inference. The indirect message is that the chapter on point estimation (chapter 8) comes after that on interval estimation (chapter 7). This may puzzle the reader, who may anticipate that the complications of interval estimation would be handled after the apparently simpler point estimation rather than before. However, with the start of chapter 8, the reasoning is made clear. Cox and Hinkley state: Continue reading →

Categories: Fisher, randomization, Stephen Senn | Tags:

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