Last week I went to the AZ MRC Science Symposium organised jointly by Astra Zeneca and the MRC Biostatistics Unit. Among a line-up of great speakers was Stephen Senn, who has an impressively encyclopaedic knowledge of statistics and its history, particularly relating to statistics in medicine. Unfortunately his talk was only half an hour and in the middle of the afternoon when I was flagging a bit, so I came away thinking 'that would have been really interesting if I had understood it.' In terms of what I remember, he made some very forceful remarks directed against personalised medicine, i.e., giving different treatments to different people based on their demography or genetics. This was particularly memorable because several other speakers seemed to have great hopes for the potential of personalised medicine to transform healthcare.

His opposition to personalised medicine was based on the following obstacles, which I presume he thinks are insurmountable.

His opposition to personalised medicine was based on the following obstacles, which I presume he thinks are insurmountable.

- Large sample sizes are needed to test for effects by sub-population. This makes it much more expensive to run a clinical trial than the more traditional case where you only test for effects at the population level.
- The analysis becomes more complicated when you include variables that cannot be randomized. Most demographic or genetic variables fall into this category. He talked about Nelder's theory of general balance which can apparently account for this in a principled way. Despite being developed in the 1970's it has been ignored by a lot of people due to its complexity.
- Personalised treatment is difficult to market. I guess this point is about making things as simple as possible for clinicians. It is easier to say use treatment X for disease Y, instead of use treatment X_i for disease variant Y_j in sub-population Z_k.

Proponents of personalised medicine would argue that all these problems can be solved through the effective use of computers. For example,

- Collecting data from GPs and hospitals may make it possible to analyse large samples of patients without needing to recruit any additional subjects for clinical trials.
- There is already a lot of software that automates part or all of complicated statistical analysis. There is scope for further automation, enabling the more widespread use of complex statistical methodology.
- It should be possible for clinicians to have information on personalised effects at their fingertips. It may even be possible to automate medical prescriptions.

It's difficult to know how big these challenges are. Some of the speakers at the AZ MRC symposium said things along the lines of 'ask me again in 2030 whether what I'm doing now is a good idea.' This doesn't exactly inspire confidence, but at least is an open and honest assessment.

As well as commenting on the future, Stephen Senn has also written a lot about the past. I particularly like his description of the origins of Statistics in chapter 2 of his book 'Statistical Issues in Drug Development',

*Statistics is the science of collecting, analysing and interpreting data. Statistical theory has its origin in three branches of human activity: first the study of mathematics as applied to games of chance; second, the collection of data as part of the art of governing a country, managing a business or, indeed, carrying out any other human enterprise; and third, the study of errors in measurement, particularly in astronomy. At first, the connection between these very different fields was not evident but gradually it came to be appreciated that data, like dice, are also governed to a certain extent by chance (consider, for example, mortality statistics), that decisions have to be made in the face of uncertainty in the realms of politics and business no less than at the gaming tables, and that errors in measurement have a random component. The infant statistics learned to speak from its three parents (no wonder it is such an interesting child) so that, for example, the word statistics itself is connected to the word state (as in country) whereas the words trial and odds come from gambling and error (I mean the word!), has been adopted from astronomy.*