A good friend and colleague, Kaveh Shojania, recently shared an article about bitcoin (a form of digital currency), which predicts the end of the finance industry as we know it. The article argues that commercial banks, in particular, will no longer be needed. But what about our own industry of clinical epidemiology? Two thoughts occur:
- The current endeavour might not be sustainable.
- There might be another way to study prognosis, diagnosis and treatment.
We have argued in a previous post that traditional systematic reviews might soon become a victim to their own success. News blog readers will remember that we have argued that the size of the literature will soon become just too large to review in the normal way. In addition to which we have posited the twin issues of “question inflation and effect size deflation”. That is to say the number of potential comparisons is already becoming unwieldy (some network meta-analyses include over 100 individual comparators ), and plausible effect sizes are getting smaller as the headroom for further improvements gets used up. Our colleague Norman Waugh tells us that his latest Cochrane review concerning glucagon-like peptides in diabetes runs to over 800 pages. Many have written about the role of automation to search and screen the relevant literature,[3-5] including ourselves in a previous post, but the task of analysing the shedload of retrieved articles will itself become almost insurmountable. At the rate things are going, this may happen sooner than you think!
What is to be done? One possibility is that the whole of clinical epidemiology will be largely automated. We have written before about electronic patient records as a potential source of data for clinical research. This ‘rich’ data will be available for analysis by standard statistical methods. However, machine learning is being taken increasingly seriously, and so it is possible to imagine a world in which the bulk of clinical epidemiological studies are largely automated under programme control. That is to say, machine learning algorithms will sit behind rapidly accumulating clinical databases, searching for signals and conducting replication studies autonomously, perhaps even across national borders. In previous posts we have waxed lukewarm about IT systems, which have the potential to disrupt doctor-patient relationships, and where greater precision may be achieved at the cost of increasing inaccuracy. However, it is also possible that these problems can be mitigated by collecting and adjusting for ever larger amounts of information, and perhaps by finding instrumental variables, including those afforded by Mendelian randomisation.
Will all this mean that the CLAHRC WM director will soon retire, while his young colleagues find themselves being made redundant? Almost certainly not. For as long as can be envisaged, human agency will be required to write and monitor computer algorithms, to apply judgement to the outputs, to work out what it all means, and to design and implement subsidiary studies. If anything, epidemiologists of the future will require deeper epistemological understanding, statistical ability and technical knowhow.
— Richard Lilford, CLAHRC WM Director
— Yen-Fu Chen, Senior Research Fellow
- Lanchester J. When bitcoin grows up. London Rev Books. 2016; 38(8): 3-12.
- Zintzaras E, Doxani C, Mprotsis T, Schmid CH, Hadjigeorgiou GM. Network analysis of randomized controlled trials in multiple sclerosis. Clin Ther. 2012; 34(4): 857-69.
- O’Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev. 2015; 4: 5.
- Tsafnat G, Glasziou P, Choong MK, Dunn A, Galgani F, Coiera E. Systematic review automation technologies. Syst Rev. 2014; 3: 74.
- Choong MK, Galgani F, Dunn AG, Tsafnat G. Automatic evidence retrieval for systematic reviews. J Med Internet Res. 2014; 16(10): e223.
- Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS Med. 2010;7(9): e1000326.