in medical research, but without the knowledge of what he is doing, why this is important, what might be important to researchers, how to better check the data, what to propose when asked for help by researchers. In other words – lack of this ability makes the biostatistician almost blind during the analysis.
In clinical research, writing a synopsis for a project, statistical input for the protocol or SAP (statistical analysis plan) is a perfect example of the situation, where this ability is extremely helpful, just priceless. Yes, sponsors should come with the ready set of hypotheses to be statistically verified, but this is only in theory. It’s more than likely biostatistician will be asked many times for advice on how to conduct a certain kind of analysis.
How is one going to respond well having even no rough idea about the subject of a trial and how everything works? Statistics in medical research is not very advanced (relatively), but it requires strong understanding of what is going on, how to interpret results.
This is the place where outliers (lying far from the majority of observations) might be perfectly OK from clinical perspective and observations seem perfectly OK might convey worrying news.
This is visible much better in the exploratory analysis (in evidence-based medicine), when biostatistician helps a researcher in writing a thesis or article or in conducting scientific research. They make a closely coupled team entering an unknown area. No ready hypotheses (just some anticipation and initial thoughts), mostly ad’hoc ideas, unexpected situations, strange results of calculations (indicating errors or a new discovery) and so on.
It doesn’t mean biostatistician should own PhD in medicine! Not at all. But he should quickly learn new things from the domain of medicine, diagnostics or pharmacy, acquire it and accumulate for further use.
Can one be a good biostatistician without this ability? My constant answer is: