Medical Research is Better Because of Statistics

September 10, 2013

in A World Without Statistics

lab_mice RESIZED

In his 1865 book An Introduction to the Study of Experimental Medicine, Claude Bernard, the French scientist often called the father of modern medicine, argued against the use of “statistics” in medicine.

Interestingly, Bernard was really arguing against poor statistical practice at the time in observational clinical studies and for using the scientific method in laboratory investigations based on many ideas that are statistical in nature. He argued against the misuse of statistics that display only averages without understanding the sources of variability in the data.

Bernard argued that causative claims emerge more readily from experiments than from observation. He argued that experiments should have an underpinning of clear hypotheses that can be demonstrated or negated. He described how to eliminate sources of bias and was the first to suggest the use of blind experiments to foster objectivity.

Claude Bernard RESIZED
Claude Bernard

Bernard often turned to the use of animals, especially mice, in experiments as a model of human physiology. Although mouse models are not appropriate for all human conditions, they have been very fruitful in investigating the mechanisms underlying many disease processes—especially those in cancer. Examples of such experimental systems that today provide great insight into the biology and genetics of human cancers include:

  • Purebred mice that lack immune systems
  • Animals with a specific genetic aberration underlying a disease
  • Mice that can have their genome manipulated to remove a specific cancer fighting mechanism
  • Mice that are amenable to transplantation of a human tumor

Statistical ideas in handling variation are at the heart of all of these murine experiments. Statistics allows us to quantify the variability in measurements to decide on the scope of an experiment; to reduce the variability in designs through appropriate controls; to examine the variability in analyses though statistical modeling; and to precisely state the inferential conclusions that arise.

Without statistics pre-clinical medical science would be less efficient and more subject to ambiguous interpretation and without statistics lab mice would have nothing to do.