Teaching Statistical Principles and Methods in Medical Applications (original) (raw)
Biostatistics is undesirably perceived as a mathematical science. Time now is to reorient the subject so that it enjoys its rightful place into medical disciplines instead of being treated as an alien subject. The following is a brief outline of the plan of teaching this subject that can be very convincing to medical undergraduates as the one that fulfills their medical need. Establish the perimeter of biostatistics: Variation is spice of life. This enjoyable feature in human beings is also a source of enormous uncertainties in clinical decisions. Besides genetic, biological, environmental, observer, instrumental and sampling variability, factors such as incomplete information on the patient, inadequacy of medical knowledge, poor compliance with the regimen, and imperfect medical tools contribute to the spectrum of uncertainty. Some times these uncertainties are so profound that medicine digresses from science to an art. Clearly, tools are needed that help to keep a handle on these uncertainties. They must be properly measured and strategies should be adopted to minimise their impact on clinical decisions. (Define biostatistics as the science to manage medical uncertainties, and keep this theme alive all through the course.) Measurement of uncertainty: Uncertainty is measured through its complement, namely, the probability. Sensitivity, specificity, positive and negative predictive values are the examples of probability that are used every day in clinical practice. (Explain clinical relevance of these concepts and illustrate their calculation though a real life example.) Sensitivity and specificity are easy to calculate but move in a reverse direction from disease to symptoms. Predictive values rightly move from symptoms to disease but are difficult to obtain in a direct manner. Indirectly they can be obtained from sensitivity and specificity by using Bayes' rule. (Give a practical clinical example of how
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