Probability

On first consideration, it seems a straightforward question: How effective and safe is drug A in treating condition B? But the design and analysis of the clinical trials that set out to answer this question are far from straightforward, involving an overwhelming number of variables.

First, the subjects: Any group of human beings will show boundless variation in terms of both genetic makeup and nongenetic variation, such as age and lifestyle.

Then the disease: Behind the convenient categorization, each case of the "same" disease is as unique as the patient in terms of stage, underlying cause, previous treatment, and host interaction.

The impact of the tested drug will be influenced by dose, the patient's metabolism, genetics, and compliance with the trial regimen. Even seemingly trivial variability in the way individuals in different centers implement the trial design will add to the uncertainty and the inevitable errors in reading, recording, and analyzing data.

Extracting meaning from this noisy data is an industry in itself, and one that has its fair share of controversies. Most prominent are the definitions of significance, including the appropriateness of relying on P values; the interpretation of trial results involving multiple drugs; and the so-called meta-analysis of results from the same drug used in different trials.

If we want to have an educated citizenship in a modern technological society, we need to teach them three things: reading, writing, and statistical thinking.
- H.G. Wells

Bayesian methods and classical methods