From P Values to Bayesian Statistics, It's All in the Numbers

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 non-genetic variation, such as age and lifestyle.Then the disease: Behind the convenient categorization,

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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 non-genetic 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 ...

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