When Averages Hide Individual Differences in Clinical Trials
Analyzing the results of clinical trials to expose individual patients' risks might help doctors make better treatment decisions
The development and approval of medical therapies today relies on a mid-20th-century invention called the randomized clinical trial. Patients are recruited for an experimental treatment; a random subsample gets a placebo or different approach; any difference in the aggregated outcomes of the groups is attributed to the effects of the treatment. But the risks and benefits of a treatment typically vary substantially from one patient to the next. A small minority of high-risk patients can manifest dramatic results that obscure the fact that the same treatment is barely beneficial or actually harmful to most patients. The authors advocate using risk-stratified analysis to better understand how treatments affect the individual.
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