Algorithm Finds Thousands of Unknown Drug Interaction Side Effects

ananyo writes "An algorithm designed by U.S. scientists to trawl through a plethora of drug interactions has yielded thousands of previously unknown side effects caused by taking drugs in combination (abstract). The work provides a way to sort through the hundreds of thousands of 'adverse events' reported to the U.S. Food and Drug Administration each year. The researchers developed an algorithm that would match data from each drug-exposed patient to a nonexposed control patient with the same condition. The approach automatically corrected for several known sources of bias, including those linked to gender, age and disease. The team then used this method to compile a database of 1,332 drugs and possible side effects that were not listed on the labels for those drugs. The algorithm came up with an average of 329 previously unknown adverse events for each drug — far surpassing the average of 69 side effects listed on most drug labels."

Comments

Re:I was wondered about something

[whydavid]

In Biomedicine you tend to see a heavy reliance on T-Tests, Chi-Square variants, Fisher's Exact, regression, McNemar's and Cox Proportional Hazards when temporally rich data is being tested. I don't have access to this article yet, but I would be surprised if they weren't performing a paired T-test in situations where outcome variables were measured on a ratio scale, McNemar's for binary outcomes where temporal data is not provided (maybe rare or nonexistent in this study), and Cox Proportional Hazards if there are any cases where we have a long temporal history of the data. Based on the sheer number of hypotheses tested we would expect to see some sort of correction for multiple testing here, too.