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hello out there Brian Cohen here from the emj Club podcast here to talk to you today about one of those things that I know keeps you up at night fixed effect and random effects models whenever you come across a meta analysis one of the things you want to look at is how the authors combine the data to create their estimate of a treatment effect there are two primary methods used to combine data in a meta analysis fixed effect and random effects models its not important to understand the mathematics and statistics involved in each of these models but it is important to understand the difference between the two models so that you know when it is appropriate to use one versus the other Im going to talk about these two models and highlight their differences using it as an example a debilitating disease process while only described a few years ago this disease has had a devastating effect on our young population while contained in more recent years this disease still poses a docHub r