Competing risks methods are often presented as a more realistic way to analyze time-to-event data in epidemiology and public health. They promise to handle situations where other events prevent the outcome of interest from ever occurring. But what if the method becomes more sophisticated while the interpretation becomes less clear? 

In this episode, we break down why competing risks analyses are often overtrusted, how the choice of estimand quietly changes what the result means, and why better methods do not remove the need for sharper scientific thinking.

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