Meta-analysis for aggregated survival data with competing risks: a parametric approach using cumulative incidence functions

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Pooling summary competing risks data, using hazard ratios only, may result into missing a possible treatment effect on the predicted proportion of event types. One can try to catch this effect by also using a treatment effect built on cumulative incidence functions (CIFs). We also show that pooling methods that do not account for follow-up time, may also miss valuable information.

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