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When interested in a time-to-event outcome, competing events that prevent theoccurrence of the event of interest may be present. In the presence ofcompeting events, various statistical estimands have been suggested fordefining the causal effect of treatment on the event of interest. Depending onthe estimand, the competing events are either accommodated or eliminated,resulting in causal effects with different interpretation. The former approachcaptures the total effect of treatment on the event of interest while thelatter approach captures the direct effect of treatment on the event ofinterest that is not mediated by the competing event. Separable effects havealso been defined for settings where the treatment effect can be partitionedinto its effect on the event of interest and its effect on the competing eventthrough different causal pathways. We outline various causal effects that maybe of interest in the presence of competing events, including total, direct andseparable effects, and describe how to obtain estimates using regressionstandardisation with the Stata command standsurv. Regression standardisation isapplied by obtaining the average of individual estimates across all individualsin a study population after fitting a survival model. With standsurv severalcontrasts of interest can be calculated including differences, ratios and otheruser-defined functions. Confidence intervals can also be obtained using thedelta method. Throughout we use an example analysing a publicly availabledataset on prostate cancer to allow the reader to replicate the analysis andfurther explore the different effects of interest.

Estimating causal effects in the presence of competing events using regression standardisation with the Stata command standsurv