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

TL;DR

This paper discusses methods to estimate various causal effects in time-to-event data with competing risks, using regression standardisation with the Stata command standsurv, illustrated through a prostate cancer dataset.

Contribution

It introduces a comprehensive framework for estimating total, direct, and separable causal effects in the presence of competing events using regression standardisation in Stata.

Findings

Estimates of different causal effects can be obtained using standsurv.

Regression standardisation allows calculation of contrasts like differences and ratios.

Confidence intervals for effects are derived using the delta method.

Abstract

When interested in a time-to-event outcome, competing events that prevent the occurrence of the event of interest may be present. In the presence of competing events, various statistical estimands have been suggested for defining the causal effect of treatment on the event of interest. Depending on the estimand, the competing events are either accommodated or eliminated, resulting in causal effects with different interpretation. The former approach captures the total effect of treatment on the event of interest while the latter approach captures the direct effect of treatment on the event of interest that is not mediated by the competing event. Separable effects have also been defined for settings where the treatment effect can be partitioned into its effect on the event of interest and its effect on the competing event through different causal pathways. We outline various causal effects that may be of interest in the presence of competing events, including total, direct and separable effects, and describe how to obtain estimates using regression standardisation with the Stata command standsurv. Regression standardisation is applied by obtaining the average of individual estimates across all individuals in a study population after fitting a survival model. With standsurv several contrasts of interest can be calculated including differences, ratios and other user-defined functions. Confidence intervals can also be obtained using the delta method. Throughout we use an example analysing a publicly available dataset on prostate cancer to allow the reader to replicate the analysis and further explore the different effects of interest.