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

Elisavet Syriopoulou, Sarwar I Mozumder, Mark J Rutherford and Paul C Lambert
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Elisavet Syriopoulou: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
Sarwar I Mozumder: Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK
Mark J Rutherford: Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK
Paul C Lambert: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden and Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK

London Stata Conference 2021 from Stata Users Group

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 (total effects) or eliminated (direct effects), resulting in causal effects with different interpretation. Separable effects can also be 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 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. standsurv supports several models including flexible parametric models. With standsurv several contrasts can be calculated: differences, ratios and other user-defined functions. Confidence intervals are obtained using the delta method. Throughout we use an example analysing a publicly available dataset on prostate cancer.

Date: 2021-09-12
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