Causal inference in survival analysis using pseudo‐observations

Causal inference for non‐censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization (‘G‐formula’) or (2) inverse probability of treatment assignment weights (‘propensity score’). To do causal inference in survival analysis, one needs to address right‐censoring, and often, special techniques are required for that purpose. We will show how censoring can be dealt with ‘once and for all’ by means of so‐called pseudo‐observations when doing causal inference in survival analysis. The pseudo‐observations can be used as a replacement of the outcomes without censoring when applying ‘standard’ causal inference methods, such as (1) or (2) earlier. We study this idea for estimating the average causal effect of a binary treatment on the survival probability, the restricted mean lifetime, and the cumulative incidence in a competing risks situation. The methods will be illustrated in a small simulation study and via a study of patients with acute myeloid leukemia who received either myeloablative or non‐myeloablative conditioning before allogeneic hematopoetic cell transplantation. We will estimate the average causal effect of the conditioning regime on outcomes such as the 3‐year overall survival probability and the 3‐year risk of chronic graft‐versus‐host disease. Copyright © 2017 John Wiley & Sons, Ltd.