Causal inference in survival analysis using pseudo-observations
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Causal inference in survival analysis using pseudo-observations. / Andersen, Per K.; Syriopoulou, Elisavet; Parner, Erik T.
In: Statistics in Medicine, Vol. 36, No. 17, 30.07.2017, p. 2669-2681.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Causal inference in survival analysis using pseudo-observations
AU - Andersen, Per K.
AU - Syriopoulou, Elisavet
AU - Parner, Erik T.
N1 - Copyright © 2017 John Wiley & Sons, Ltd.
PY - 2017/7/30
Y1 - 2017/7/30
N2 - 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.
AB - 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.
U2 - 10.1002/sim.7297
DO - 10.1002/sim.7297
M3 - Journal article
C2 - 28384840
VL - 36
SP - 2669
EP - 2681
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 17
ER -
ID: 195511024