When observational studies are used to establish the causal effects of treatments, the estimated effect is affected by treatment selection bias. The inverse propensity score weight (IPSW) is often used to deal with such bias. However, IPSW requires strong assumptions whose misspecifications and strategies to correct the misspecifications were rarely studied. We present a bootstrap bias correction of IPSW (BC-IPSW) to improve the performance of propensity score in dealing with treatment selection bias in the presence of failure to the ignorability and overlap assumptions. The approach was motivated by a real observational study to explore the potential of anticoagulant treatment for reducing mortality in patients with end-stage renal disease. The benefit of the treatment to enhance survival was demonstrated; the suggested BC-IPSW method indicated a statistically significant reduction in mortality for patients receiving the treatment. Using extensive simulations, we show that BC-IPSW substantially reduced the bias due to the misspecification of the ignorability and overlap assumptions. Further, we showed that IPSW is still useful to account for the lack of treatment randomization, but its advantages are stringently linked to the satisfaction of ignorability, indicating that the existence of relevant though unmeasured or unused covariates can worsen the selection bias.

Arisido, M., Mecatti, F., Rebora, P. (2022). Improving the causal treatment effect estimation with propensity scores by the bootstrap. ASTA ADVANCES IN STATISTICAL ANALYSIS, 106(3), 455-471 [10.1007/s10182-021-00427-3].

Improving the causal treatment effect estimation with propensity scores by the bootstrap

Arisido, Maeregu W.
;
Mecatti, Fulvia;Rebora, Paola
2022

Abstract

When observational studies are used to establish the causal effects of treatments, the estimated effect is affected by treatment selection bias. The inverse propensity score weight (IPSW) is often used to deal with such bias. However, IPSW requires strong assumptions whose misspecifications and strategies to correct the misspecifications were rarely studied. We present a bootstrap bias correction of IPSW (BC-IPSW) to improve the performance of propensity score in dealing with treatment selection bias in the presence of failure to the ignorability and overlap assumptions. The approach was motivated by a real observational study to explore the potential of anticoagulant treatment for reducing mortality in patients with end-stage renal disease. The benefit of the treatment to enhance survival was demonstrated; the suggested BC-IPSW method indicated a statistically significant reduction in mortality for patients receiving the treatment. Using extensive simulations, we show that BC-IPSW substantially reduced the bias due to the misspecification of the ignorability and overlap assumptions. Further, we showed that IPSW is still useful to account for the lack of treatment randomization, but its advantages are stringently linked to the satisfaction of ignorability, indicating that the existence of relevant though unmeasured or unused covariates can worsen the selection bias.
Articolo in rivista - Articolo scientifico
Average treatment effect; Bootstrap bias; Causal inference; Observational study; Propensity score; Simulation; Time-to-event endpoint;
English
21-dic-2021
2022
106
3
455
471
open
Arisido, M., Mecatti, F., Rebora, P. (2022). Improving the causal treatment effect estimation with propensity scores by the bootstrap. ASTA ADVANCES IN STATISTICAL ANALYSIS, 106(3), 455-471 [10.1007/s10182-021-00427-3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/341760
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