2026-10-14T15:36:49Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34018116 2026-09-28T18:04:04Z category_7 category_21 category_46 category_734 category_811 category_64 category_135 portal_87 item_type_3 month_year_09_2026

Sieve estimation of the average treatment effect on log survival time Lina Xie (3597941) Yongxiu Cao (25132923) Jichang Yu (25132926) Medicine Biotechnology Immunology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Cancer Computational Biology Observational studies average treatment effect propensity score sieve method survival analysis 62N01 62N02 <p>Propensity scores have been extensively utilized to investigate causal effects in observational studies. When the outcome is subject to right censoring, we propose a method that combines the propensity score sieve approach with log-rank regression to estimate treatment effects, which subsumes the propensity score covariate adjustment method as a special case. Compared to inverse probability weighting and double robust methods, our approach is less sensitive to extreme propensity scores and does not require modeling the distribution of censoring times given the covariates. We establish the consistency and asymptotic normality of the proposed estimator and assess its finite-sample performance through simulation studies. Finally, we apply our method to a real dataset from the German Breast Cancer Study Group.</p> 2026-09-28T18:04:04Z Dataset Dataset 10.6084/m9.figshare.34018116.v1 https://figshare.com/articles/dataset/Sieve_estimation_of_the_average_treatment_effect_on_log_survival_time/34018116 CC BY 4.0