Academic Journal
Causal inference for time-to-event data with a cured subpopulation
| Τίτλος: | Causal inference for time-to-event data with a cured subpopulation |
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| Συγγραφείς: | Yi Wang, Yuping Deng, Xiao‐Hua Zhou |
| Πηγή: | Biometrics. 80 |
| Στοιχεία εκδότη: | Oxford University Press (OUP), 2024. |
| Έτος έκδοσης: | 2024 |
| Θεματικοί όροι: | Statistics and Probability, Causal Inference, Artificial intelligence, Biometry, Methods for Causal Inference in Observational Studies, Regularization and Variable Selection Methods, 01 natural sciences, Disease-Free Survival, FOS: Economics and business, Inference, Observational study, 0502 economics and business, FOS: Mathematics, Humans, Computer Simulation, Identifiability, Intensive care medicine, Econometrics, 0101 mathematics, Internal medicine, Censoring (clinical trials), Models, Statistical, Mediation Analysis, Treatment Effects, Statistics, 05 social sciences, Precursor Cell Lymphoblastic Leukemia-Lymphoma, Cure rate, Survival Analysis, Computer science, 3. Good health, Causality, Treatment Outcome, Physical Sciences, Medicine, Mathematics, Statistical Methods in Clinical Trials and Drug Development, Causal inference |
| Περιγραφή: | When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions. |
| Τύπος εγγράφου: | Article Other literature type |
| Γλώσσα: | English |
| ISSN: | 1541-0420 0006-341X |
| DOI: | 10.1093/biomtc/ujae028 |
| DOI: | 10.60692/j0g22-vek32 |
| DOI: | 10.60692/qf43s-rwj76 |
| Σύνδεσμος πρόσβασης: | https://pubmed.ncbi.nlm.nih.gov/38708764 |
| Rights: | CC BY |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....6ea7d1b19511a402f8a90291565bacd6 |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 15410420 0006341X |
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| DOI: | 10.1093/biomtc/ujae028 |