Semi-parametric accelerated failure-time model : A useful alternative to the proportional-hazards model in cancer clinical trials
© 2021 John Wiley & Sons Ltd..
The accelerated failure-time (AFT) model has been long recognized as a useful alternative to the proportional-hazards (PH) model. Semi-parametric AFT model has been known since 1981. Its use has been hampered by the difficulty in solving the estimating equations for the model's coefficients. In recent years, however, important developments have taken place regarding the methods of solving the equations. In this article, we briefly review the developments, focusing mainly on rank-based estimation. We conduct a simulation study that directly focuses on the applicability of the model in the context of (cancer) clinical trials. We also investigate the robustness of the AFT model to the omission of covariates. Finally, we conduct a meta-analysis of multiple clinical trials in gastric cancer to illustrate the benefits of the use of the model in practice.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:21 |
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| Enthalten in: |
Pharmaceutical statistics - 21(2022), 2 vom: 20. März, Seite 292-308 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Burzykowski, Tomasz [VerfasserIn] |
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| Links: |
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| Themen: |
Buckley-James estimator |
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| Anmerkungen: |
Date Completed 14.04.2022 Date Revised 14.04.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/pst.2169 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM330952374 |
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| 520 | |a © 2021 John Wiley & Sons Ltd. | ||
| 520 | |a The accelerated failure-time (AFT) model has been long recognized as a useful alternative to the proportional-hazards (PH) model. Semi-parametric AFT model has been known since 1981. Its use has been hampered by the difficulty in solving the estimating equations for the model's coefficients. In recent years, however, important developments have taken place regarding the methods of solving the equations. In this article, we briefly review the developments, focusing mainly on rank-based estimation. We conduct a simulation study that directly focuses on the applicability of the model in the context of (cancer) clinical trials. We also investigate the robustness of the AFT model to the omission of covariates. Finally, we conduct a meta-analysis of multiple clinical trials in gastric cancer to illustrate the benefits of the use of the model in practice | ||
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