Prior Effective Sample Size When Borrowing on the Treatment Effect Scale
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910415679651840 |
|---|---|
| author | Zhang, Hongtao Anderson, Keaven M Zimmer, Zachary Golm, Gregory Sapre, Aditi Ibrahim, Joseph G |
| author_facet | Zhang, Hongtao Anderson, Keaven M Zimmer, Zachary Golm, Gregory Sapre, Aditi Ibrahim, Joseph G |
| contents | With the robust uptick in the applications of Bayesian external data borrowing, eliciting a prior distribution with the proper amount of information becomes increasingly critical. The prior effective sample size (ESS) is an intuitive and efficient measure for this purpose. The majority of ESS definitions have been proposed in the context of borrowing control information. While many Bayesian models can be naturally extended to leveraging external information on the treatment effect scale, very little attention has been directed to computing the prior ESS in this setting. In this research, we bridge this methodological gap by extending the popular ELIR ESS definition. We lay out the general framework, and derive the prior ESS for various types of endpoints and treatment effect measures. The posterior distribution and the predictive consistency property of ESS are also examined. The methods are implemented in R programs available on GitHub: https://github.com/squallteo/TrtEffESS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13366 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prior Effective Sample Size When Borrowing on the Treatment Effect Scale Zhang, Hongtao Anderson, Keaven M Zimmer, Zachary Golm, Gregory Sapre, Aditi Ibrahim, Joseph G Methodology With the robust uptick in the applications of Bayesian external data borrowing, eliciting a prior distribution with the proper amount of information becomes increasingly critical. The prior effective sample size (ESS) is an intuitive and efficient measure for this purpose. The majority of ESS definitions have been proposed in the context of borrowing control information. While many Bayesian models can be naturally extended to leveraging external information on the treatment effect scale, very little attention has been directed to computing the prior ESS in this setting. In this research, we bridge this methodological gap by extending the popular ELIR ESS definition. We lay out the general framework, and derive the prior ESS for various types of endpoints and treatment effect measures. The posterior distribution and the predictive consistency property of ESS are also examined. The methods are implemented in R programs available on GitHub: https://github.com/squallteo/TrtEffESS. |
| title | Prior Effective Sample Size When Borrowing on the Treatment Effect Scale |
| topic | Methodology |
| url | https://arxiv.org/abs/2404.13366 |