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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.06350 |
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| _version_ | 1866914862693613568 |
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| author | Gilbert, Peter B. Peng, James Han, Larry Lange, Theis Lu, Yun Nie, Lei Shih, Mei-Chiung Waddy, Salina P. Wiley, Ken Yann, Margot Zafari, Zafar Ghosh, Debashis Follmann, Dean Juraska, Michal Díaz, Iván |
| author_facet | Gilbert, Peter B. Peng, James Han, Larry Lange, Theis Lu, Yun Nie, Lei Shih, Mei-Chiung Waddy, Salina P. Wiley, Ken Yann, Margot Zafari, Zafar Ghosh, Debashis Follmann, Dean Juraska, Michal Díaz, Iván |
| contents | For many rare diseases with no approved preventive interventions, promising interventions exist, yet it has been difficult to conduct a pivotal phase 3 trial that could provide direct evidence demonstrating a beneficial effect on the target disease outcome. When a promising putative surrogate endpoint(s) for the target outcome is available, surrogate-based provisional approval of an intervention may be pursued. We apply the Causal Roadmap rubric to define a surrogate endpoint based provisional approval causal roadmap, which combines observational study data that estimates the relationship between the putative surrogate and the target outcome, with a phase 3 surrogate endpoint study that collects the same data but is very under-powered to assess the treatment effect (TE) on the target outcome. The objective is conservative estimation/inference for the TE with an estimated lower uncertainty bound that allows (through two bias functions) for an imperfect surrogate and imperfect transport of the conditional target outcome risk in the untreated between the observational and phase 3 studies. Two estimators of TE (plug-in, nonparametric efficient one-step) with corresponding inference procedures are developed. Finite-sample performance of the plug-in estimator is evaluated in two simulation studies, with R code provided. The roadmap is illustrated with contemporary Group B Streptococcus vaccine development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06350 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Surrogate Endpoint Based Provisional Approval Causal Roadmap Gilbert, Peter B. Peng, James Han, Larry Lange, Theis Lu, Yun Nie, Lei Shih, Mei-Chiung Waddy, Salina P. Wiley, Ken Yann, Margot Zafari, Zafar Ghosh, Debashis Follmann, Dean Juraska, Michal Díaz, Iván Methodology For many rare diseases with no approved preventive interventions, promising interventions exist, yet it has been difficult to conduct a pivotal phase 3 trial that could provide direct evidence demonstrating a beneficial effect on the target disease outcome. When a promising putative surrogate endpoint(s) for the target outcome is available, surrogate-based provisional approval of an intervention may be pursued. We apply the Causal Roadmap rubric to define a surrogate endpoint based provisional approval causal roadmap, which combines observational study data that estimates the relationship between the putative surrogate and the target outcome, with a phase 3 surrogate endpoint study that collects the same data but is very under-powered to assess the treatment effect (TE) on the target outcome. The objective is conservative estimation/inference for the TE with an estimated lower uncertainty bound that allows (through two bias functions) for an imperfect surrogate and imperfect transport of the conditional target outcome risk in the untreated between the observational and phase 3 studies. Two estimators of TE (plug-in, nonparametric efficient one-step) with corresponding inference procedures are developed. Finite-sample performance of the plug-in estimator is evaluated in two simulation studies, with R code provided. The roadmap is illustrated with contemporary Group B Streptococcus vaccine development. |
| title | A Surrogate Endpoint Based Provisional Approval Causal Roadmap |
| topic | Methodology |
| url | https://arxiv.org/abs/2407.06350 |