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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Accesso online: | https://arxiv.org/abs/2604.03863 |
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| _version_ | 1866918428255715328 |
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| author | Kang, Myeongjong Yi, Sangyoon |
| author_facet | Kang, Myeongjong Yi, Sangyoon |
| contents | The estimand framework provides guidance on handling intercurrent events, such as treatment discontinuation, in the analysis of clinical trial responses. Under ICH E9(R1), the treatment policy (TP) strategy incorporates post-discontinuation data to reflect treatment effects in real-world practice. However, many existing approaches focus primarily on imputing missing endpoint values for lost-to-follow-up subjects and do not explicitly model completers, retrieved dropouts (RDs), and lost-to-follow-up subjects within a unified framework. This may obscure the relationship between modeling assumptions and the estimand of interest when RD data are present. We propose a likelihood-based model for continuous endpoints that integrates data from all subject categories, including RDs. The approach combines an analysis of covariance formulation with a probit model for treatment discontinuation, enabling explicit formulation of treatment effects for estimands defined using the hypothetical and TP strategies. Estimation is carried out via a computationally efficient maximum likelihood procedure. Numerical studies demonstrate that the proposed method achieves improved bias and variability properties compared with commonly used imputation-based approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03863 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Estimation of treatment effect in clinical trials of continuous endpoints with retrieved dropouts Kang, Myeongjong Yi, Sangyoon Methodology The estimand framework provides guidance on handling intercurrent events, such as treatment discontinuation, in the analysis of clinical trial responses. Under ICH E9(R1), the treatment policy (TP) strategy incorporates post-discontinuation data to reflect treatment effects in real-world practice. However, many existing approaches focus primarily on imputing missing endpoint values for lost-to-follow-up subjects and do not explicitly model completers, retrieved dropouts (RDs), and lost-to-follow-up subjects within a unified framework. This may obscure the relationship between modeling assumptions and the estimand of interest when RD data are present. We propose a likelihood-based model for continuous endpoints that integrates data from all subject categories, including RDs. The approach combines an analysis of covariance formulation with a probit model for treatment discontinuation, enabling explicit formulation of treatment effects for estimands defined using the hypothetical and TP strategies. Estimation is carried out via a computationally efficient maximum likelihood procedure. Numerical studies demonstrate that the proposed method achieves improved bias and variability properties compared with commonly used imputation-based approaches. |
| title | Estimation of treatment effect in clinical trials of continuous endpoints with retrieved dropouts |
| topic | Methodology |
| url | https://arxiv.org/abs/2604.03863 |