A non-parametric approach for estimating the correlation between log-rank test statistics with applications to a conjunctive power calculation
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915711076532224 |
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| author | Soerensen, Anne Lyngholm Blanche, Paul Ravn, Henrik Pipper, Christian |
| author_facet | Soerensen, Anne Lyngholm Blanche, Paul Ravn, Henrik Pipper, Christian |
| contents | We present a method for estimating the correlation between log-rank test statistics evaluating separate null hypotheses for two time-to-event endpoints. The correlation is estimated using subject-level data by a non-parametric approach based on the independent and identically distributed (iid) decomposition of the log-rank test statistic under any alternative. Using the iid decomposition, we are able to make an assumption-lean estimation of the correlation. A motivating example using the developed approach is provided. Here, we illustrate how the suggested approach can be used to give a realistic quantification of expected conjunctive power that can guide the design of a new randomized clinical trial using historical data. Finally, we investigate the method's finite sample properties via a simulation study that confirms unbiased and consistent behavior of the proposed approach. In addition, the simulation study gives insight into the effects of censoring on the correlation between the log-rank test statistics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03069 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A non-parametric approach for estimating the correlation between log-rank test statistics with applications to a conjunctive power calculation Soerensen, Anne Lyngholm Blanche, Paul Ravn, Henrik Pipper, Christian Methodology We present a method for estimating the correlation between log-rank test statistics evaluating separate null hypotheses for two time-to-event endpoints. The correlation is estimated using subject-level data by a non-parametric approach based on the independent and identically distributed (iid) decomposition of the log-rank test statistic under any alternative. Using the iid decomposition, we are able to make an assumption-lean estimation of the correlation. A motivating example using the developed approach is provided. Here, we illustrate how the suggested approach can be used to give a realistic quantification of expected conjunctive power that can guide the design of a new randomized clinical trial using historical data. Finally, we investigate the method's finite sample properties via a simulation study that confirms unbiased and consistent behavior of the proposed approach. In addition, the simulation study gives insight into the effects of censoring on the correlation between the log-rank test statistics. |
| title | A non-parametric approach for estimating the correlation between log-rank test statistics with applications to a conjunctive power calculation |
| topic | Methodology |
| url | https://arxiv.org/abs/2601.03069 |