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Weighted log rank type statistics for comparing survival curves when there is a time lag in the effectiveness of treatment
Abstract
In certain long-term treatment trials, one expects some lag period before the treatment is fully effective. This paper presents two weighted log rank type statistics designed to have good efficiency over a wide range of lags: a maximin efficiency robust statistic and a simplified version of this statistic. Both statistics can be computed as easily as the log rank statistic. Asymptotic efficiency calculations, supported by small sample simulations, show that the two proposed statistics are substantially more efficient than the conventional log rank statistic in certain lag situations with comparatively little efficiency loss relative to the log rank statistic when no lag exists. We recommend the maximin statistic for situations where a lag is expected but cannot be specified precisely in advance.
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“We used a stratified constant piecewise weighted log-rank test (in which a weight of 0 was assigned for the log-rank statistic for weeks 0 to 8 after randomization and a weight of 1 after that period) 22,23 to compare the trial groups, because both groups received the same treatments during the bridging period.”
From Cilta-cel or Standard Care in Lenalidomide-Refractory Multiple Myeloma. (2023) · cites this paper as [23]
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Items 101–107 of 107
Crossing points in survival analysis sensitively depend on system conditions.Preprint
McAndrew, T., Redfors, B., Zhang, Y., Crowley, A., Chen, S., Stone, G., & Jenkins, P. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.02835
“Weighting methods apply weights to events that occur before or after a crossing point, and typically shift attention away from survival differences before a crossing point and towards later survival differences [13, 14].”
MethodStochastic properties of the mixed accelerated hazard models
Li, P., Ling, X.-L., & Ding, W.-Y. (2017) · doi:10.1080/03610926.2015.1129416
“The AH model assumes that the hazard rate does change at time 0 and may be more suitable in randomized clinical trials when the treatment effect is gradually released and there is a lag period before the treatment is fully effective (cf. Zucker and Lakatos, 1990).”
BackgroundKroese, D. P., Taimre, T., & Botev, Z. (2011) · doi:10.1002/9781118014967.oth
Lakatos, E. (2018) · doi:10.1007/978-981-10-7829-3_14
Self-citation
Metadata
- Authors
- ZUCKER, D. M., & LAKATOS, E.
- Affiliations
- National Heart, Lung, and Blood Institute (US)
- ORCID
- ZUCKER, DAVID M.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1990
- Volume
- 77
- Issue
- 4
- Pages
- 853-864
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2079219970
- Cited by
- 114
- References
- 0
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 8 · 2022: 1 · 2021: 8 · 2020: 7 · 2019: 2 · 2018: 11 · 2017: 4 · 2016: 3 · 2015: 4 · 2014: 7 · 2013: 5 · 2012: 5
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrika (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- survival curves, asymptotic efficiency, small sample simulation
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