ArticleJournal of biopharmaceutical statistics2022
Randomized two-stage optimal design for interval-censored data.
Article in Journal of biopharmaceutical statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Predicting Conversion Time from Mild Cognitive Impairment to Dementia with Interval-Censored Models.Journal of Alzheimer's disease : JAD · 2024Article
- Response adaptive randomization design for a two-stage study with binary response.Journal of biopharmaceutical statistics · 2023Article
- Promising zone two-stage design for a single-arm study with binary outcome.Statistical methods in medical research · 2023Article
- Monte Carlo cross-validation for a study with binary outcome and limited sample size.BMC medical informatics and decision making · 2022Article
- Disease progression from mild cognitive impairment to dementia for patients with Alzheimer's disease or Lewy body pathology.Journal of Alzheimer's disease reportsArticle
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Interval-censored data occur in a study where the exact event time of each participant is not observed but it is known to be within a certain time interval. Multiple tests were proposed for such data, including the logrank test by Sun, the proportional hazard test by Finkelstein, and the Wilcoxon-type test by Peto and Peto. We propose sample size calculations based on these tests for a parallel one-stage or two-stage design. When the proportional hazard assumption is met, the proportional hazard test and the logrank test need smaller sample sizes than the Wilcoxon-type test, and the sample size savings are substantial. But this trend is reversed when the proportional hazard assumption does not hold, and the sample size savings using the Wilcoxon-type test are sizable. An example from a lung cancer clinical trial is used to illustrate the application of the proposed sample size calculations.
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