Predicting study duration in clinical trials with a time-to-event endpoint.
In event-driven clinical trials comparing the survival functions of two groups, the number of events required to achieve the desired power is usually calculated using the Freedman formula or the Schoenfeld formula. Then, the sample size and the study duration derived from the required number of even...
| Publicado en: | Statistics in Medicine Vol. 40; no. 10; pp. 2413 - 2422 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
5/10/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149706789&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149706789 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 5/10/2021 vid: 40 iid: 10 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149706789 149706789 NLM33580519 149706789 10.1002/sim.8911 NLM33580519 149706789 ppf: 2413 ppct: 9 formats: tig: atl: Predicting study duration in clinical trials with a time-to-event endpoint. aug: au: Machida, Ryunosuke Fujii, Yosuke Sozu, Takashi affil: Department of Information and Computer Technology, Tokyo University of Science Graduate School of Engineering, Tokyo, Japan sug: subj: Study Design Uncertainty Human Sample Size Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales ab: In event-driven clinical trials comparing the survival functions of two groups, the number of events required to achieve the desired power is usually calculated using the Freedman formula or the Schoenfeld formula. Then, the sample size and the study duration derived from the required number of events are considered; however, their combination is not uniquely determined. In practice, various combinations are examined considering the enrollment speed, study duration, and the cost of enrollment. However, effective methods for visually representing their relationships and evaluating the uncertainty in study duration are insufficient. We developed a graphical approach for examining the relationship between sample size and study duration. To evaluate the uncertainty in study duration under a given sample size, we also derived the probability density function of the study duration and a method for updating the probability density function according to the observed number of events (ie, information time). The proposed methods are expected to improve the operation and management of clinical trials with a time-to-event endpoint. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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