Literature DB >> 22411544

Prediction of accrual closure date in multi-center clinical trials with discrete-time Poisson process models.

Gong Tang1, Yuan Kong, Chung-Chou Ho Chang, Lan Kong, Joseph P Costantino.   

Abstract

In a phase III multi-center cancer clinical trial or a large public health study, sample size is predetermined to achieve desired power, and study participants are enrolled from tens or hundreds of participating institutions. As the accrual is closing to the target size, the coordinating data center needs to project the accrual closure date on the basis of the observed accrual pattern and notify the participating sites several weeks in advance. In the past, projections were simply based on some crude assessment, and conservative measures were incorporated in order to achieve the target accrual size. This approach often resulted in excessive accrual size and subsequently unnecessary financial burden on the study sponsors. Here we proposed a discrete-time Poisson process-based method to estimate the accrual rate at time of projection and subsequently the trial closure date. To ensure that target size would be reached with high confidence, we also proposed a conservative method for the closure date projection. The proposed method was illustrated through the analysis of the accrual data of the National Surgical Adjuvant Breast and Bowel Project trial B-38. The results showed that application of the proposed method could help to save considerable amount of expenditure in patient management without compromising the accrual goal in multi-center clinical trials.
Copyright © 2012 John Wiley & Sons, Ltd.

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Year:  2012        PMID: 22411544      PMCID: PMC5012647          DOI: 10.1002/pst.1506

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  3 in total

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Authors:  S Senn
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3.  Modelling, prediction and adaptive adjustment of recruitment in multicentre trials.

Authors:  Vladimir V Anisimov; Valerii V Fedorov
Journal:  Stat Med       Date:  2007-11-30       Impact factor: 2.373

  3 in total
  3 in total

1.  Bayesian modeling and prediction of accrual in multi-regional clinical trials.

Authors:  Yi Deng; Xiaoxi Zhang; Qi Long
Journal:  Stat Methods Med Res       Date:  2014-11-03       Impact factor: 3.021

2.  Cure modeling in real-time prediction: How much does it help?

Authors:  Gui-Shuang Ying; Qiang Zhang; Yu Lan; Yimei Li; Daniel F Heitjan
Journal:  Contemp Clin Trials       Date:  2017-05-22       Impact factor: 2.226

3.  Interim recruitment prediction for multi-center clinical trials.

Authors:  Szymon Urbas; Chris Sherlock; Paul Metcalfe
Journal:  Biostatistics       Date:  2022-04-13       Impact factor: 5.899

  3 in total

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