Literature DB >> 26188165

Real-time prediction of clinical trial enrollment and event counts: A review.

Daniel F Heitjan1, Zhiyun Ge2, Gui-Shuang Ying3.   

Abstract

Clinical trial planning involves the specification of a projected duration of enrollment and follow-up needed to achieve the targeted study power. If pre-trial estimates of enrollment and event rates are inaccurate, projections can be faulty, leading potentially to inadequate power or other mis-allocation of resources. Recent years have witnessed the development of methods that use the accumulating data from the trial itself to create improved predictions in real time. We review these methods, taking as a case study REMATCH, a trial that compared a left-ventricular assist device to optimal medical management in the treatment of end-stage heart failure. REMATCH provided the motivation and test bed for the first real-time clinical trial prediction model. Our review summarizes developments to date and points to unresolved issues and open research opportunities.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Enrollment; Event count; Prediction; Software

Mesh:

Year:  2015        PMID: 26188165     DOI: 10.1016/j.cct.2015.07.010

Source DB:  PubMed          Journal:  Contemp Clin Trials        ISSN: 1551-7144            Impact factor:   2.226


  8 in total

1.  Milestone prediction for time-to-event endpoint monitoring in clinical trials.

Authors:  Fang-Shu Ou; Martin Heller; Qian Shi
Journal:  Pharm Stat       Date:  2019-02-26       Impact factor: 1.894

2.  Bayesian accrual modeling and prediction in multicenter clinical trials with varying center activation times.

Authors:  Junhao Liu; Jo Wick; Yu Jiang; Matthew Mayo; Byron Gajewski
Journal:  Pharm Stat       Date:  2020-04-21       Impact factor: 1.894

3.  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

4.  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

5.  Concept and development of an interactive tool for trial recruitment planning and management.

Authors:  Ruan Spies; Nandi Siegfried; Bronwyn Myers; Sara S Grobbelaar
Journal:  Trials       Date:  2021-03-06       Impact factor: 2.279

6.  Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)-rationale and design for an international collaborative study.

Authors:  Benjamin Kasenda; Junhao Liu; Yu Jiang; Byron Gajewski; Cen Wu; Erik von Elm; Stefan Schandelmaier; Giusi Moffa; Sven Trelle; Andreas Michael Schmitt; Amanda K Herbrand; Viktoria Gloy; Benjamin Speich; Sally Hopewell; Lars G Hemkens; Constantin Sluka; Kris McGill; Maureen Meade; Deborah Cook; Francois Lamontagne; Jean-Marc Tréluyer; Anna-Bettina Haidich; John P A Ioannidis; Shaun Treweek; Matthias Briel
Journal:  Trials       Date:  2020-08-21       Impact factor: 2.279

7.  How to deal with the Poisson-gamma model to forecast patients' recruitment in clinical trials when there are pauses in recruitment dynamic?

Authors:  Nathan Minois; Stéphanie Savy; Valérie Lauwers-Cances; Sandrine Andrieu; Nicolas Savy
Journal:  Contemp Clin Trials Commun       Date:  2017-01-06

8.  Does asymmetry in patient recruitment in large critical care trials follow the Pareto principle?

Authors:  Mahesh Ramanan; Laurent Billot; Dorrilyn Rajbhandari; John Myburgh; Simon Finfer; Rinaldo Bellomo; Balasubramanian Venkatesh
Journal:  Trials       Date:  2020-05-05       Impact factor: 2.279

  8 in total

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