Literature DB >> 23869201

On the Existence of Constant Accrual Rates in Clinical Trials and Direction for Future Research.

Byron J Gajewski1, Stephen D Simon, Susan E Carlson.   

Abstract

Many clinical trials fall short of their accrual goals. This can be avoided with accurate accrual prediction tools. Past researchers provide important methodological alternative models for predicting accrual in clinical trials. One model allows for slow accrual at the start of the study, which eventually reaches a threshold. A simpler model assumes a constant rate of accrual. A comparison has been attempted but we wish to point out some important considerations when comparing these two models. In fact, we can examine the reasonableness of a constant accrual assumption (simpler model) which had data 239 days into a three-year study. We can now update that and report accumulated from the full three years of accrual data and we can demonstrate that constant accrual rate assumption was met in this particular study. We will use this report to frame future research in the area of accrual prediction.

Entities:  

Keywords:  Bayesian; exponential; inverse gamma; prior elicitation; sample size

Year:  2012        PMID: 23869201      PMCID: PMC3712523          DOI: 10.5539/ijsp.v1n2p43

Source DB:  PubMed          Journal:  Int J Stat Probab


  3 in total

1.  Predicting accrual in clinical trials with Bayesian posterior predictive distributions.

Authors:  Byron J Gajewski; Stephen D Simon; Susan E Carlson
Journal:  Stat Med       Date:  2008-06-15       Impact factor: 2.373

2.  Stochastic modeling and prediction for accrual in clinical trials.

Authors:  Xiaoxi Zhang; Qi Long
Journal:  Stat Med       Date:  2010-03-15       Impact factor: 2.373

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
  6 in total

1.  Building efficient comparative effectiveness trials through adaptive designs, utility functions, and accrual rate optimization: finding the sweet spot.

Authors:  Byron J Gajewski; Scott M Berry; Melanie Quintana; Mamatha Pasnoor; Mazen Dimachkie; Laura Herbelin; Richard Barohn
Journal:  Stat Med       Date:  2015-01-07       Impact factor: 2.373

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.  Accrual Prediction Program: A web-based clinical trials tool for monitoring and predicting accrual for early-phase cancer studies.

Authors:  Junhao Liu; Jo A Wick; Dinesh Pal Mudaranthakam; Yu Jiang; Matthew S Mayo; Byron J Gajewski
Journal:  Clin Trials       Date:  2019-08-26       Impact factor: 2.486

4.  Modeling and validating Bayesian accrual models on clinical data and simulations using adaptive priors.

Authors:  Yu Jiang; Steve Simon; Matthew S Mayo; Byron J Gajewski
Journal:  Stat Med       Date:  2014-11-06       Impact factor: 2.373

5.  Optimising recruitment into trials using an internal pilot.

Authors:  W Bertram; A Moore; V Wylde; R Gooberman-Hill
Journal:  Trials       Date:  2019-04-11       Impact factor: 2.279

6.  Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application.

Authors:  Yu Jiang; Peter Guarino; Shuangge Ma; Steve Simon; Matthew S Mayo; Rama Raghavan; Byron J Gajewski
Journal:  Trials       Date:  2016-07-22       Impact factor: 2.279

  6 in total

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