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

Jiang, Yu; Guarino, Peter; Ma, Shuangge; Simon, Steve; Mayo, Matthew S.; Rama, Raghavan; Gajewski, Byron J.

doi:10.1186/s13063-016-1457-3

Cited by 16 publications

(15 citation statements)

References 12 publications

(29 reference statements)

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“…All analyses were conducted using R version 3.0.2 statistical software (https://www.r-project.org/) with P<.05 deemed statistically significant. (27, 28) This project was approved by the University Health Network Research Ethics Board (IRB). All authors have completed the ICMJE uniform disclosure form and declare no support from any organization for the submitted work.…”

Section: Methodsmentioning

confidence: 99%

Disparities in Knee and Hip Arthroplasty Outcomes: an Observational Analysis of the ACS-NSQIP Clinical Registry

Cram

Hawker

Matelski

et al. 2017

J. Racial and Ethnic Health Disparities

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Section: Methodsmentioning

confidence: 99%

Disparities in Knee and Hip Arthroplasty Outcomes: an Observational Analysis of the ACS-NSQIP Clinical Registry

Cram

Hawker

Matelski

et al. 2017

J. Racial and Ethnic Health Disparities

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“…The results are illustrated in Figure , which is the plot of these predictions together with 95% confidence intervals. Figure is enriched with the plot of the 95% confidence interval [526–1145] (E‐G) given by the online program based on an Poisson‐exponential model . This is a particular case of the Poisson–gamma model when only one centre is involved.…”

Section: Recommendations and Illustrative Example: Feasibility Of Ifmmentioning

confidence: 99%

Using Poisson–gamma model to evaluate the duration of recruitment process when historical trials are available

Minois

Lauwers‐Cancès

Savy

et al. 2017

Statistics in Medicine

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At the design of clinical trial operation, a question of a paramount interest is how long it takes to recruit a given number of patients. Modelling the recruitment dynamics is the necessary step to answer this question. Poisson-gamma model provides very convenient, flexible and realistic approach. This model allows predicting the trial duration using data collected at an interim time with very good accuracy. A natural question arises: how to evaluate the parameters of recruitment model before the trial begins? The question is harder to handle as there are no recruitment data available for this trial. However, if there exist similar completed trials, it is appealing to use data from these trials to investigate feasibility of the recruitment process. In this paper, the authors explore the recruitment data of two similar clinical trials (Intergroupe Francais du Myélome 2005 and 2009). It is shown that the natural idea of plugging the historical rates estimated from the completed trial in the same centres of the new trial for predicting recruitment is not a relevant strategy. In contrast, using the parameters of a gamma distribution of the rates estimated from the completed trial in the recruitment dynamic model of the new trial provides reasonable predictive properties with relevant confidence intervals. Copyright © 2017 John Wiley & Sons, Ltd.

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“…One of the identified models was a Bayesian model developed by Jiang et al [12] with an open source package for the statistical program R [13]. This model requires only input information that is readily available to trialists and trial coordinators, such as planned sample size, anticipated time needed for total trial recruitment, number of patients recruited to date, and interim time point (e.g., 6 months after trial initiation).…”

Section: Introductionmentioning

confidence: 99%

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

Kasenda

Liu

Jiang

et al. 2020

Trials

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Background Poor recruitment of patients is the predominant reason for early termination of randomized clinical trials (RCTs). Systematic empirical investigations and validation studies of existing recruitment models, however, are lacking. We aim to provide evidence-based guidance on how to predict and monitor recruitment of patients into RCTs. Our specific objectives are the following: (1) to establish a large sample of RCTs (target n = 300) with individual patient recruitment data from a large variety of RCTs, (2) to investigate participant recruitment patterns and study site recruitment patterns and their association with the overall recruitment process, (3) to investigate the validity of a freely available recruitment model, and (4) to develop a user-friendly tool to assist trial investigators in the planning and monitoring of the recruitment process. Methods Eligible RCTs need to have completed the recruitment process, used a parallel group design, and investigated any healthcare intervention where participants had the free choice to participate. To establish the planned sample of RCTs, we will use our contacts to national and international RCT networks, clinical trial units, and individual trial investigators. From included RCTs, we will collect patient-level information (date of randomization), site-level information (date of trial site activation), and trial-level information (target sample size). We will examine recruitment patterns using recruitment trajectories and stratifications by RCT characteristics. We will investigate associations of early recruitment patterns with overall recruitment by correlation and multivariable regression. To examine the validity of a freely available Bayesian prediction model, we will compare model predictions to collected empirical data of included RCTs. Finally, we will user-test any promising tool using qualitative methods for further tool improvement. Discussion This research will contribute to a better understanding of participant recruitment to RCTs, which could enhance efficiency and reduce the waste of resources in clinical research with a comprehensive, concerted, international effort.

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

Cited by 16 publications

References 12 publications

Disparities in Knee and Hip Arthroplasty Outcomes: an Observational Analysis of the ACS-NSQIP Clinical Registry

Disparities in Knee and Hip Arthroplasty Outcomes: an Observational Analysis of the ACS-NSQIP Clinical Registry

Using Poisson–gamma model to evaluate the duration of recruitment process when historical trials are available

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

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