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

Heitjan, Daniel F.; Ge, Zhiyun; Ying, Gui Shuang

doi:10.1016/j.cct.2015.07.010

Cited by 39 publications

(50 citation statements)

References 52 publications

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“…A brief review of the papers related to these directions is provided by the authors [11] in Sec. 3 "Predicting Accrual".…”

Section: Interim Stagementioning

confidence: 99%

“…3.3 [11] that "Mijoule et al [15] proposed replacing the gamma with a Pareto mixture." Actually in [15] the authors investigated further properties of P-G model, compared them with the Pareto-Poisson model using real datasets from [2], and also investigated the feasibility of the model.…”

Section: Use Of a Poisson-gamma Enrollment Modelmentioning

confidence: 99%

“…

First, it would be useful to specify that there are two basic stages in predicting patient enrollment and various events: 1.

…”

mentioning

confidence: 99%

“…Interim prediction where it is possible to use real trial data and update (re-project) trial behaviour for the remaining period. At both stages, good predictive techniques potentially can use similar models, only input parameters will be evaluated differently.Since the trial start-up stage is not reflected in detail in [11], it seems expedient to devote some time to this. …”

mentioning

confidence: 99%

“…Since the trial start-up stage is not reflected in detail in [11], it seems expedient to devote some time to this.…”

mentioning

confidence: 99%

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Discussion on the paper “Real-Time Prediction of Clinical Trial Enrollment and Event Counts: A Review”, by DF Heitjan, Z Ge, and GS Ying

Anisimov

2016

Contemporary Clinical Trials

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The paper by Heitjan et al [11] provides very interesting and useful review of the methods for predicting patient enrollment and event counts in clinical trials. The aim of this letter is to raise an additional discussion on some points and to provide readers with more comprehensive information and clarification of particular methods/techniques.First, it would be useful to specify that there are two basic stages in predicting patient enrollment and various events: 1. Start-up (baseline) prediction before trials starts and therefore there is no real trial data available yet, and 2. Interim prediction where it is possible to use real trial data and update (re-project) trial behaviour for the remaining period. At both stages, good predictive techniques potentially can use similar models, only input parameters will be evaluated differently.Since the trial start-up stage is not reflected in detail in [11], it seems expedient to devote some time to this.

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“…A brief review of the papers related to these directions is provided by the authors [11] in Sec. 3 "Predicting Accrual".…”

Section: Interim Stagementioning

confidence: 99%

Section: Use Of a Poisson-gamma Enrollment Modelmentioning

confidence: 99%

“…

First, it would be useful to specify that there are two basic stages in predicting patient enrollment and various events: 1.

…”

mentioning

confidence: 99%

mentioning

confidence: 99%

“…Since the trial start-up stage is not reflected in detail in [11], it seems expedient to devote some time to this.…”

mentioning

confidence: 99%

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Discussion on the paper “Real-Time Prediction of Clinical Trial Enrollment and Event Counts: A Review”, by DF Heitjan, Z Ge, and GS Ying

Anisimov

2016

Contemporary Clinical Trials

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Predicting Event Counts in Event‐Driven Clinical Trials Accounting for Cure and Ongoing Recruitment

2022

Data Analysis and Related Applications 2

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Modeling time‐varying recruitment rates in multicenter clinical trials

2022

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Multicenter phase II/III clinical trials are large-scale operations that often include hundreds of recruiting centers in several countries. Therefore, the operational aspects of a trial must be thoroughly planned and closely monitored to ensure better oversight and study conduct. Predicting patient recruitment plays a pivotal role in trial monitoring as it informs how many people are expected to be recruited on a given day. As such, study teams may rely on predictions to assess progress and detect any deviations from the original plan that might put the study and potentially, patients at risk. Recruitment predictions are often based on a Poisson-Gamma model that assumes that centers have a constant recruitment rate throughout the trial. The model has useful features and has extensively been used, yet its main assumption is often unrealistic. A nonhomogeneous Poisson process has been recently proposed that can incorporate time-varying recruitment rates. In this work, we predict patient recruitment using both approaches and assess the impact of said assumption in a real-world setting where studies may not necessarily have constant center-specific recruitment rates. The paper showcases experience from modeling recruitment in trials sponsored by AstraZeneca between 2005 and 2018. In these data, there is evidence of timevarying recruitment rates. The predictive performance of models that account for both constant and time-varying recruitment effects is presented. Following a descriptive analysis of data, we assess model performance and investigate the impact of model misspecification.

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

Cited by 39 publications

References 52 publications

Discussion on the paper “Real-Time Prediction of Clinical Trial Enrollment and Event Counts: A Review”, by DF Heitjan, Z Ge, and GS Ying

Discussion on the paper “Real-Time Prediction of Clinical Trial Enrollment and Event Counts: A Review”, by DF Heitjan, Z Ge, and GS Ying

Predicting Event Counts in Event‐Driven Clinical Trials Accounting for Cure and Ongoing Recruitment

Modeling time‐varying recruitment rates in multicenter clinical trials

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