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, Vladimir V.

doi:10.1016/j.cct.2015.11.008

Cited by 23 publications

(16 citation statements)

References 17 publications

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“…() One could further refine this model by incorporating nonhomogeneous arrival rates catering to each specific clinical center . There are many more improvements for the interarrival rate model, and a thorough review is available in Heitjan et al and Anisimov . While the selected models have room for improvement, our goal is to demonstrate the utility of prediction synthesis in improving prediction results.…”

Section: Resultsmentioning

confidence: 99%

Milestone prediction for time‐to‐event endpoint monitoring in clinical trials

Heller

2019

Pharmaceutical Statistics

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Predicting the times of milestone events, ie, interim and final analyses in clinical trials, helps resource planning. This manuscript presents and compares several easily implemented methods for predicting when a milestone event is achieved. We show that it is beneficial to combine the predictions from different models to craft a better predictor through prediction synthesis. Furthermore, a Bayesian approach provides a better measure of the uncertainty involved in prediction of milestone events. We compare the methods through two simulations where the model has been correctly specified and where the models are a mixture of three incorrectly specified model classes. We then apply the methods on two real clinical trial data, North Central Cancer Treatment Group (NCCTG) N0147 and N9841. In summary, the Bayesian prediction synthesis methods automatically perform well even when the data collection is far from homogeneous. An R shiny app is under development to carry out the prediction in a user-friendly fashion.

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Section: Resultsmentioning

confidence: 99%

Milestone prediction for time‐to‐event endpoint monitoring in clinical trials

Heller

2019

Pharmaceutical Statistics

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“…Statistical models aimed at predicting recruitment rates and guiding adaptive adjustments in patient recruitment in pharmaceutical trials have been described [7,8]. Furthermore, these models have been refined to predict recruitment rates at multiple levels including trial-level, region-level and site-level recruitment [8,9].…”

Section: Introductionmentioning

confidence: 99%

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

Ramanan

Billot

Rajbhandari

et al. 2020

Trials

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Background: Randomised controlled trials (RCT) may be hindered by slow recruitment rates, particularly in critically ill patients. While statistical models to predict recruitment rates have been described, no systematic assessment has been conducted of the distribution of recruitment across sites, temporal trends in site participation and impact of competing trials on patient recruitment. Methods: We used recruitment and screening logs from the SAFE, NICE-SUGAR, RENAL, CHEST and ADRENAL trials, five of the largest critical care RCTs. We quantified the extent of recruitment asymmetry between sites using Lorenz curves and Gini coefficients and assessed whether the recruitment distribution across sites follow the Pareto principle, which states that 80% of effects come from 20% of causes. Peak recruitment rates and growth in participating sites were calculated. Results: In total, 25,412 patients were randomised in 99 intensive care units (ICUs) for the five trials. Distribution of recruitment was asymmetric, with a small number of ICUs recruiting a large proportion of the patients. The Gini coefficients ranged from 0.14 to 0.52. The time to peak recruitment rate ranged from 7 to 41 months and was variable (7, 31, 41, 10 and 40 months). Over time, the proportion of recruitment at non-tertiary ICUs increased from 15% to 34%. Conclusions: There is asymmetry of recruitment with a small proportion of ICUs recruiting a large proportion of patients. The distributions of recruitment were not consistent with the Pareto principle. There has been increasing participation of non-tertiary ICUs in clinical trials.

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“…Researchers apply statistical models of patient enrollment to predict accrual. For multicenter trials, these models include variables such as center activation time, the time needed to complete the trial, and the risk function, the latter of which takes into account possible treatment costs, enrollment costs, center activation, advertisement expenses, and potential revenue loss due to trial delays 1‐4 …”

Section: Introductionmentioning

confidence: 99%

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

Liu

Wick

Jiang

et al. 2020

Pharmaceutical Statistics

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Summary Investigators who manage multicenter clinical trials need to pay careful attention to patterns of subject accrual, and the prediction of activation time for pending centers is potentially crucial for subject accrual prediction. We propose a Bayesian hierarchical model to predict subject accrual for multicenter clinical trials in which center activation times vary. We define center activation time as the time at which a center can begin enrolling patients in the trial. The difference in activation times between centers is assumed to follow an exponential distribution, and the model of subject accrual integrates prior information for the study with actual enrollment progress. We apply our proposed Bayesian multicenter accrual model to two multicenter clinical studies. The first is the PAIN‐CONTRoLS study, a multicenter clinical trial with a goal of activating 40 centers and enrolling 400 patients within 104 weeks. The second is the HOBIT trial, a multicenter clinical trial with a goal of activating 14 centers and enrolling 200 subjects within 36 months. In summary, the Bayesian multicenter accrual model provides a prediction of subject accrual while accounting for both center‐ and individual patient‐level variation.

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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

Cited by 23 publications

References 17 publications

Milestone prediction for time‐to‐event endpoint monitoring in clinical trials

Milestone prediction for time‐to‐event endpoint monitoring in clinical trials

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

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

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