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Milestone prediction for time‐to‐event endpoint monitoring in clinical trials
Author(s) -
Ou FangShu,
Heller Martin,
Shi Qian
Publication year - 2019
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1934
Subject(s) - milestone , interim , event (particle physics) , computer science , bayesian probability , predictive modelling , prediction interval , interim analysis , data mining , clinical trial , machine learning , artificial intelligence , medicine , physics , archaeology , pathology , quantum mechanics , history
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.