This App can be used to predict a milestone event time (date of certain number of events occurs) for clinical trial monitoring.
Ou, F‐S, Heller, M, Shi, Q. Milestone prediction for time‐to‐event endpoint monitoring in clinical trials. Pharmaceutical Statistics. 2019; 18: 433– 446. https://doi.org/10.1002/pst.1934
Mayo Clinic does not save any of the data or output.
For clinical trials which use time-to-event endpoints, the analysis timing is typically based on the number of events observed. It is important for resource planning to predict the time at which a certain number of events will occur. This app predicts the time to a certain number of events occurs based on the current observed information.
The input dataset needs to conform to the file format. Please see “About” tab in the app for details.
Use the upload function to select the file to use.
See “About” tab for data upload requirements.
After the file is uploaded, the “Data View” tab will display the information uploaded.
After confirming that the dataset is correct, go to the “Calculate Milestone” tab and click the “Run Milestone Prediction”.
The progress of the prediction can be seen in the lower right corner of the monitor.
A report can be downloaded by clicking the “Generate report” button.
Suppose there is a study “AAA” which is a randomized phase 3 study designed to detect a target hazard ratio of 0.6. The historical data indicates that the control arm had a 3-year disease-free survival (DFS) rate of 75%. The study design calls for a sample size of 350 patients per arm (700 patients in total) to result in 165 events so the study can be powered at 90% level (type I error = 0.025, 1-sided). Suppose the first patient was enrolled in January 1, 2015 and we would like to predict when the 165th event will occur when we are 3.5 years (1270 days) into the trial.
The predication can be carried out fairly quickly (less than 1 min). Based on the recommended method, Bayesian Predictive Synthesis (MSPE), the final analysis is predicted to occur around June 2020 with a confidence interval of (November 2019, January 2021).
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