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Design considerations in clinical trials with cure rate survival data: A case study in oncology
Author(s) -
Sun Steven,
Liu Grace,
Lyu Tianmeng,
Xue Fubo,
Yeh TzuMin,
Rao Sudhakar
Publication year - 2017
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.1840
Subject(s) - sample size determination , event (particle physics) , cutoff , clinical trial , cure rate , medicine , statistics , log rank test , hazard ratio , clinical endpoint , proportional hazards model , medical physics , oncology , mathematics , confidence interval , physics , quantum mechanics
For clinical trials with time‐to‐event as the primary endpoint, the clinical cutoff is often event‐driven and the log‐rank test is the most commonly used statistical method for evaluating treatment effect. However, this method relies on the proportional hazards assumption in that it has the maximal power in this circumstance. In certain disease areas or populations, some patients can be curable and never experience the events despite a long follow‐up. The event accumulation may dry out after a certain period of follow‐up and the treatment effect could be reflected as the combination of improvement of cure rate and the delay of events for those uncurable patients. Study power depends on both cure rate improvement and hazard reduction. In this paper, we illustrate these practical issues using simulation studies and explore sample size recommendations, alternative ways for clinical cutoffs, and efficient testing methods with the highest study power possible.