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Weibull prediction of event times in clinical trials
Author(s) -
Ying Guishuang,
Heitjan Daniel F.
Publication year - 2007
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.271
Subject(s) - parametric statistics , weibull distribution , statistics , computer science , clinical trial , event (particle physics) , range (aeronautics) , survival analysis , monte carlo method , econometrics , mathematics , medicine , physics , pathology , quantum mechanics , materials science , composite material
In clinical trials with interim analyses planned at pre‐specified event counts, one may wish to predict the times of these landmark events as a tool for logistical planning. Currently available methods use either a parametric approach based on an exponential model for survival (Bagiella and Heitjan, Statistics in Medicine 2001; 20:2055) or a non‐parametric approach based on the Kaplan–Meier estimate (Ying et al., Clinical Trials 2004; 1:352). Ying et al. (2004) demonstrated the trade‐off between bias and variance in these models; the exponential method is highly efficient when its assumptions hold but potentially biased when they do not, whereas the non‐parametric method has minimal bias and is well calibrated under a range of survival models but typically gives wider prediction intervals and may fail to produce useful predictions early in the trial. As a potential compromise, we propose here to make predictions under a Weibull survival model. Computations are somewhat more difficult than with the simpler exponential model, but Monte Carlo studies show that predictions are robust under a broader range of assumptions. We demonstrate the method using data from a trial of immunotherapy for chronic granulomatous disease. Copyright © 2007 John Wiley & Sons, Ltd.