TY - JOUR
T1 - Cure modeling in real-time prediction
T2 - How much does it help?
AU - Ying, Gui shuang
AU - Zhang, Qiang
AU - Lan, Yu
AU - Li, Yimei
AU - Heitjan, Daniel F.
N1 - Funding Information:
This project received support under grants U10CA21661, U10CA180822, and U10CA180868 from the National Cancer Institute (NCI), and grant 4100057652 from the Pennsylvania Department of Health, which specifically declaims responsibility for any analyses, interpretations or conclusions.
PY - 2017/8
Y1 - 2017/8
N2 - Various parametric and nonparametric modeling approaches exist for real-time prediction in time-to-event clinical trials. Recently, Chen (2016 BMC Biomedical Research Methodology 16) proposed a prediction method based on parametric cure-mixture modeling, intending to cover those situations where it appears that a non-negligible fraction of subjects is cured. In this article we apply a Weibull cure-mixture model to create predictions, demonstrating the approach in RTOG 0129, a randomized trial in head-and-neck cancer. We compare the ultimate realized data in RTOG 0129 to interim predictions from a Weibull cure-mixture model, a standard Weibull model without a cure component, and a nonparametric model based on the Bayesian bootstrap. The standard Weibull model predicted that events would occur earlier than the Weibull cure-mixture model, but the difference was unremarkable until late in the trial when evidence for a cure became clear. Nonparametric predictions often gave undefined predictions or infinite prediction intervals, particularly at early stages of the trial. Simulations suggest that cure modeling can yield better-calibrated prediction intervals when there is a cured component, or the appearance of a cured component, but at a substantial cost in the average width of the intervals.
AB - Various parametric and nonparametric modeling approaches exist for real-time prediction in time-to-event clinical trials. Recently, Chen (2016 BMC Biomedical Research Methodology 16) proposed a prediction method based on parametric cure-mixture modeling, intending to cover those situations where it appears that a non-negligible fraction of subjects is cured. In this article we apply a Weibull cure-mixture model to create predictions, demonstrating the approach in RTOG 0129, a randomized trial in head-and-neck cancer. We compare the ultimate realized data in RTOG 0129 to interim predictions from a Weibull cure-mixture model, a standard Weibull model without a cure component, and a nonparametric model based on the Bayesian bootstrap. The standard Weibull model predicted that events would occur earlier than the Weibull cure-mixture model, but the difference was unremarkable until late in the trial when evidence for a cure became clear. Nonparametric predictions often gave undefined predictions or infinite prediction intervals, particularly at early stages of the trial. Simulations suggest that cure modeling can yield better-calibrated prediction intervals when there is a cured component, or the appearance of a cured component, but at a substantial cost in the average width of the intervals.
KW - Bayesian bootstrap
KW - Enrollment model
KW - Event-based trial
KW - Interim analysis
KW - Weibull distribution
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U2 - 10.1016/j.cct.2017.05.012
DO - 10.1016/j.cct.2017.05.012
M3 - Article
C2 - 28545934
AN - SCOPUS:85019965958
VL - 59
SP - 30
EP - 37
JO - Contemporary Clinical Trials
JF - Contemporary Clinical Trials
SN - 1551-7144
ER -