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 - Publisher Copyright:
© 2017 Elsevier Inc.
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
UR - http://www.scopus.com/inward/record.url?scp=85019965958&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019965958&partnerID=8YFLogxK
U2 - 10.1016/j.cct.2017.05.012
DO - 10.1016/j.cct.2017.05.012
M3 - Article
C2 - 28545934
AN - SCOPUS:85019965958
SN - 1551-7144
VL - 59
SP - 30
EP - 37
JO - Contemporary Clinical Trials
JF - Contemporary Clinical Trials
ER -