Using a surrogate marker for early testing of a treatment effect.
The development of methods to identify, validate, and use surrogate markers to test for a treatment effect has been an area of intense research interest given the potential for valid surrogate markers to reduce the required costs and follow-up times of future studies. Several quantities and procedures have been proposed to assess the utility of a surrogate marker. However, few methods have been proposed to address how one might use the surrogate marker information to test for a treatment effect at an earlier time point, especially in settings where the primary outcome and the surrogate marker are subject to censoring. In this paper, we propose a novel test statistic to test for a treatment effect using surrogate marker information measured prior to the end of the study in a time-to-event outcome setting. We propose a robust nonparametric estimation procedure and propose inference procedures. In addition, we evaluate the power for the design of a future study based on surrogate marker information. We illustrate the proposed procedure and relative power of the proposed test compared to a test performed at the end of the study using simulation studies and an application to data from the Diabetes Prevention Program.
['Parast|Layla|L|0000-0002-7288-1009', 'Cai|Tianxi|T|', 'Tian|Lu|L|0000-0002-5893-0169']
D015415:Biomarkers D003198:Computer Simulation D003920:Diabetes Mellitus D006801:Humans D015233:Models, Statistical D018709:Statistics, Nonparametric D013997:Time Factors D016896:Treatment Outcome
surrogate testing kernel smoothing nonparametric method resampling survival analysis