Using a surrogate marker for early testing of a treatment effect

• Published in: Biometrics Vol. 75; no. 4; pp. 1253 - 1263
• Main Authors: Parast, Layla, Cai, Tianxi, Tian, Lu
• Format: Journal Article
• Language: English
• Published: United States Wiley Periodicals, Inc 01.12.2019; Blackwell Publishing Ltd
• Subjects: Antiretroviral drugs; BIOMETRIC METHODOLOGY; biometry; diabetes; Diabetes mellitus; disease control programs; Identification methods; kernel smoothing; Markers; nonparametric method; Nonparametric statistics; resampling; surrogate; survival analysis; testing; kernel smoothing; surrogate; nonparametric method; survival analysis; resampling; testing
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.13067

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 timeto-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.