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Abstract:
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The use of surrogate markers to estimate and test for a treatment effect has been an area of popular research. Given the long follow-up periods that are often required for treatment or intervention studies, appropriate use of surrogate marker information has the potential to decrease required follow-up time. However, previous studies have shown that using inadequate markers or making inappropriate assumptions about the relationship between the primary outcome and the surrogate marker can lead to inaccurate conclusions regarding the treatment effect. Currently available methods for identifying, validating and using surrogate markers to test for a treatment effect tend to rely on restrictive model assumptions and/or focus on uncensored outcomes. We propose a novel approach to quantify the proportion of treatment effect explained by surrogate marker information and to test for a treatment effect using surrogate marker information collected up to a landmark time in a censored time-to-event outcome setting. Our proposed approach accommodates a setting where individuals may experience the primary outcome before the surrogate marker is measured. Simulation studies demonstrate that the proposed procedures perform well in finite samples and illustrate the expected power loss associated with earlier assessment of a treatment effect. We illustrate our proposed procedure using data from the Diabetes Prevention Program.
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