Abstract
In randomized clinical trials, true clinical endpoints that are rare or difficult to measure can be costly and time consuming. Therefore, there is an increasing need to substitute true clinical endpoints with surrogate endpoints. However, candidate surrogate endpoints require appropriate validation. To evaluate the extent of a treatment effect (TE) captured by candidate surrogate endpoints, various surrogacy measures have been proposed by biostatisticians and medical professionals. Although many researchers stated that it is desirable that surrogacy measures should take values between zero to one, those often fall outside a range [0, 1] without suitable assumptions. To overcome this problem, we propose two types of surrogacy measures based on the causal association (CA) paradigm and the causal effect (CE) paradigm. These operate by decomposing the TE into those parts that are and are not captured by candidate surrogate endpoints. The surrogacy measures based on the CA paradigm mainly consider how much of the TE of the treatment on the true clinical endpoint can be predicted through the TE on the candidate surrogate endpoints, while the surrogacy measures based on the CE paradigm are concerned with how much of the TE on the true clinical endpoint is a result of the candidate surrogate endpoints. In addition, we demonstrate some properties of the proposed surrogacy measures, and show that they always fall inside the range [0, 1]. Furthermore, they can be considered as improved and extended versions of existing surrogacy measures. Based on simulation experiments and applications of the proposed surrogacy measures to a case study of the Olmesartan Reducing Incidence of End-stage Renal Disease in Diabetic Nephropathy Trial, we show that the proposed surrogacy measures solve the problems encountered by the existing surrogacy measures. This paper presents new quantitative surrogacy measures that reliably evaluate the proportion of the TE captured by candidate surrogate endpoints.
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We would like to thank the ORIENT group for allowing the use of their data. This research was funded by the Ministry of Education, Culture, Sports, Science and Technology of Japan.
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Kobayashi, F., Kuroki, M. Causal Measures of the Treatment Effect Captured by Candidate Surrogate Endpoints. JABES 20, 409–430 (2015). https://doi.org/10.1007/s13253-015-0215-4
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DOI: https://doi.org/10.1007/s13253-015-0215-4