Abstract
In clinical trials, surrogate outcomes are early measures of treatment effect that are used to predict treatment effect on a later primary outcome of interest: the primary outcome therefore does not need to be observed and trials can be shortened. Evaluating surrogates is a complex area as a given treatment can act through multiple pathways, some of which may circumvent the surrogate. One of the best established and practically sound approaches to surrogacy evaluation is based on information theory. We have extended this approach to the case of ordinal outcomes, which are used as primary outcomes in many medical areas. This extension provides researchers with the means of evaluating surrogates in this setting, which expands the usefulness of the information theory approach while also demonstrating its versatility.
Original language | English |
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Pages (from-to) | 1-13 |
Number of pages | 13 |
Journal | Journal of biopharmaceutical statistics |
Early online date | 30 Dec 2019 |
DOIs | |
Publication status | E-pub ahead of print - 30 Dec 2019 |