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http://hdl.handle.net/1942/32768
Title: | On the relationship between association and surrogacy when both the surrogate and true endpoint are binary outcomes | Authors: | MEYVISCH, Paul ALONSO ABAD, Ariel VAN DER ELST, Wim MOLENBERGHS, Geert |
Issue Date: | 2020 | Publisher: | WILEY | Source: | STATISTICS IN MEDICINE, 39 (26) , p. 3867 -3878 | Abstract: | The relationship between association and surrogacy has been the focus of much debate in the surrogate marker literature. Recently, the individual causal association (ICA) has been introduced as a metric of surrogacy in the causal inference framework, when both the surrogate and the true endpoint are normally distributed and when both are binary. Earlier work on the normal case has demonstrated that, although the ICA and the adjusted association are related metrics, their relationship strongly depends on unidentifiable parameters and, consequently, the association between both endpoints conveys little information on the validity of the surrogate. In addition, in the normal setting, the magnitude of the ICA does not depend on the mean of the outcomes. The latter implies that identifiable parameters such as mean responses and treatment effects provide no information on the validity of the surrogate. In the present work it is shown that this is fundamentally different in the binary case. We demonstrate that the observed association between the outcomes as well as the success rates in both treatment groups are quite predictive for the ICA. It is shown that finding a good surrogate will be more likely when the association between the endpoints is large, there are sizeable treatment effects and the success rates for both endpoints are similar in both treatment groups. These results are demonstrated using extensive simulations and illustrated on a case study in multi-drug resistant tuberculosis. | Notes: | Meyvisch, P (corresponding author), Galapagos NV, Gen De Wittelaan L11 A3, B-2800 Mechelen, Belgium. paul.meyvisch@glpg.com |
Other: | Meyvisch, P (corresponding author), Galapagos NV, Gen De Wittelaan L11 A3, B-2800 Mechelen, Belgium. paul.meyvisch@glpg.com | Keywords: | causal inference;R package surrogate;surrogate endpoint | Document URI: | http://hdl.handle.net/1942/32768 | ISSN: | 0277-6715 | e-ISSN: | 1097-0258 | DOI: | 10.1002/sim.8698 | ISI #: | WOS:000564470400001 | Rights: | © 2020 John Wiley & Sons, Ltd. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2021 |
Appears in Collections: | Research publications |
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