Flexible evaluation of surrogacy in platform studies
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Flexible evaluation of surrogacy in platform studies. / Sachs, Michael C.; Gabriel, Erin E.; Crippa, Alessio; Daniels, Michael J.
I: Biostatistics, Bind 25, Nr. 1, 2023, s. 220–236.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Flexible evaluation of surrogacy in platform studies
AU - Sachs, Michael C.
AU - Gabriel, Erin E.
AU - Crippa, Alessio
AU - Daniels, Michael J.
PY - 2023
Y1 - 2023
N2 - Trial-level surrogates are useful tools for improving the speed and cost effectiveness of trials but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial-level surrogate evaluation, but none, to our knowledge, for the specific setting of platform studies. As platform studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate evaluation that would not normally be possible. However, they also offer a set of statistical issues including heterogeneity of the study population, treatments, implementation, and even potentially the quality of the surrogate. We propose the use of a hierarchical Bayesian semiparametric model for the evaluation of potential surrogates using nonparametric priors for the distribution of true effects based on Dirichlet process mixtures. The motivation for this approach is to flexibly model relationships between the treatment effect on the surrogate and the treatment effect on the outcome and also to identify potential clusters with differential surrogate value in a data-driven manner so that treatment effects on the surrogate can be used to reliably predict treatment effects on the clinical outcome. In simulations, we find that our proposed method is superior to a simple, but fairly standard, hierarchical Bayesian method. We demonstrate how our method can be used in a simulated illustrative example (based on the ProBio trial), in which we are able to identify clusters where the surrogate is, and is not useful. We plan to apply our method to the ProBio trial, once it is completed.
AB - Trial-level surrogates are useful tools for improving the speed and cost effectiveness of trials but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial-level surrogate evaluation, but none, to our knowledge, for the specific setting of platform studies. As platform studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate evaluation that would not normally be possible. However, they also offer a set of statistical issues including heterogeneity of the study population, treatments, implementation, and even potentially the quality of the surrogate. We propose the use of a hierarchical Bayesian semiparametric model for the evaluation of potential surrogates using nonparametric priors for the distribution of true effects based on Dirichlet process mixtures. The motivation for this approach is to flexibly model relationships between the treatment effect on the surrogate and the treatment effect on the outcome and also to identify potential clusters with differential surrogate value in a data-driven manner so that treatment effects on the surrogate can be used to reliably predict treatment effects on the clinical outcome. In simulations, we find that our proposed method is superior to a simple, but fairly standard, hierarchical Bayesian method. We demonstrate how our method can be used in a simulated illustrative example (based on the ProBio trial), in which we are able to identify clusters where the surrogate is, and is not useful. We plan to apply our method to the ProBio trial, once it is completed.
KW - Bayesian nonparametrics
KW - Biomarkers
KW - Multilevel model
KW - Prostate cancer
KW - Surrogate markers
KW - Trial-level surrogate
KW - END-POINTS
KW - MODELS
U2 - 10.1093/biostatistics/kxac053
DO - 10.1093/biostatistics/kxac053
M3 - Journal article
C2 - 36610075
VL - 25
SP - 220
EP - 236
JO - Biostatistics
JF - Biostatistics
SN - 1465-4644
IS - 1
ER -
ID: 333610278