Links between analysis of surrogate endpoints and endogeneity.
There has been substantive interest in the assessment of surrogate endpoints in medical research. These are measures that could potentially replace 'true' endpoints in clinical trials and lead to studies that require less follow-up. Recent research in the area has focused on assessments using causal inference frameworks. Beginning with a simple model for associating the surrogate and true endpoints in the population, we approach the problem as one of endogenous covariates. An instrumental variables estimator and general two-stage algorithm are proposed. Existing surrogacy frameworks are then evaluated in the context of the model. In addition, we define an extended relative effect estimator as well as a sensitivity analysis for assessing what we term the treatment instrumentality assumption. A numerical example is used to illustrate the methodology.
Ghosh
Debashis
D
Department of Statistics, Penn State University, University Park, PA, USA. ghoshd@psu.edu
Elliott
Michael R
MR
Taylor
Jeremy M G
JM
D000465:Algorithms; D015415:Biomarkers; D056808:Biostatistics / Q000379:methods*; D015984:Causality; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D023381:Endpoint Determination / Q000706:statistics & numerical data*; D005901:Glaucoma / Q000628:therapy; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics
D015415:Biomarkers
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