title: Surrogate markers for time-varying treatments and outcomes.
abstract: BACKGROUND
  A surrogate marker is a variable commonly used in clinical trials to guide treatment decisions when the outcome of ultimate interest is not available. A good surrogate marker is one where the treatment effect on the surrogate is a strong predictor of the effect of treatment on the outcome. We review the situation when there is one treatment delivered at baseline, one surrogate measured at one later time point, and one ultimate outcome of interest and discuss new issues arising when variables are time-varying.
  
  
  METHODS
  Most of the literature on surrogate markers has only considered simple settings with one treatment, one surrogate, and one outcome of interest at a fixed time point. However, more complicated time-varying settings are common in practice. In this article, we describe the unique challenges in two settings, time-varying treatments and time-varying surrogates, while relating the ideas back to the causal-effects and causal-association paradigms.
  
  
  CONCLUSIONS
  In addition to discussing and extending popular notions of surrogacy to time-varying settings, we give examples illustrating that one can be misled by not taking into account time-varying information about the surrogate or treatment. We hope this article has provided some motivation for future work on estimation and inference in such settings.
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA hsu9@mail.med.upenn.edu.
authors.forename: Jesse Y
authors.initials: JY
authors.lastname: Hsu
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Edward H
authors.initials: EH
authors.lastname: Kennedy
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Jason A
authors.initials: JA
authors.lastname: Roy
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Alisa J
authors.initials: AJ
authors.lastname: Stephens-Shields
authors.affiliation: Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Dylan S
authors.initials: DS
authors.lastname: Small
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Marshall M
authors.initials: MM
authors.lastname: Joffe
mesh_terms: D015415:Biomarkers; D002986:Clinical Trials as Topic; D006801:Humans; D055641:Mathematical Concepts; D017063:Outcome Assessment, Health Care; D012044:Regression Analysis; D013812:Therapeutics; D013997:Time Factors
chemical_list: D015415:Biomarkers
keywords: Causal inference; observational studies; randomization; surrogacy; time-varying