Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Smock cited in amicus brief for Supreme Court case on citizenship rights for foreign-born children of unwed parents

Levy, Buchmueller and colleagues examine Medicaid expansion's impact on ER visits

ISR data show large partisan gap in consumer expectations for economy

More News

Highlights

MiCDA Research Fellowship - applications due July 21, 2017

U-M awarded $58 million to develop ideas for preventing and treating health problems

Bailey, Eisenberg , and Fomby promoted at PSC

Former PSC trainee Eric Chyn wins PAA's Dorothy S. Thomas Award for best paper

More Highlights

Causal assessment of surrogacy in a meta-analysis of colorectal cancer trials

Publication Abstract

Li, Yun, Jeremy Taylor, Michael R. Elliott, and Daniel J. Sargent. 2011. "Causal assessment of surrogacy in a meta-analysis of colorectal cancer trials." Biometrics, 12(3): 478-492.

When the true end points (T) are difficult or costly to measure, surrogate markers (S) are often collected in clinical trials to help predict the effect of the treatment (Z). There is great interest in understanding the relationship among S, T, and Z. A principal stratification (PS) framework has been proposed by Frangakis and Rubin (2002) to study their causal associations. In this paper, we extend the framework to a multiple trial setting and propose a Bayesian hierarchical PS model to assess surrogacy. We apply the method to data from a large collection of colon cancer trials in which S and T are binary. We obtain the trial-specific causal measures among S, T, and Z, as well as their overall population-level counterparts that are invariant across trials. The method allows for information sharing across trials and reduces the nonidentifiability problem. We examine the frequentist properties of our model estimates and the impact of the monotonicity assumption using simulations. We also illustrate the challenges in evaluating surrogacy in the counterfactual framework that result from nonidentifiability.

DOI:10.1093/biostatistics/kxq082 (Full Text)

PMCID: PMC3114655. (Pub Med Central)

Browse | Search : All Pubs | Next