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

PSC In The News

RSS Feed icon

Bloomberg cites MTF data in story on CDC's anti-smoking ads for e-cigarettes

Bound says notion that foreign students are displacing U.S. students "isn't right"

Prescott says online option for access to court system can help equalize justice

Highlights

U-M ranked #1 in Sociology of Population by USN&WR's "Best Graduate Schools"

PAA 2015 Annual Meeting: Preliminary program and list of UM participants

ISR addition wins LEED Gold Certification

PSC Fall 2014 Newsletter now available

Next Brown Bag

Mon, March 23
Lundberg, State Care of the Elderly & Labor Supply of Adult Children

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