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

PSC In The News

RSS Feed icon

Stephenson says homophobia among gay men raises risk of intimate partner violence

Frey says having more immigrants with higher birth rates fills need in the US

Inglehart's work on the rise of populism cited in NYT

More News

Highlights

Savolainen wins Outstanding Contribution Award for study of how employment affects recidivism among past criminal offenders

Giving Blueday at ISR focuses on investing in the next generation of social scientists

Pfeffer and Schoeni cover the economic and social dimensions of wealth inequality in this special issue

PRB Policy Communication Training Program for PhD students in demography, reproductive health, population health

More Highlights

Next Brown Bag

Mon, Jan 23, 2017 at noon:
H. Luke Shaefer

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