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

PSC In The News

RSS Feed icon

Groves keynote speaker at MIDAS symposium, Nov 15-16: "Big Data: Advancing Science, Changing the World"

Shaefer says drop child tax credit in favor of universal, direct investment in American children

Buchmueller breaks down partisan views on Obamacare

More News


Gonzalez, Alter, and Dinov win NSF "Big Data Spokes" award for neuroscience network

Post-doc Melanie Wasserman wins dissertation award from Upjohn Institute

ISR kicks off DE&I initiative with lunchtime presentation: Oct 13, noon, 1430 ISR Thompson

U-M ranked #4 in USN&WR's top public universities

More Highlights

Next Brown Bag

Mon, Oct 24 at noon:
Academic innovation & the global public research university, James Hilton

Investigating the Impact of Selection Bias in Dose-Response Analyses of Preventive Interventions

Publication Abstract

McGowan, H.M., R.L. Nix, Susan A. Murphy, and K.L. Bierman. 2010. "Investigating the Impact of Selection Bias in Dose-Response Analyses of Preventive Interventions." Prevention Science, 11(3): 239-251.

This paper focuses on the impact of selection bias in the context of extended, community-based prevention trials that attempt to "unpack" intervention effects and analyze mechanisms of change. Relying on dose-response analyses as the most general form of such efforts, this study provides two examples of how selection bias can affect the estimation of treatment effects. In Example 1, we describe an actual intervention in which selection bias was believed to influence the dose-response relation of an adaptive component in a preventive intervention for young children with severe behavior problems. In Example 2, we conduct a series of Monte Carlo simulations to illustrate just how severely selection bias can affect estimates in a dose-response analysis when the factors that affect dose are not recorded. We also assess the extent to which selection bias is ameliorated by the use of pretreatment covariates. We examine the implications of these examples and review trial design, data collection, and data analysis factors that can reduce selection bias in efforts to understand how preventive interventions have the effects they do.

DOI:10.1007/s11121-010-0169-2 (Full Text)

PMCID: PMC3044506. (Pub Med Central)

Country of focus: United States of America.

Browse | Search : All Pubs | Next