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

PSC In The News

RSS Feed icon

Levy says ACA has helped increase rates of insured, but rates still lowest among poor

Bruch reveals key decision criteria in making first cuts on dating sites

Murphy on extending health support via a smart phone and JITAI

More News

Highlights

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

Frey's new report explores how the changing US electorate could shape the next 5 presidential elections, 2016 to 2032

U-M's Data Science Initiative offers expanded consulting services via CSCAR

Elizabeth Bruch promoted to Associate Professor

Next Brown Bag

Mon, Sept 19 at noon:
Paradox of Unintended Pregnancy, Jennifer Barber

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