Investigating the Impact of Selection Bias in Dose-Response Analyses of Preventive Interventions
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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.
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Unmeasured characteristics may have influenced participants' willingness to comply with the treatment protocol, potentially biasing the results.
“In addition, it is possible that unmeasured characteristics affected individuals' willingness to comply with the treatment/experimental protocol, thereby biasing our results [31].”
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- Authors
- Conduct Problems Prevention Research Group*, McGowan, H. M., Nix, R. L., Murphy, S. A., & Bierman, K. L.
- Affiliations
- North Carolina State University (US)
- Pennsylvania State University (US)
- University of Michigan (US)
- Corresponding author
- McGowan, Herle M.
- Venue
- Prevention Science
- Published
- 2010-02-24
- Volume
- 11
- Issue
- 3
- Pages
- 239-251
- ISSN
- 1389-4986 (ISSN-L)
- 1573-6695
- PMC
- PMC3044506
- OpenAlex
- W2077849591
- Cited by
- 19
- References
- 58
- Citations by year
- 2022: 2 · 2021: 1 · 2019: 1 · 2018: 2 · 2016: 1 · 2015: 3 · 2014: 1 · 2013: 6 · 2012: 1
- FWCI
- 2.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Prevention Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH080015, R01MH050953, R18MH050953)
- National Institute on Drug Abuse (K02DA015674, R01DA016903, P50DA010075)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- selection bias, dose-response analysis, preventive interventions, mechanisms of change, treatment effect estimation, trial design
- MeSH terms
- Monte Carlo Method, Preventive Health Services, Selection Bias
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