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Evaluating Public Health Interventions

3. The Two-Stage Design for Confounding Bias Reduction—Having Your Cake and Eating It Two

Bibliographic Data

ID11030240
AuthorsDonna Spiegelman (0000-0003-4006-4650, Donna Spiegelman and Claudia L. Rivera-Rodriguez are with the Departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA. Sebastien Haneuse is with the Department of Biostatistics, Harvard T. H. Chan School of Public Health.), Claudia L Rivera-Rodriguez (Donna Spiegelman and Claudia L. Rivera-Rodriguez are with the Departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA. Sebastien Haneuse is with the Department of Biostatistics, Harvard T. H. Chan School of Public Health.), Claudia Rivera‐Rodriguez (0000-0002-9614-7036, Cancer Research And Biostatistics), Sebastien Haneuse (0000-0003-4963-0655, Donna Spiegelman and Claudia L. Rivera-Rodriguez are with the Departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA. Sebastien Haneuse is with the Department of Biostatistics, Harvard T. H. Chan School of Public Health.)
Year2016
Volume106
Issue7
Pages1223-1226
Publication date2016-07-01
Open AccessNo
TypeARTICLE
VenueAmerican Journal of Public Health (JOURNAL)
Journal identifiersISSN: 0090-0036 • E-ISSN: 1541-0048
PublisherAmerican Public Health Association (PUBLISHER • US)
DOI10.2105/ajph.2016.303250
PMID27285260
OpenAlexW2418269271
LanguageEN
References cited24

In public health evaluations, confounding bias in the estimate of the intervention effect will typically threaten the validity of the findings. It is a common misperception that the only way to avoid this bias is to measure detailed, high-quality data on potential confounders for every intervention participant, but this strategy for adjusting for confounding bias is often infeasible. Rather than ignoring confounding altogether, the two-phase design and analysis—in which detailed high-quality confounding data are obtained among a small subsample—can be considered. We describe the two-stage design and analysis approach, and illustrate its use in the evaluation of an intervention conducted in Dar es Salaam, Tanzania, of an enhanced community health worker program to improve antenatal care uptake

Confounding · Environmental health · Geography · Information bias · Intervention (counseling) · Psychiatry · Psychological intervention · Public health · Selection bias · Tanzania · Food Security and Health in Diverse Populations · Global Maternal and Child Health · Healthcare Policy and Management · Medicine · Nursing

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Highly citedNo

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