Invited Commentary: Causal Diagrams and Measurement Bias
AUTOR(ES)
Hernán, Miguel A.
FONTE
Oxford University Press
RESUMO
Causal inferences about the effect of an exposure on an outcome may be biased by errors in the measurement of either the exposure or the outcome. Measurement errors of exposure and outcome can be classified into 4 types: independent nondifferential, dependent nondifferential, independent differential, and dependent differential. Here the authors describe how causal diagrams can be used to represent these 4 types of measurement bias and discuss some problems that arise when using measured exposure variables (e.g., body mass index) to make inferences about the causal effects of unmeasured constructs (e.g., “adiposity”). The authors conclude that causal diagrams need to be used to represent biases arising not only from confounding and selection but also from measurement.
ACESSO AO ARTIGO
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2765368Documentos Relacionados
- Invited commentary: persistent organic pollutants and childhood learning and behavioural disorders
- Invited Commentary: Assessing Treatment Effects by Using Observational Analyses—Opportunities and Limitations
- Commentary: Nephrologist
- Commentary: Cornfield, Epidemiology and Causality
- Commentary: Author's reply