title: Bias and causal associations in observational research.
abstract: Readers of medical literature need to consider two types of validity, internal and external. Internal validity means that the study measured what it set out to; external validity is the ability to generalise from the study to the reader's patients. With respect to internal validity, selection bias, information bias, and confounding are present to some degree in all observational research. Selection bias stems from an absence of comparability between groups being studied. Information bias results from incorrect determination of exposure, outcome, or both. The effect of information bias depends on its type. If information is gathered differently for one group than for another, bias results. By contrast, non-differential misclassification tends to obscure real differences. Confounding is a mixing or blurring of effects: a researcher attempts to relate an exposure to an outcome but actually measures the effect of a third factor (the confounding variable). Confounding can be controlled in several ways: restriction, matching, stratification, and more sophisticated multivariate techniques. If a reader cannot explain away study results on the basis of selection, information, or confounding bias, then chance might be another explanation. Chance should be examined last, however, since these biases can account for highly significant, though bogus results. Differentiation between spurious, indirect, and causal associations can be difficult. Criteria such as temporal sequence, strength and consistency of an association, and evidence of a dose-response effect lend support to a causal link.
authors.affiliation: Family Health International, PO Box 13950, Research Triangle Park, NC 27709, USA. dgrimes@fhi.org
authors.forename: David A
authors.initials: DA
authors.lastname: Grimes
authors.forename: Kenneth F
authors.initials: KF
authors.lastname: Schulz
mesh_terms: D015982:Bias; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D003625:Data Collection; D006801:Humans; D015999:Multivariate Analysis; D015203:Reproducibility of Results; D012107:Research Design; D015983:Selection Bias