A simple checklist, such as that shown in panel 1, can be helpful. All observational studies (and, regrettably, many badly done randomised controlled trials) 9,10 have built-in bias the challenge for investigators, editors, and readers is to ferret these out and judge how they might have affected results. Unlike the conventional meaning of bias-ie, prejudice-bias in research denotes deviation from the truth. 5 Participants in randomised controlled trials tend to be different (including being healthier 6–8) from those who choose not to take part, a function of the restricted entryįamily Health International, PO Box 13950, Research Triangle Park, NC 27709, USA (D A Grimes MD, K F Schulz PhD)Ĭorrespondence to: Dr David A Grimes (e-mail: The filtering process for admission to randomised trials might, therefore, result in “a type of hothouse flower, which cannot bloom or be successfully removed beyond its special greenery.” 5īias undermines the internal validity of research. This problem of unsuitable participants is also termed distorted assembly.
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For example, randomised controlled trials are more likely than observational studies to be free of bias, 4 but, because they usually enrol selected participants, external validity can suffer. Internal and external validity entail important tradeoffs. Gauging external validity is necessarily more subjective than is assessment of internal validity. 3 Internal validity is the sine qua non of clinical research extrapolation of invalid results to the broader population is not only worthless but potentially dangerous.Ī second important concern is external validity can results from study participants be extrapolated to the reader’s patients? Since a total enumeration or census approach to medical research is usually impossible, the customary tactic is to choose a sample, study it, and, hopefully, extrapolate the result to one’s practice.
In other words, a research study should avoid bias or systematic error. 2 The inference from participants in a study should be accurate. 1 Here, we will frame these two questions in terms of study validity, describe a simple checklist for readers, and offer some criteria by which to judge reported associations.Īnalogous to a laboratory test, a study should have internal validity-ie, the ability to measure what it sets out to measure. Criteria such as temporal sequence, strength and consistency of an association, and evidence of a dose-response effect lend support to a causal link.Ĭlinicians face two important questions as they read medical research: is the report believable, and, if so, is it relevant to my practice? Uncritical acceptance of published research has led to serious errors and squandered resources. Differentiation between spurious, indirect, and causal associations can be difficult. Chance should be examined last, however, since these biases can account for highly significant, though bogus results.
If a reader cannot explain away study results on the basis of selection, information, or confounding bias, then chance might be another explanation. Confounding can be controlled in several ways: restriction, matching, stratification, and more sophisticated multivariate techniques. 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). By contrast, non-differential misclassification tends to obscure real differences. If information is gathered differently for one group than for another, bias results. The effect of information bias depends on its type. Information bias results from incorrect determination of exposure, outcome, or both.
Selection bias stems from an absence of comparability between groups being studied. With respect to internal validity, selection bias, information bias, and confounding are present to some degree in all observational research. 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. Readers of medical literature need to consider two types of validity, internal and external. Bias and causal associations in observational research
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