Study designs.

Department of Clinical Pharmacology, Seth GS Medical College and KEM Hospital, Parel, Mumbai - 400 012, India.
International journal of Ayurveda research 04/2010; 1(2):128-31. DOI: 10.4103/0974-7788.64406
Source: PubMed
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    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.
    The Lancet 02/2002; 359(9302):248-52. DOI:10.1016/S0140-6736(02)07451-2 · 39.21 Impact Factor
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    ABSTRACT: We describe an extension of an existing framework used by several investigators for classifying various types of bias. The framework consists of three categories of bias: selection, information, and confounding. The existing framework is expanded to include subclassification according to the type of study design: cross-sectional, case-control, retrospective cohort, and prospective cohort. Direction and method of prevention of biases within each category in the framework are discussed. This article provides a useful checklist for epidemiologists to determine possible sources and methods of reduction of bias that are specific to a particular type of study design.
    Journal of occupational medicine.: official publication of the Industrial Medical Association 04/1992; 34(3):265-71. DOI:10.1097/00043764-199203000-00010
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    ABSTRACT: Since updated population registers do not exist in many countries it is often difficult to sample valid population controls from the study base to a case-control study. Use of patient controls is an alternative option if the exposure experience under study for these patients are interchangeable with the experience for population controls. Patient controls may even be preferable from population controls under certain conditions. In this study we examine if colon cancer patients can serve as surrogates for proper population controls in case-control studies of occupational risk factors. The study was conducted from 1995 to 1997. Incident colon cancer controls (N = 428) aged 35-69 years with a histological verified diagnosis and population controls (N = 583) were selected. Altogether 254 (59%) of the colon cancer controls and 320 (55%) of the population controls were interviewed about occupational, medical and life style conditions. No statistical significant difference for educational level, medical history or smoking status was seen between the two control groups. There was evidence of a higher alcohol intake, less frequent work as a farmer and less exposure to pesticides among colon cancer controls. Use of colon cancer controls may provide valid exposure estimates in studies of many occupational risk factors for cancer, but not for studies on exposure related to farming.
    BMC Cancer 05/2004; 4:15. DOI:10.1186/1471-2407-4-15 · 3.32 Impact Factor