External cogent evidence, repeat determination, and preciseness of risk estimation in misclassified exposure data in epidemiology
Stephen W Duffy, Dmitrii M Maximovitch, Nicholas E Day
Abstract Study objective-The take up was to quantify the difference in precision of risk estimates in epidemiology between the situations where misclassification of exposure is corrected for by out-of-door validation and where it is corrected for by internal repeat measurement. Precision was metrical in terms of the expected width of the 95% presumption interval on the betting odds ratio. Design-In a hypothetical case-control study, original with 100 cases and 100 controls, then with 100 cases and 1000 controls (the latter(prenominal) to approximate the cohort study situation), expected estimated odds ratios and sureness intervals were calculated establish on postulated underlying true odds ratios and misclassification error rates. The sizes of the confidence intervals utilise the two design strategies were compared, based on the same number of subjects receiving internal repeat measurements as were used in the external validation study. Main results-Confidence intervals obtained using internal repeat measurement were considerably narrower than those using external validation.
Both methods yielded approximately correct point estimates. Conclusions-In terms of precision, it is preferable to correct for misclassification using internal repeat measurement sort of than external validation. J Epidemiol Community Health 1992; 46: 620-624
the distributor point of measurement error should be made for purposes of accounting for mismeasurement in the analysis. In this paper, we consider the effect of mismeasurement on estimates of risk in the absence of confounding. The problem of controlling for confounding in the guinea pig of measurement error we leave to a later paper. In the absence of confounding, measurement error which applies equally to infirmityd and disease free...If you want to get a full essay, order it on our website: Orderessay
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