Epidemiology

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By N. Lunet

ISBN-10: 9535103822

ISBN-13: 9789535103820

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Extra resources for Epidemiology -Current Perspectives on Research, Practice

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34 Epidemiology – Current Perspectives on Research and Practice The last statement should not be interpreted as a claim about the relation between any estimate and the true effect (‘the causal parameter’). That relation remains unknown, and an estimate from a biased study could happen to be closer to the unknown parameter than an estimate from an unbiased study. Claims about bias should therefore point the finger at the method that has generated an estimate (‘the estimator’), not at the estimate itself.

1 Deconfounding Most, if not all, of the methods to eliminate confounding bias are based on conditioning, which means (in its simplest form) restriction to one value of a variable. Conditioning (denoted by a box) dissociates a “variable”—now just a value—from both its causes and its effects, because a value is not associated with any other variable. For example, rather than estimating the marginal association between E and D, we would estimate a conditional association—the association between E and D when the confounder C is restricted to a single value.

To estimate the effect of EOFFERED on D, we invoke a theory of effect modification and assume that C-type variables have a null effect on EOFFERED when randomization is executed (R=randomization mechanism), in contrast to a non-null effect otherwise (R=not randomized). It is not that difficult, however, to propose C-type variables that have a non-null effect on EOFFERED even within a randomized trial (Shahar & Shahar, 2009). Although the effect of offering a treatment (EOFFEREDETAKEND) is of interest to the physician, the patient is far more interested in the effect of taking a treatment (ETAKEND).

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Epidemiology -Current Perspectives on Research, Practice by N. Lunet


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