By N. Lunet
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Additional info for Epidemiology -Current Perspectives on Research, Practice
Nevertheless, the model provides, for individuals or populations, a causal interpretation to measures of association. More importantly, the counterfactual ideal is relevant in the design and analysis of etiologic studies since it provides a framework for choosing the target population as well as an appropriate substitute for that target. It is also an important approach to clarify threats to validity. 4 Causal diagrams One of the most intuitive ways of representing and communicating hypothetical causal relations between epidemiological variables is to visually depict the paths believed to relate them, namely exposure, outcome and possible confounders.
4 Matching: Attempting to deconfound efficiently There are two common forms of matching: 1) matching the confounder distribution in the categories of E (a matched cohort study); 2) matching the confounder distribution in the categories of D (a matched case-control study). Both methods try to remove confounding bias more efficiently—reduce the variance—than would have been achieved otherwise with 37 Causal Diagrams and Three Pairs of Biases a given sample size. In both cases, however, the saving of variance is not guaranteed and is not free.
Moreover, scientists often struggle with an inevitable trade-off between bias, variance, and effort. For example, reducing bias costs extra variance and reducing variance costs extra bias. No algorithm can strike a balance between the two. It is a human decision. 32 Epidemiology – Current Perspectives on Research and Practice 2. Effect modification The effect ED might depend on the value of another cause of D, say Q (QD). For instance, the harmful effect of heparin injection (E) on bleeding (D) could depend on aspirin use (Q): heparin might have a stronger effect on bleeding in the presence of aspirin in the blood than in its absence.
Epidemiology -Current Perspectives on Research, Practice by N. Lunet