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Rev Epidemiol Sante Publique. 2000 Aug;48(4):389-400.

[Modeling incomplete observations].

[Article in French]

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  • 1INSERM U472, 16 Av. P. Vaillant-Couturier, 94807 Villejuif, France.


Incomplete observations, common in epidemiology as in many other fields, lead to problems of bias, precision and power. Using a simple example with 3 binary variables, we discuss situations where the observed odds ratio is biased. We present and compare the main strategies of analysis: complete observations modeling, missing data indicator, weighted analysis, simple imputation, multiple imputation, selection models, shared variable models.

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