[Modeling incomplete observations].
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.
Chavance
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INSERM U472, 16 Av. P. Vaillant-Couturier, 94807 Villejuif, France.
Manfredi
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D015982:Bias; D003627:Data Interpretation, Statistical; D015991:Epidemiologic Measurements; D015340:Epidemiologic Research Design; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D016017:Odds Ratio