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