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All available variables will be used without missing outcomes being factored in.
All available results will be used without missing outcomes being imputed.
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Under the assumption that data are missing at random (MAR), we assume instead that the participants' missing outcomes are independent of the missing data indicator (see below), given the covariates and any observed outcomes.
The procedures for Poisson regression using automated data with missing outcomes are derived in Section 4. The finite-sample performances of the estimators are studied in simulations in Section 5.
Patients with missing outcomes were all classified as successes in one analysis and then as failures in a separate analysis.
In modeling the Z-score of the waist circumference at young adulthood as the outcome measure (number of missing outcomes is 5), we obtained similar results for the age interval 2 6 y.
With outcomes at one follow-up time point, complete case is a valid analysis assuming missing outcomes are MAR [ 36].
Thus, the assumption that all missing outcomes are non-adherent leads to an overly conservative estimate of effect size.
However, the reasons for missing outcomes are unrelated to the actual outcome and to maternal and gestational age in cases subsequently traced.
Under the assumption of covariate dependent missingness, missing outcomes were handled by complete case analysis, standard multiple imputation (MI) and within-cluster MI strategies.
Under the MAR assumption we expect that the missing outcomes are only dependent on the observed data and not on the unobserved data [ 23].
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