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Selection of adjustment variables was based on univariate analysis and variables previously noted in the literature to be associated with cognition and DR. Interactions among all independent variables were assessed.
Even with a reasonable selection of adjustment variables, when treatment and control groups differ vastly in characteristics, reliable effect estimates are not possible without breaching the assumptions of the statistical model.
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The selection of the adjustment variables in the first stage is shown in Fig. 1.
The selection of these adjustment variables was based upon our own hypotheses and previous findings from the literature.
That is, shifts in the benefits versus costs of high metabolic rates (and thus, heart rates) between the egg stage and the free-living juvenile may impose selection for adjustment of cardiovascular function.
During the prescription process in response to half of the alerts (69 of 136) the prescribers made adjustments (selection of another brand, adjustment of dosage regimen) and in the overwhelming majority (87%, 60 of 69) the adjustments clearly improved the medication regimen (Table 2).
Hence, baseline imbalances should not guide selection of covariates for adjustment in observational research.
In observational studies, the selection of covariates for adjustment should not be based on baseline imbalances either [ 5, 6].
The selection of a suitable adjustment method depends on the error rate that one wants to control [ 55].
Selection of covariates for adjustment in randomized trials is still frequently based on observed baseline imbalances between the study groups [ 1], even though this strategy is flawed and hence not recommended [ 2– 4].
The weights consist of two components: an unequal probability of selection adjustment and a poststratification adjustment.
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