The phrase "auxiliary outcomes" is correct and usable in written English.
It can be used in contexts where you are discussing secondary or additional results that accompany a primary outcome, often in research or analysis.
Example: "In addition to the main findings, the study also revealed several auxiliary outcomes that warrant further investigation."
Alternatives: "secondary results" or "additional findings".
The developed approaches are applied to Poisson regression model with missing outcomes based on auxiliary outcomes and a validated sample for true outcomes.
Applying the missing data methods presented in Section 3, we derive some estimation procedures for the Poisson regression model with missing outcomes based on auxiliary outcomes and a validated sample for true outcomes.
Applying the developed missing data methods, we derive the estimation procedures for Poisson regression model with missing outcomes based on auxiliary outcomes and a validated sample for true outcomes.
The relationship between the true outcome Y and the auxiliary outcome A in the available observations can inform about the missing values of Y. Let V be a subsample of the study subjects, termed the validation sample, for which both true and auxiliary outcomes are available.
Secondary outcomes were HIV viral load and CD4 counts, while auxiliary outcomes included weight and CD8 and CD3 counts.
Secondary outcomes included viral load and CD4 counts, and auxiliary outcomes included weight and CD3 and CD8 counts.
In some situations, an easily measured but less accurate outcome named auxiliary outcome variable, A, is supplemented.
As an auxiliary outcome, the approach also produces the latent capacity of a network that delivers the lowest impact.
The issues also rise when substituting auxiliary outcome for true outcome; see Ellenberg and Hamilton ([1989]), Prentice ([1989]) and Fleming ([1992]).
The number of nonspecific MAARI cases extracted using the ICD-9 codes is the auxiliary outcome A, whereas the actual number of influenza cases Y is the outcome of interest.
The mean score estimator is the MLE estimator employing the EM-algorithm ([Dempster et al. 1977]) under the assumption that the auxiliary outcome is noninformative in the sense that the probability model P θ (A|Y,X) is unrelated to β.
