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By using high-density human single nucleotide polymorphism data, we compared the variable model with the constant model.
This resulted in a five variable model with the epidemiologic performance characteristics of accuracy 65.2% (model correctly classified 4,406 of 5,450 subjects), sensitivity 66.1%, and specificity 62.2%.
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Columns 2 4 report the results of instrumental variable models with various levels of control for censoring in the dependent variables.
In order to investigate the effects of spatial variables on model performance, we compared the model fitness of the nine versions of the reduced-variable model with that with the full predictor variables.
The paper studies a linear errors-in-variables model with first order autoregressive processes.
For all non-significant real associations, we generated the predictor and target variables using the single-variable model with the parameters learned by that model on the real data.
bThe best three-variable model with deviance 132.3 and Hosmer-Lemeshow test, χ2 = 11.62; P = 0.17.
This yielded a 13-variable model with an average concordance value of 0.673.
aThe best two-variable model with deviance 92.9 and Hosmer-Lemeshow test, χ2 = 0.92; P = 0.1.
A 13-variable model with strong predictive performance was generated using a forward search strategy (mean AUC = 0.673).
Finally, the one-variable model with non-word repetition as the predictor variable was tested for its success in classifying parents according to proband status.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com