Table 7.
Variable | Effects | Model p- value |
R2 | AICc | Lack of Fit p- value |
n | Effect | Effect p- value# |
FDR |
---|---|---|---|---|---|---|---|---|---|
T2DM | Age, HG, gender |
<0.0001 | 0.0402 | 1317.24 | 0.5473 | 1100 | Age | <0.0001 | 0.00001 |
HG | <0.0001 | 0.00012 | |||||||
Gender | 0.4912 | 0.49121 | |||||||
HTN | Age, HG, gender |
<0.0001 | 0.0359 | 1229.28 | 0.0278 | 969 | Age | <0.0001 | 0.00000 |
HG | 0.0026 | 0.00394 | |||||||
Gender | 0.0075 | 0.00753 | |||||||
OB | Age, HG, gender |
<0.0001 | 0.1335 | 1057.55 | 0.4180 | 1085 | Age | <0.0001 | 0.00000 |
HG | 0.0562 | 0.08437 | |||||||
Gender | 0.1444 | 0.14439 |
R2: The proportion of the total uncertainty that is attributed to the model fit. Because certainty in the predicted probabilities is rare for logistic models, R2 tends to be small.
AICc: Corrected Akaike Information Criterion that assesses model fit based on –2LogLikelihood.
Lack of Fit: Chi-square statistics on the negative log-likelihood error due to lack of fit, error in a saturated model (pure error), and the total error in the fitted model. High p-values denote that the lack of fit Chi-square is not significant and support the conclusion that there is little to be gained by introducing additional variables.
FDR: Benjamini-Hochberg false discovery rate.
Age levels: advanced ≥60, 30 ≤medium < 60, young < 30.
HG levels: D5, F4, N9a, macro-M (D, E, G, M*, M7, M8, M9, M10), and macro-N (A, B, F, R).
Likelihood-Ratio test p-value.