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We developed a final model using backward elimination, with variables with p>0.05 eliminated from the model.
The final model was constructed using a manual backward stepwise elimination procedure (p > 0.20): we eliminated from the model the predictors one-by-one on the basis of the highest p-value.
Race, gender, dialysis center, time of entry into the study, serum iron, TIBC, MCV, MCH, serum calcium, and serum phosphorus were eliminated from the model in backward elimination as they failed to reach the level of significance (P < 0.05).
The pandemic wave indicator was the only predictor that was eliminated from the model by the backward elimination procedure (P = 0.53).
Other economic and sociodemographic variables such as government transfers to the affected states through the hurricane relief funds or the proportion of people aged 60 and above in total state population were also considered in initial modeling efforts, but were later eliminated from the model since they were not significant predictors of changes in income distribution.
All the statistically non-significant terms were eliminated from the model.
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Interactions were therefore eliminated from the models.
All variables with P ≥ 0.05 were eliminated from the models.
In the absence of multicolinearity, covariates were not eliminated from the models.
Nonsignificant interaction effects between diagnosis and age were subsequently eliminated from the models.
Additionally, when infection was eliminated from the models, there were no major changes in the OC – immune markers association.
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