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Due to history dependence of plasticity, adjoint variables must be solved via a stepwise backward procedure.
The analysis was performed with multivariate logistic regression models, using a stepwise backward elimination with complete case analysis.
A stepwise backward process was then performed until all p values were <0.05, and models were compared using the Wald test.
All parameters which were under the significance threshold of <0.05 were entered into a stepwise, backward multivariate logistic regression analysis for mortality at 12 months.
The proposed greedy multi-relay selection method is a stepwise backward selection algorithm, where we optimize the selection based on the SINR criterion at each stage.
We also included the interaction term 'presence of eggs × body size' and excluded non-significant terms in a stepwise backward elimination procedure as mentioned above.
For model building, a stepwise backward elimination procedure was performed.
A stepwise backward multivariate analysis included all variables with p<0.25 in the univariate analysis.
Risk factors were included in a stepwise backward multivariate model if p<0.1 to obtain independent adjusted ORs.
A stepwise backward regression was performed.
A stepwise backward elimination approach was applied.
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