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Starting with a model including all variables, we removed variables to find a model containing only variables with clear associations (P < 0.05) with mortality and where the residuals were not overly overdispersed or underdispersed.
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Wald testing was used to select the exposure variables for the model by adding and removing variables to determine which were statistically significantly associated with the outcome and stable in the presence of other variables.
A stepwise selection procedure (significance level for entry of 0.10 and 0.05 for stay), was used to remove variables unrelated to CS. Stratum-specific models were defined for groups I to IV and V-X that included only clinical and demographic variables selected using a stepwise selection procedure.
Removed variables were added back singly to the final model to test the appropriateness of the final model.
Removed variables were reentered one by one to the final model to obtain relevant statistics.
These nested models were determined by a backward feature elimination procedure that iteratively removed variables with the smallest contribution to the model likelihood.
Removed variables were re-entered one by one to the final model to obtain relevant statistics.
Furthermore, we removed variables that are linear combination of other variables.
The second model removed variables that did not present statistical significance in the first model.
The issue of removing variables prior to model building is, however, not without contention.
For the progeny lines with a significant marker-trait correlation, the data were also used to estimate additive and dominance effects of the QTL/gene using the model, (Model2) y i k = μ + a x + d z + ε i k Model (2) was modified from model (1) by removing variables related to the second locus.
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CEO of Professional Science Editing for Scientists @ prosciediting.com