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Using all JHS individuals, we also built a 10 SNP model by stepwise linear regression for validation in other cohorts (see below).
For each group, 3 sets were used for SNP discovery (p<0.05 for association with sex and ancestry-adjusted Lp(a)), one used to build a model by stepwise regression/ANOVA (including coefficient estimation), and the fifth used as a test set to evaluate either the percentage of residual variance or local-ancestry specific variance explained.
Variables were introduced in the model by stepwise selection at the 5% significance level.
Multivariate Cox proportional hazards model by stepwise methods was performed to test the independent function of each screened prognostic variables of univariate analysis.
Variables that had a univariate probability value <0.20 or those judged to be clinically important were selected for inclusion in a logistic regression model by stepwise selection.
Univariate analysis was applied and variables with statistical significance were included in a multivariate model by stepwise logistic regression to identify independent predictors.
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These retention times were modelled by stepwise multiple linear regressions.
We reduced the full models by stepwise exclusion of non-significant fixed terms, taking into consideration the AICc: if the exclusion of a non-significant term increased the AICc this term was re-entered into the model.
Registry area, age, marital status, race, year of diagnosis, tumor histology, grade, stage, tumor size, number of positive nodes, number of examined nodes, odds of nodal involvement, extent of surgery, and radiotherapy were evaluated in Cox models by stepwise selection using the Akaike Information Criteria.
In a second step, we determined the optimal model built by stepwise logistic model building based on the corrected Akaike's criterion (AICc) [ 1, 20, 47] that limits overfitting.
The model also includes a seventh-order autoregressive error model, selected by stepwise regression.
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