Exact(1)
Using this initial model as a starting point, the final predictor variables were determined by utilizing a manual stepwise procedure (with p <.05 as the criterion for variables to either enter or stay in the model), using all of the potential predictor variables (including variables not in the initial model).
Similar(59)
Variables with a univariable p-value ≤0.2 were eligible for inclusion in multivariable models built using a manual stepwise approach.
A manual stepwise approach was used, beginning with a univariate testing (α = 0.05).
A manual stepwise procedure was used to build the multivariable models.
We used a manual stepwise backward method to remove non-significant variables.
A manual stepwise procedure was used for the addition of individual predictors to build the model.
A manual stepwise technique was used to arrive at a model that contained only significant predictive characteristics.
Factors that were statistically significant (p < 0.05) in unadjusted analyses were added to a multivariate model, using a manual stepwise procedure.
Here final multivariable models (Table 3, 4, 5, 6) were obtained with a manual stepwise backward elimination procedure with a cut-off level at P < 0.05.
We performed a manual stepwise backward multiple variable regression with city as random intercept to obtain a parsimonious model with significantly (p < 0.05) associated predictors of each outcome.
A multivariate logistic regression model was then used to identify independent risk factors for HCV seropositivity using a manual stepwise strategy.
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