Your English writing platform
Discover LudwigSuggestions(1)
Exact(3)
To "optimize" the multivariable model, a stepwise approach was used (backward elimination with Wald criteria) [ 17 ].
To identify the most parsimonious prediction model, a stepwise Cox proportional hazards regression analysis was conducted (P values for entry =0.25 and for retaining in the model =0.05).
After having entered all selected variables simultaneously in the conditional logistic regression model, a stepwise procedure was used where at each step the variable with the highest asymptotic P-value >0.40 was eliminated.
Similar(57)
The logistic regression analysis was an enter model not a stepwise model.
Each independent variable was first analysed in the univariate setting and then all variables with a p-value < 0.1 analysed together in a multivariate model using a stepwise model search to find the relevant predictors.
Statistical Note: Two types of predictive models were developed, a decision tree model and a stepwise regression model.
Similar to the logistic regression model, we established the probit model using a stepwise procedure, and we selected the best model based on the AIC.
We build up the mathematical model in a stepwise manner, starting with a model of viral replication.
Non-significant terms were dropped from the full model in a stepwise manner to leave a minimal model, given in Table 1.
Næsset et al. (2016) used a linear ordinary least squares model and a stepwise predictor variable selection method.
For model building, a stepwise backward elimination procedure was performed.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com