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In the multivariate models, we introduced explanatory variables mostly at the patient level.
In order to conduct the conditional logistic regression models, we introduced matching on age at diagnosis.
In separate models, we introduced the interaction term SG × family history of diabetes.
After we established the basic models, we introduced the pollutant variables and analyzed their effects on mortality outcomes.
Again, using generalized linear models, we introduced an interaction between time and group to evaluate the effect in EPTR.
In the multivariable models we introduced covariates that were associated with the outcome at P < 0.10 in univariate analyses.
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To assess the reliability of the surface models, we introduce a spatial uncertainty measurement based on grayscale gradient scale length.
In all types of mouse models, we introduce mutations to initiate and speed up neoplastic transformation.
In order to efficiently analyze the properties of infinitely visited states of large models, we introduce a symbolic algorithm which builds on the structure of our modeling method.
To overcome the limitations of synchronous or asynchronous models, we introduce events, discrete signals that guide or restrain the choice of fireable transitions.
In order to deal with biases associated with misspecification of our model, we introduced several control variables.
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CEO of Professional Science Editing for Scientists @ prosciediting.com