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Some of the non-binary variables such as age and Charlson index are currently provided as their verbatim value, which means models such as logistic regression (propensity scores) will model them as linear. However, a linear assumption is almost never realistic.
FeatureExtraction could also offer these same variables as splines, by already computing the spline design matrix. An example where I've done this before is here in the SelfControlledCaseSeries package. The hard part would be the administration of the covariate IDs for the design matrix variables.
The text was updated successfully, but these errors were encountered:
Some of the non-binary variables such as age and Charlson index are currently provided as their verbatim value, which means models such as logistic regression (propensity scores) will model them as linear. However, a linear assumption is almost never realistic.
FeatureExtraction could also offer these same variables as splines, by already computing the spline design matrix. An example where I've done this before is here in the SelfControlledCaseSeries package. The hard part would be the administration of the covariate IDs for the design matrix variables.
The text was updated successfully, but these errors were encountered: