I have been playing around with using restricted cubic splines using the RMS package. Output below.
library(rms) nlmodel_ni_bi_4 <- lrm(outcome~ rcs(age,4) + ethnicity + AV + sex + nb, data=df) nlmodel_ni_bi_4 Frequencies of Missing Values Due to Each Variable outcome age ethnicity AV_binary poisex n_charge_binary 0 0 3896 0 12 0 Logistic Regression Model lrm(formula = outcome ~ rcs(age, 4) + ethnicity + AV + sex + nb, data = df) Model Likelihood Discrimination Rank Discrim. Ratio Test Indexes Indexes Obs 62364 LR chi2 4200.40 R2 0.112 C 0.690 0 52455 d.f. 7 g 0.719 Dxy 0.380 1 9909 Pr(> chi2) <0.0001 gr 2.052 gamma 0.386 max |deriv| 2e-11 gp 0.100 tau-a 0.102 Brier 0.123 Coef S.E. Wald Z Pr(>|Z|) Intercept -7.2339 0.3149 -22.97 <0.0001 age 0.4079 0.0239 17.05 <0.0001 age' -0.6351 0.0483 -13.15 <0.0001 age'' 2.4672 0.2589 9.53 <0.0001 ethnicity=NI -0.6664 0.0299 -22.31 <0.0001 AV=1 0.6583 0.0252 26.14 <0.0001 sex=M 0.2920 0.0274 10.67 <0.0001 nb=1 1.1922 0.0244 48.82 <0.0001
I am used to running logistic regression where all of the predictors are either continuous linear or categorical. Here, when describing the individual predictors effect on the outcome, we would present the adjusted odds ratio, associated p value and sometimes relative risk. I am not sure how to report the age predictor in my current model with RCS. I am lost on a number of issues:
Exactly what are the three terms associated with the age variable in the output (age, age', age''). Is this the derivative and the derivative of the derivative? or is it a term for each knot that has been fitted?
With a linear term the adjusted odds ratio has a simple interpretation, with a consistent slope. Given that the RCS is not linear, what is the recommended way to describe its effect?
Are there any guidelines for how to report predictors fitted with splines?