I have been developing a logistic regression model based on retrospective data from a national trauma database of head injury in the UK. The key outcome is 30 day mortality (denoted as
Outcome30 measure). Other measures across the whole database with published evidence of significant effect on outcome in previous studies include:
Yeardecimal - Date of procedure = 1994.0-2013.99 inctoCran - Time from head injury to craniotomy in minutes = 0-2880 (After 2880 minutes is defined as a separate diagnosis) ISS - Injury Severity Score = 1-75 Age - Age of patient = 16.0-101.5 GCS - Glasgow Coma Scale = 3-15 Sex - Gender of patient = Male or Female rcteyemi - Pupil reactivity (1 = neither, 2 = one, 3 = both) neuroFirst2 - Location of admission (Neurosurgical unit or not) Other - other traums (0 - No, 1 - Yes) othopYN - Other operation required LOS - Length of stay in days LOSCC - Length of stay in critical care in days
When I conduct univariate analysis of the variables, I have conducted a logistic regression for each continuous variable. I am unable to model Yeardecimal however, with the following result:
> rcs.ASDH<-lrm(formula = Survive ~ Yeardecimal, data = ASDH_Paper1.1) singular information matrix in lrm.fit (rank= 1 ). Offending variable(s): Yeardecimal Error in lrm(formula = Survive ~ Yeardecimal, data = ASDH_Paper1.1) : Unable to fit model using “lrm.fit”
However, the restricted cubic spline works:
> rcs.ASDH<-lrm(formula = Survive ~ rcs(Yeardecimal), data = ASDH_Paper1.1) > > rcs.ASDH Logistic Regression Model lrm(formula = Survive ~ rcs(Yeardecimal), data = ASDH_Paper1.1) Model Likelihood Discrimination Rank Discrim. Ratio Test Indexes Indexes Obs 5998 LR chi2 106.61 R2 0.027 C 0.578 0 1281 d.f. 4 g 0.319 Dxy 0.155 1 4717 Pr(> chi2) <0.0001 gr 1.376 gamma 0.160 max |deriv| 2e-08 gp 0.057 tau-a 0.052 Brier 0.165 Coef S.E. Wald Z Pr(>|Z|) Intercept -68.3035 45.8473 -1.49 0.1363 Yeardecimal 0.0345 0.0229 1.51 0.1321 Yeardecimal' 0.1071 0.0482 2.22 0.0262 Yeardecimal'' -2.0008 0.6340 -3.16 0.0016 Yeardecimal''' 11.3582 4.0002 2.84 0.0045
Could anyone explain why this is? I am nervous about using a mode complicated model if I am unable to model with a simpler approach.
I am currently using restricted cubic splines to model Age, ISS and Yeardecimal. Would anyone recommend any alternative approach?