Dear list,
I made a logistic regression model (MyModel) using lrm and penalization
by pentrace for data of 104 patients, which consists of 5 explanatory
variables and one binary outcome (poor/good). Then, I found bootcov and
robcov function in rms package for calculation of confidence range of
coefficients and odds ratio by bootstrap covariance matrix and
Huber-White sandwich method, respectively.
> MyModel.boot <- bootcov(MyModel, B=1000, coef.reps=T)
> MyModel.robcov <- robcov(MyModel)
> anova(MyModel)
Wald Statistics Response: outcome
Factor Chi-Square d.f. P
stenosis 0.20 1 0.6547
x1 10.69 1 0.0011
x2 2.33 1 0.1270
procedure 3.27 1 0.0708
ClinicalScore 2.55 1 0.1102
TOTAL 18.71 5 0.0022
> anova(MyModel.boot)
Wald Statistics Response: outcome
Factor Chi-Square d.f. P
stenosis 0.16 1 0.6921
x1 17.90 1 <.0001
x2 3.36 1 0.0669
procedure 4.62 1 0.0316
ClinicalScore 1.82 1 0.1774
TOTAL 31.82 5 <.0001
> anova(MyModel.robcov)
Wald Statistics Response: outcome
Factor Chi-Square d.f. P
stenosis 0.17 1 0.6758
x1 20.52 1 <.0001
x2 3.83 1 0.0505
procedure 5.09 1 0.0241
ClinicalScore 1.84 1 0.1744
TOTAL 34.80 5 <.0001
The confidence intervals are narrower in bootcov model, and further
narrower in robcov model than in original model, as demonstrated in the
followings.
I am wondering which confidence interval should be used.
I would appreciate anybody's help in advance.
--
KH
> summary(MyModel, stenosis=c(70, 80), x1=c(1.5, 2.0), x2=c(1.5, 2.0))
Effects Response : outcome
Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
stenosis 70.0 80 10.0 -0.11 0.24 -0.59 0.37
Odds Ratio 70.0 80 10.0 0.90 NA 0.56 1.45
x1 1.5 2 0.5 1.21 0.37 0.49 1.94
Odds Ratio 1.5 2 0.5 3.36 NA 1.63 6.95
x2 1.5 2 0.5 -0.29 0.19 -0.65 0.08
Odds Ratio 1.5 2 0.5 0.75 NA 0.52 1.08
ClinicalScore 3.0 5 2.0 0.61 0.38 -0.14 1.36
Odds Ratio 3.0 5 2.0 1.84 NA 0.87 3.89
procedure - CA:CE 2.0 1 NA 0.83 0.46 -0.07 1.72
Odds Ratio 2.0 1 NA 2.28 NA 0.93 5.59
> summary(MyModel.boot, stenosis=c(70, 80), x1=c(1.5, 2.0), x2=c(1.5, 2.0))
Effects Response : outcome
Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
stenosis 70.0 80 10.0 -0.11 0.28 -0.65 0.43
Odds Ratio 70.0 80 10.0 0.90 NA 0.52 1.54
x1 1.5 2 0.5 1.21 0.29 0.65 1.77
Odds Ratio 1.5 2 0.5 3.36 NA 1.92 5.89
x2 1.5 2 0.5 -0.29 0.16 -0.59 0.02
Odds Ratio 1.5 2 0.5 0.75 NA 0.55 1.02
ClinicalScore 3.0 5 2.0 0.61 0.45 -0.28 1.50
Odds Ratio 3.0 5 2.0 1.84 NA 0.76 4.47
procedure - CAS:CEA 2.0 1 NA 0.83 0.38 0.07 1.58
Odds Ratio 2.0 1 NA 2.28 NA 1.08 4.85
> summary(MyModel.robcov, stenosis=c(70, 80), T1=c(1.5, 2.0), T2=c(1.5,
2.0))
Effects Response : outcome
Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
stenosis 70.0 80 10.0 -0.11 0.26 -0.62 0.40
Odds Ratio 70.0 80 10.0 0.90 NA 0.54 1.50
x1 1.5 2 0.5 1.21 0.27 0.69 1.74
Odds Ratio 1.5 2 0.5 3.36 NA 1.99 5.68
x2 1.5 2 0.5 -0.29 0.15 -0.57 0.00
Odds Ratio 1.5 2 0.5 0.75 NA 0.56 1.00
ClinicalScore 3.0 5 2.0 0.61 0.45 -0.27 1.49
Odds Ratio 3.0 5 2.0 1.84 NA 0.76 4.44
procedure - CAS:CEA 2.0 1 NA 0.83 0.37 0.11 1.54
Odds Ratio 2.0 1 NA 2.28 NA 1.11 4.68
