4.6 In section 1.6 a study is described to evaluate a protocol change in disinfectant practice in a large midwestern university medical center. Control of infection is the primary concern for the 155 patients entered into the burn unit with varying degrees of burns. The outcome variable is the time until infection from admission to the unit. Censoring variables are discharge from the hospital without an infection or death without an infection. Eighty-four patients were in the group which had chlorhexidine as the disinfectant and 72 patients received the routine disinfectant povidone-iodine.
The survival function can be estimated by formula (4.2.1) in the textbook.
## Loading required package: splines
## Call: survfit(formula = Surv(bath$T3, bath$D3) ~ 1)
##
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 70 1 0.986 0.0142 0.958 1.000
## 3 69 3 0.943 0.0277 0.890 0.999
## 4 66 4 0.886 0.0380 0.814 0.963
## 5 62 1 0.871 0.0400 0.796 0.953
## 6 60 2 0.842 0.0436 0.761 0.932
## 7 58 3 0.799 0.0481 0.710 0.899
## 8 53 1 0.784 0.0495 0.693 0.887
## 9 50 2 0.752 0.0522 0.657 0.862
## 10 47 1 0.736 0.0535 0.639 0.849
## 11 44 2 0.703 0.0561 0.601 0.822
## 13 40 1 0.685 0.0574 0.582 0.808
## 19 33 1 0.665 0.0593 0.558 0.791
## 21 30 1 0.642 0.0613 0.533 0.774
## 23 27 1 0.619 0.0635 0.506 0.756
## 32 18 1 0.584 0.0686 0.464 0.735
## 44 8 1 0.511 0.0910 0.361 0.725
## 47 6 1 0.426 0.1086 0.258 0.702
## 51 4 1 0.320 0.1231 0.150 0.680
## Call: survfit(formula = Surv(cleaning$T3, cleaning$D3) ~ 1)
##
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 2 84 3 0.964 0.0202 0.925 1.000
## 3 81 1 0.952 0.0232 0.908 0.999
## 4 80 1 0.940 0.0258 0.891 0.992
## 5 79 5 0.881 0.0353 0.814 0.953
## 8 73 1 0.869 0.0369 0.800 0.944
## 10 70 1 0.856 0.0384 0.784 0.935
## 11 68 1 0.844 0.0398 0.769 0.926
## 14 57 1 0.829 0.0418 0.751 0.915
## 16 50 1 0.812 0.0441 0.730 0.904
## 17 47 2 0.778 0.0485 0.688 0.879
## 18 41 2 0.740 0.0531 0.643 0.852
## 42 9 1 0.658 0.0907 0.502 0.862
The cumulative hazard function H(t) can be estimated by -ln(S(t))
## time event risk ht1 standardError1
## 1 1 1 70 0.01439 0.01429
## 2 3 3 69 0.05884 0.02888
## 3 4 4 66 0.12136 0.04186
## 4 5 1 62 0.13762 0.04486
## 5 6 2 60 0.17152 0.05068
## 6 7 3 58 0.22463 0.05882
## 7 8 1 53 0.24368 0.06177
## 8 9 2 50 0.28450 0.06794
## 9 10 1 47 0.30601 0.07119
## 10 11 2 44 0.35253 0.07811
## 11 13 1 40 0.37785 0.08202
## 12 19 1 33 0.40862 0.08744
## 13 21 1 30 0.44252 0.09357
## 14 23 1 27 0.48026 0.10064
## 15 32 1 18 0.53742 0.11495
## 16 44 1 8 0.67095 0.16982
## 17 47 1 6 0.85327 0.23794
## 18 51 1 4 1.14095 0.34513
## time event risk ht2 standardError2
## 1 2 3 84 0.03637 0.02062
## 2 3 1 81 0.04879 0.02403
## 3 4 1 80 0.06137 0.02709
## 4 5 5 79 0.12675 0.03918
## 5 8 1 73 0.14055 0.04150
## 6 10 1 70 0.15493 0.04389
## 7 11 1 68 0.16975 0.04629
## 8 14 1 57 0.18745 0.04951
## 9 16 1 50 0.20765 0.05339
## 10 17 2 47 0.25114 0.06129
## 11 18 2 41 0.30115 0.07033
## 12 42 1 9 0.41893 0.13150
It seems two cumulative hazard rates are NOT proportional to each other.
## time S Linear Log Arcsin
## 1 1 0.9857 34.2455 3.8748 11.1374
## 2 3 0.9429 15.9628 4.9321 9.1031
## 3 4 0.8857 10.1431 4.9254 7.3717
## 4 5 0.8714 9.2840 4.8464 7.0056
## 5 6 0.8424 7.8476 4.6249 6.2996
## 6 7 0.7988 6.2148 4.2052 5.3406
## 7 8 0.7837 5.7345 4.0350 5.0209
## 8 9 0.7524 4.8322 3.6495 4.3726
## 9 10 0.7364 4.4170 3.4428 4.0541
## 10 11 0.7029 3.6186 2.9878 3.4055
## 11 13 0.6853 3.2311 2.7392 3.0743
## 12 19 0.6646 2.7770 2.4216 2.6721
## 13 21 0.6424 2.3238 2.0816 2.2588
## 14 23 0.6186 1.8690 1.7176 1.8331
## 15 32 0.5843 1.2279 1.1644 1.2161
## 16 44 0.5112 0.1234 0.1227 0.1234
## 17 47 0.4260 -0.6812 -0.6957 -0.6762
## 18 51 0.3195 -1.4668 -1.4765 -1.3995
We can find the 95% confidence interval for the median by selecting value between [-1.96, 1.96] from above frame, so the CI for both three methods are x >23. The estimated median is x = 47, because surv(x=47)=0.426<0.5.
## time S Linear Log Arcsin
## 1 2 0.9643 22.930 5.105 10.912
## 2 3 0.9524 19.469 5.307 10.364
## 3 4 0.9405 17.063 5.420 9.879
## 4 5 0.8810 10.781 5.369 7.939
## 5 8 0.8689 10.009 5.288 7.599
## 6 10 0.8565 9.293 5.183 7.254
## 7 11 0.8439 8.638 5.062 6.915
## 8 14 0.8291 7.877 4.865 6.473
## 9 16 0.8125 7.085 4.611 5.974
## 10 17 0.7779 5.726 4.087 5.047
## 11 18 0.7400 4.522 3.501 4.138
## 12 42 0.6578 1.738 1.529 1.678
We can find the 95% confidence interval for the median by selecting value between [-1.96, 1.96] from above frame, so the CI for both three methods are x > 42. The estimated median is larger than 47, we cannot find the exact value.
## [1] 0.6142
## [1] 0.8252
The 95% confidence intervals for the survival (infection-free) functions using the arcsine transformed is :
## [1] 0.6258
## [1] 0.8336
The 95% confidence intervals for the survival (infection-free) functions using the log transformed is :
## [1] 0.7611
## [1] 0.9158
The 95% confidence intervals for the survival (infection-free) functions using the arcsine transformed is :
## [1] 0.7737
## [1] 0.9229
## time lower upper
## 7 8 0.6100 0.9166
## 8 9 0.5722 0.8958
## 9 10 0.5533 0.8848
## 10 11 0.5141 0.8613
## 11 13 0.4938 0.8488
## 12 19 0.4684 0.8349
## time lower upper
## 5 8 0.7409 0.9577
## 6 10 0.7247 0.9503
## 7 11 0.7084 0.9426
## 8 14 0.6883 0.9339
## 9 16 0.6652 0.9243
## 10 17 0.6187 0.9035
## 11 18 0.5689 0.8799
## time lower upper
## 7 8 0.6034 0.9204
## 8 9 0.5751 0.8940
## 9 10 0.5603 0.8802
## 10 11 0.5285 0.8512
## 11 13 0.5114 0.8359
## 12 19 0.4899 0.8187
## time lower upper
## 5 8 0.6949 0.9759
## 6 10 0.6855 0.9673
## 7 11 0.6755 0.9580
## 8 14 0.6626 0.9468
## 9 16 0.6472 0.9340
## 10 17 0.6138 0.9065
## 11 18 0.5750 0.8758
We can combine the graphs in (f) into one picture.
Although we can see the survival line of new bathing method is slightly better than that of routine bathing method, we cannot decide which one is better because two confidence intervals are highly overlapped.