Survival by STS (high, low, none)
## Call: survfit(formula = Surv(time2death, death) ~ sts.order, data = dat)
##
## n events median 0.95LCL 0.95UCL
## sts.order=high 48 30 0.567 0.40 1.16
## sts.order=low 19 8 2.256 1.86 NA
## sts.order=none 61 44 1.122 0.49 3.47
## Call: survfit(formula = Surv(time2death, death) ~ sts.order, data = dat)
##
## sts.order=high
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 48 0 1.000 0.0000 1.0000 1.000
## 0.5 23 18 0.592 0.0747 0.4624 0.758
## 1.0 12 7 0.404 0.0784 0.2762 0.591
## 2.0 4 3 0.299 0.0782 0.1794 0.499
## 3.0 2 1 0.200 0.0967 0.0772 0.516
## 4.0 1 0 0.200 0.0967 0.0772 0.516
##
## sts.order=low
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 19 0 1.000 0.0000 1.0000 1.000
## 0.5 13 3 0.812 0.0976 0.6421 1.000
## 1.0 10 1 0.750 0.1083 0.5652 0.995
## 2.0 5 1 0.643 0.1358 0.4249 0.973
## 3.0 2 2 0.386 0.1627 0.1687 0.882
## 4.0 1 1 0.193 0.1588 0.0384 0.969
##
## sts.order=none
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 61 0 1.000 0.0000 1.000 1.000
## 0.5 34 23 0.609 0.0639 0.495 0.748
## 1.0 28 6 0.501 0.0660 0.387 0.649
## 2.0 20 6 0.392 0.0650 0.283 0.543
## 3.0 13 2 0.351 0.0644 0.244 0.503
## 4.0 10 2 0.292 0.0656 0.188 0.454
## 5.0 9 0 0.292 0.0656 0.188 0.454
## Call:
## coxph(formula = Surv(time2death, death) ~ sts.order, data = dat)
##
## n= 128, number of events= 82
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sts.orderlow -0.7917 0.4531 0.3997 -1.981 0.0476 *
## sts.ordernone -0.2855 0.7516 0.2519 -1.134 0.2569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sts.orderlow 0.4531 2.207 0.2070 0.9917
## sts.ordernone 0.7516 1.330 0.4588 1.2313
##
## Concordance= 0.557 (se = 0.033 )
## Rsquare= 0.035 (max possible= 0.994 )
## Likelihood ratio test= 4.54 on 2 df, p=0.1034
## Wald test = 4.14 on 2 df, p=0.1259
## Score (logrank) test = 4.28 on 2 df, p=0.1178
Survival by STS (yes, no)
## Call: survfit(formula = Surv(time2death, death) ~ trt, data = dat)
##
## n events median 0.95LCL 0.95UCL
## trt=TRUE 67 38 1.01 0.553 2.45
## trt=FALSE 61 44 1.12 0.490 3.47
## Call: survfit(formula = Surv(time2death, death) ~ trt, data = dat)
##
## trt=TRUE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 67 0 1.000 0.0000 1.0000 1.000
## 0.5 36 21 0.657 0.0614 0.5467 0.788
## 1.0 22 8 0.506 0.0668 0.3906 0.655
## 2.0 9 4 0.395 0.0731 0.2746 0.568
## 3.0 4 3 0.237 0.0841 0.1182 0.475
## 4.0 2 1 0.178 0.0813 0.0725 0.436
##
## trt=FALSE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 61 0 1.000 0.0000 1.000 1.000
## 0.5 34 23 0.609 0.0639 0.495 0.748
## 1.0 28 6 0.501 0.0660 0.387 0.649
## 2.0 20 6 0.392 0.0650 0.283 0.543
## 3.0 13 2 0.351 0.0644 0.244 0.503
## 4.0 10 2 0.292 0.0656 0.188 0.454
## 5.0 9 0 0.292 0.0656 0.188 0.454
## Call:
## coxph(formula = Surv(time2death, death) ~ trt, data = dat)
##
## n= 128, number of events= 82
##
## coef exp(coef) se(coef) z Pr(>|z|)
## trtFALSE -0.05117 0.95011 0.23471 -0.218 0.827
##
## exp(coef) exp(-coef) lower .95 upper .95
## trtFALSE 0.9501 1.053 0.5998 1.505
##
## Concordance= 0.47 (se = 0.031 )
## Rsquare= 0 (max possible= 0.994 )
## Likelihood ratio test= 0.05 on 1 df, p=0.8274
## Wald test = 0.05 on 1 df, p=0.8274
## Score (logrank) test = 0.05 on 1 df, p=0.8274
Death or Hospitalization by STS (high, low, none)
## Call: survfit(formula = Surv(time2death, death.or.hosp) ~ sts.order,
## data = dat)
##
## n events median 0.95LCL 0.95UCL
## sts.order=high 48 36 0.553 0.356 1.05
## sts.order=low 19 9 2.152 1.292 NA
## sts.order=none 61 53 1.122 0.490 2.36
## Call: survfit(formula = Surv(time2death, death.or.hosp) ~ sts.order,
## data = dat)
##
## sts.order=high
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 48 0 1.0000 0.0000 1.0000 1.000
## 0.5 23 20 0.5491 0.0753 0.4197 0.718
## 1.0 12 8 0.3479 0.0747 0.2285 0.530
## 2.0 4 5 0.1841 0.0673 0.0899 0.377
## 3.0 2 1 0.1227 0.0673 0.0419 0.359
## 4.0 1 1 0.0614 0.0549 0.0106 0.354
##
## sts.order=low
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 19 0 1.000 0.0000 1.0000 1.000
## 0.5 13 3 0.812 0.0976 0.6421 1.000
## 1.0 10 1 0.750 0.1083 0.5652 0.995
## 2.0 5 2 0.571 0.1382 0.3557 0.918
## 3.0 2 2 0.343 0.1502 0.1453 0.809
## 4.0 1 1 0.171 0.1426 0.0336 0.875
##
## sts.order=none
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 61 0 1.000 0.0000 1.0000 1.000
## 0.5 34 23 0.609 0.0639 0.4953 0.748
## 1.0 28 6 0.501 0.0660 0.3872 0.649
## 2.0 20 7 0.375 0.0644 0.2679 0.525
## 3.0 13 7 0.244 0.0579 0.1531 0.388
## 4.0 10 3 0.188 0.0529 0.1079 0.326
## 5.0 9 1 0.169 0.0508 0.0936 0.304
## Call:
## coxph(formula = Surv(time2death, death.or.hosp) ~ sts.order,
## data = dat)
##
## n= 128, number of events= 98
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sts.orderlow -0.9055 0.4043 0.3746 -2.417 0.0156 *
## sts.ordernone -0.4227 0.6553 0.2326 -1.817 0.0692 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sts.orderlow 0.4043 2.473 0.1940 0.8426
## sts.ordernone 0.6553 1.526 0.4154 1.0337
##
## Concordance= 0.567 (se = 0.031 )
## Rsquare= 0.056 (max possible= 0.997 )
## Likelihood ratio test= 7.35 on 2 df, p=0.02539
## Wald test = 6.96 on 2 df, p=0.03086
## Score (logrank) test = 7.22 on 2 df, p=0.0271
Death or Hospitalization by STS (yes, no)
## Call: survfit(formula = Surv(time2death, death.or.hosp) ~ trt, data = dat)
##
## n events median 0.95LCL 0.95UCL
## trt=TRUE 67 45 0.893 0.529 1.74
## trt=FALSE 61 53 1.122 0.490 2.36
## Call: survfit(formula = Surv(time2death, death.or.hosp) ~ trt, data = dat)
##
## trt=TRUE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 67 0 1.0000 0.0000 1.0000 1.000
## 0.5 36 23 0.6259 0.0622 0.5151 0.761
## 1.0 22 9 0.4622 0.0660 0.3494 0.611
## 2.0 9 7 0.2943 0.0666 0.1888 0.459
## 3.0 4 3 0.1766 0.0667 0.0842 0.370
## 4.0 2 2 0.0883 0.0553 0.0258 0.302
##
## trt=FALSE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 61 0 1.000 0.0000 1.0000 1.000
## 0.5 34 23 0.609 0.0639 0.4953 0.748
## 1.0 28 6 0.501 0.0660 0.3872 0.649
## 2.0 20 7 0.375 0.0644 0.2679 0.525
## 3.0 13 7 0.244 0.0579 0.1531 0.388
## 4.0 10 3 0.188 0.0529 0.1079 0.326
## 5.0 9 1 0.169 0.0508 0.0936 0.304
## Call:
## coxph(formula = Surv(time2death, death.or.hosp) ~ trt, data = dat)
##
## n= 128, number of events= 98
##
## coef exp(coef) se(coef) z Pr(>|z|)
## trtFALSE -0.1534 0.8578 0.2171 -0.706 0.48
##
## exp(coef) exp(-coef) lower .95 upper .95
## trtFALSE 0.8578 1.166 0.5605 1.313
##
## Concordance= 0.484 (se = 0.029 )
## Rsquare= 0.004 (max possible= 0.997 )
## Likelihood ratio test= 0.5 on 1 df, p=0.48
## Wald test = 0.5 on 1 df, p=0.4799
## Score (logrank) test = 0.5 on 1 df, p=0.4796
Number of hospitalization by STS (high, low, none)
## Analysis of Variance Table
##
## Response: n.hospital
## Df Sum Sq Mean Sq F value Pr(>F)
## sts.order 2 3.546 1.7731 1.1385 0.3236
## Residuals 125 194.672 1.5574
##
## Kruskal-Wallis rank sum test
##
## data: n.hospital by sts.order
## Kruskal-Wallis chi-squared = 0.84187, df = 2, p-value = 0.6564
Number of hospitalization by STS (yes, no)
## Analysis of Variance Table
##
## Response: n.hospital
## Df Sum Sq Mean Sq F value Pr(>F)
## trt 1 1.541 1.5415 0.9875 0.3222
## Residuals 126 196.677 1.5609
##
## Kruskal-Wallis rank sum test
##
## data: n.hospital by trt
## Kruskal-Wallis chi-squared = 0.63666, df = 1, p-value = 0.4249
Time dependent covariate
## Call: survfit(formula = Surv(tstart, tstop, death) ~ treatment, data = tdata)
##
## treatment=0
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 0 0 1.000 0.0000 1.000 1.000
## 0.5 35 23 0.634 0.0621 0.523 0.768
## 1.0 29 6 0.525 0.0654 0.411 0.670
## 2.0 20 6 0.415 0.0654 0.305 0.565
## 3.0 13 2 0.371 0.0655 0.262 0.524
## 4.0 10 2 0.309 0.0676 0.201 0.474
## 5.0 9 0 0.309 0.0676 0.201 0.474
##
## treatment=1
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 0 0 1.000 0.0000 1.0000 1.000
## 0.5 35 21 0.646 0.0625 0.5346 0.781
## 1.0 21 8 0.493 0.0674 0.3776 0.645
## 2.0 9 4 0.378 0.0742 0.2575 0.555
## 3.0 4 3 0.227 0.0819 0.1119 0.460
## 4.0 2 1 0.170 0.0786 0.0688 0.421
## Call:
## coxph(formula = Surv(tstart, tstop, death) ~ treatment, data = tdata)
##
## n= 155, number of events= 82
##
## coef exp(coef) se(coef) z Pr(>|z|)
## treatment 0.1669 1.1817 0.2348 0.711 0.477
##
## exp(coef) exp(-coef) lower .95 upper .95
## treatment 1.182 0.8463 0.7459 1.872
##
## Concordance= 0.488 (se = 0.031 )
## Rsquare= 0.003 (max possible= 0.986 )
## Likelihood ratio test= 0.51 on 1 df, p=0.4772
## Wald test = 0.51 on 1 df, p=0.4771
## Score (logrank) test = 0.51 on 1 df, p=0.4767
Survival by early/late treatment
## Call: survfit(formula = Surv(time2death, death) ~ group, data = dat1,
## start.time = 7/365.25)
##
## n events median 0.95LCL 0.95UCL
## group=early 40 22 1.049 0.529 NA
## group=late 27 16 0.969 0.400 NA
## Call: survfit(formula = Surv(time2death, death) ~ group, data = dat1,
## start.time = 7/365.25)
##
## group=early
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.5 22 12 0.670 0.0786 0.5326 0.843
## 1.0 13 5 0.518 0.0853 0.3750 0.715
## 2.0 5 2 0.410 0.0981 0.2563 0.655
## 3.0 2 2 0.205 0.1136 0.0691 0.607
##
## group=late
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.5 14 9 0.634 0.0984 0.468 0.859
## 1.0 9 3 0.489 0.1062 0.320 0.748
## 2.0 4 2 0.380 0.1068 0.219 0.660
## 3.0 2 1 0.285 0.1149 0.130 0.628
## 4.0 2 0 0.285 0.1149 0.130 0.628
## Call:
## coxph(formula = Surv(time2death - 7/365.25, death) ~ group, data = dat1)
##
## n= 67, number of events= 38
##
## coef exp(coef) se(coef) z Pr(>|z|)
## grouplate -0.01549 0.98463 0.33600 -0.046 0.963
##
## exp(coef) exp(-coef) lower .95 upper .95
## grouplate 0.9846 1.016 0.5096 1.902
##
## Concordance= 0.49 (se = 0.046 )
## Rsquare= 0 (max possible= 0.979 )
## Likelihood ratio test= 0 on 1 df, p=0.9632
## Wald test = 0 on 1 df, p=0.9632
## Score (logrank) test = 0 on 1 df, p=0.9632