Survival by STS (high, low, none)
## Call: survfit(formula = Surv(last.fu.since.cua, death) ~ sts.order,
## data = dat)
##
## n events median 0.95LCL 0.95UCL
## sts.order=high 47 30 0.567 0.400 1.16
## sts.order=low 19 8 2.256 1.864 NA
## sts.order=none 62 44 1.122 0.504 3.47
## Call: survfit(formula = Surv(last.fu.since.cua, death) ~ sts.order,
## data = dat)
##
## sts.order=high
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 47 0 1.000 0.0000 1.0000 1.000
## 0.5 22 18 0.582 0.0759 0.4512 0.752
## 1.0 11 7 0.388 0.0792 0.2604 0.579
## 2.0 3 3 0.278 0.0785 0.1599 0.483
## 3.0 1 1 0.139 0.1058 0.0313 0.618
## 4.0 1 0 0.139 0.1058 0.0313 0.618
##
## 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 62 0 1.000 0.0000 1.000 1.000
## 0.5 35 23 0.615 0.0632 0.503 0.752
## 1.0 29 6 0.510 0.0654 0.396 0.655
## 2.0 20 6 0.400 0.0649 0.291 0.550
## 3.0 13 2 0.358 0.0646 0.251 0.510
## 4.0 10 2 0.298 0.0662 0.193 0.461
## 5.0 9 0 0.298 0.0662 0.193 0.461
## Call:
## coxph(formula = Surv(last.fu.since.cua, death) ~ sts.order, data = dat)
##
## n= 128, number of events= 82
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sts.orderlow -0.8527 0.4263 0.4004 -2.130 0.0332 *
## sts.ordernone -0.3733 0.6884 0.2538 -1.471 0.1412
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sts.orderlow 0.4263 2.346 0.1945 0.9343
## sts.ordernone 0.6884 1.453 0.4187 1.1321
##
## Concordance= 0.563 (se = 0.033 )
## Rsquare= 0.042 (max possible= 0.994 )
## Likelihood ratio test= 5.46 on 2 df, p=0.06515
## Wald test = 5.12 on 2 df, p=0.07715
## Score (logrank) test = 5.3 on 2 df, p=0.07071
Survival by STS (yes, no)
## Call: survfit(formula = Surv(last.fu.since.cua, death) ~ trt, data = dat)
##
## n events median 0.95LCL 0.95UCL
## trt=TRUE 66 38 0.969 0.553 2.26
## trt=FALSE 62 44 1.122 0.504 3.47
## Call: survfit(formula = Surv(last.fu.since.cua, death) ~ trt, data = dat)
##
## trt=TRUE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 66 0 1.000 0.0000 1.0000 1.000
## 0.5 35 21 0.651 0.0621 0.5396 0.785
## 1.0 21 8 0.497 0.0675 0.3808 0.648
## 2.0 8 4 0.381 0.0745 0.2596 0.559
## 3.0 3 3 0.204 0.0862 0.0891 0.467
## 4.0 2 1 0.136 0.0799 0.0430 0.430
##
## trt=FALSE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 62 0 1.000 0.0000 1.000 1.000
## 0.5 35 23 0.615 0.0632 0.503 0.752
## 1.0 29 6 0.510 0.0654 0.396 0.655
## 2.0 20 6 0.400 0.0649 0.291 0.550
## 3.0 13 2 0.358 0.0646 0.251 0.510
## 4.0 10 2 0.298 0.0662 0.193 0.461
## 5.0 9 0 0.298 0.0662 0.193 0.461
## Call:
## coxph(formula = Surv(last.fu.since.cua, death) ~ trt, data = dat)
##
## n= 128, number of events= 82
##
## coef exp(coef) se(coef) z Pr(>|z|)
## trtFALSE -0.1121 0.8939 0.2354 -0.476 0.634
##
## exp(coef) exp(-coef) lower .95 upper .95
## trtFALSE 0.8939 1.119 0.5635 1.418
##
## Concordance= 0.476 (se = 0.031 )
## Rsquare= 0.002 (max possible= 0.994 )
## Likelihood ratio test= 0.23 on 1 df, p=0.6338
## Wald test = 0.23 on 1 df, p=0.6339
## Score (logrank) test = 0.23 on 1 df, p=0.6337
Death or Hospitalization by STS (high, low, none)
## Call: survfit(formula = Surv(last.fu.since.cua, death.or.hospital) ~
## sts.order, data = dat)
##
## n events median 0.95LCL 0.95UCL
## sts.order=high 47 35 0.553 0.356 1.01
## sts.order=low 19 9 2.152 1.292 NA
## sts.order=none 62 53 1.122 0.504 2.36
## Call: survfit(formula = Surv(last.fu.since.cua, death.or.hospital) ~
## sts.order, data = dat)
##
## sts.order=high
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 47 0 1.0000 0.0000 1.0000 1.000
## 0.5 22 20 0.5386 0.0764 0.4079 0.711
## 1.0 11 8 0.3314 0.0749 0.2128 0.516
## 2.0 3 5 0.1582 0.0657 0.0701 0.357
## 3.0 1 1 0.0791 0.0649 0.0159 0.395
## 4.0 1 0 0.0791 0.0649 0.0159 0.395
##
## 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 62 0 1.000 0.0000 1.0000 1.000
## 0.5 35 23 0.615 0.0632 0.5030 0.752
## 1.0 29 6 0.510 0.0654 0.3964 0.655
## 2.0 20 7 0.383 0.0644 0.2755 0.532
## 3.0 13 7 0.249 0.0585 0.1571 0.395
## 4.0 10 3 0.191 0.0536 0.1107 0.331
## 5.0 9 1 0.172 0.0515 0.0959 0.310
## Call:
## coxph(formula = Surv(last.fu.since.cua, death.or.hospital) ~
## sts.order, data = dat)
##
## n= 128, number of events= 97
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sts.orderlow -0.9572 0.3840 0.3766 -2.542 0.0110 *
## sts.ordernone -0.4901 0.6126 0.2372 -2.066 0.0388 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sts.orderlow 0.3840 2.604 0.1836 0.8033
## sts.ordernone 0.6126 1.632 0.3848 0.9751
##
## Concordance= 0.573 (se = 0.031 )
## Rsquare= 0.063 (max possible= 0.997 )
## Likelihood ratio test= 8.33 on 2 df, p=0.0155
## Wald test = 8.02 on 2 df, p=0.01816
## Score (logrank) test = 8.34 on 2 df, p=0.01544
Death or Hospitalization by STS (yes, no)
## Call: survfit(formula = Surv(last.fu.since.cua, death.or.hospital) ~
## trt, data = dat)
##
## n events median 0.95LCL 0.95UCL
## trt=TRUE 66 44 0.758 0.529 1.69
## trt=FALSE 62 53 1.122 0.504 2.36
## Call: survfit(formula = Surv(last.fu.since.cua, death.or.hospital) ~
## trt, data = dat)
##
## trt=TRUE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 66 0 1.0000 0.0000 1.0000 1.000
## 0.5 35 23 0.6196 0.0630 0.5077 0.756
## 1.0 21 9 0.4526 0.0665 0.3393 0.604
## 2.0 8 7 0.2787 0.0668 0.1742 0.446
## 3.0 3 3 0.1493 0.0664 0.0624 0.357
## 4.0 2 1 0.0995 0.0601 0.0305 0.325
##
## trt=FALSE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 62 0 1.000 0.0000 1.0000 1.000
## 0.5 35 23 0.615 0.0632 0.5030 0.752
## 1.0 29 6 0.510 0.0654 0.3964 0.655
## 2.0 20 7 0.383 0.0644 0.2755 0.532
## 3.0 13 7 0.249 0.0585 0.1571 0.395
## 4.0 10 3 0.191 0.0536 0.1107 0.331
## 5.0 9 1 0.172 0.0515 0.0959 0.310
## Call:
## coxph(formula = Surv(last.fu.since.cua, death.or.hospital) ~
## trt, data = dat)
##
## n= 128, number of events= 97
##
## coef exp(coef) se(coef) z Pr(>|z|)
## trtFALSE -0.1887 0.8280 0.2192 -0.861 0.389
##
## exp(coef) exp(-coef) lower .95 upper .95
## trtFALSE 0.828 1.208 0.5388 1.272
##
## Concordance= 0.489 (se = 0.029 )
## Rsquare= 0.006 (max possible= 0.997 )
## Likelihood ratio test= 0.74 on 1 df, p=0.3896
## Wald test = 0.74 on 1 df, p=0.3893
## Score (logrank) test = 0.74 on 1 df, p=0.3888
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 4.021 2.0103 1.303 0.2754
## Residuals 125 192.854 1.5428
##
## Kruskal-Wallis rank sum test
##
## data: n.hospital by sts.order
## Kruskal-Wallis chi-squared = 1.0892, df = 2, p-value = 0.5801
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 1.673 1.6726 1.0797 0.3008
## Residuals 126 195.202 1.5492
##
## Kruskal-Wallis rank sum test
##
## data: n.hospital by trt
## Kruskal-Wallis chi-squared = 0.75135, df = 1, p-value = 0.386