## dat$trt
##  best  none other 
##    19     8   101

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 (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

Survival by Sensipar (yes, no)

## Call: survfit(formula = Surv(time2death, death) ~ drug.sensipar, data = dat)
## 
##                      n events median 0.95LCL 0.95UCL
## drug.sensipar=FALSE 90     56  0.969   0.553    2.26
## drug.sensipar=TRUE  38     26  1.013   0.518    3.88

## Call: survfit(formula = Surv(time2death, death) ~ drug.sensipar, data = dat)
## 
##                 drug.sensipar=FALSE 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##   0.0     90       0    1.000  0.0000        1.000        1.000
##   0.5     48      31    0.632  0.0530        0.536        0.745
##   1.0     34      10    0.497  0.0564        0.398        0.621
##   2.0     21       6    0.398  0.0582        0.299        0.530
##   3.0     12       3    0.334  0.0595        0.236        0.474
##   4.0      8       2    0.272  0.0628        0.173        0.428
##   5.0      6       0    0.272  0.0628        0.173        0.428
## 
##                 drug.sensipar=TRUE 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##   0.0     38       0    1.000  0.0000       1.0000        1.000
##   0.5     22      13    0.643  0.0797       0.5046        0.820
##   1.0     16       4    0.526  0.0839       0.3850        0.719
##   2.0      8       4    0.390  0.0857       0.2532        0.600
##   3.0      5       2    0.292  0.0877       0.1623        0.526
##   4.0      4       1    0.234  0.0875       0.1123        0.487
##   5.0      3       1    0.175  0.0829       0.0694        0.443
## Call:
## coxph(formula = Surv(time2death, death) ~ drug.sensipar, data = dat)
## 
##   n= 128, number of events= 82 
## 
##                      coef exp(coef) se(coef)     z Pr(>|z|)
## drug.sensiparTRUE 0.03048   1.03095  0.23790 0.128    0.898
## 
##                   exp(coef) exp(-coef) lower .95 upper .95
## drug.sensiparTRUE     1.031       0.97    0.6468     1.643
## 
## Concordance= 0.499  (se = 0.029 )
## Rsquare= 0   (max possible= 0.994 )
## Likelihood ratio test= 0.02  on 1 df,   p=0.8982
## Wald test            = 0.02  on 1 df,   p=0.8981
## Score (logrank) test = 0.02  on 1 df,   p=0.898

Death or Hospitalization by Sensipar (yes, no)

## Call: survfit(formula = Surv(time2death, death.or.hosp) ~ drug.sensipar, 
##     data = dat)
## 
##                      n events median 0.95LCL 0.95UCL
## drug.sensipar=FALSE 90     68  0.821   0.529    1.86
## drug.sensipar=TRUE  38     30  0.893   0.518    2.72

## Call: survfit(formula = Surv(time2death, death.or.hosp) ~ drug.sensipar, 
##     data = dat)
## 
##                 drug.sensipar=FALSE 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##   0.0     90       0    1.000  0.0000       1.0000        1.000
##   0.5     48      33    0.610  0.0535       0.5132        0.724
##   1.0     34      10    0.480  0.0558       0.3820        0.602
##   2.0     21       9    0.345  0.0555       0.2513        0.472
##   3.0     12       7    0.220  0.0518       0.1384        0.349
##   4.0      8       4    0.146  0.0457       0.0795        0.270
##   5.0      6       1    0.128  0.0435       0.0659        0.249
## 
##                 drug.sensipar=TRUE 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##   0.0     38       0    1.000  0.0000       1.0000        1.000
##   0.5     22      13    0.643  0.0797       0.5046        0.820
##   1.0     16       5    0.495  0.0845       0.3545        0.692
##   2.0      8       5    0.330  0.0829       0.2017        0.540
##   3.0      5       3    0.206  0.0767       0.0996        0.427
##   4.0      4       1    0.165  0.0716       0.0705        0.386
##   5.0      3       1    0.124  0.0645       0.0446        0.344
## Call:
## coxph(formula = Surv(time2death, death.or.hosp) ~ drug.sensipar, 
##     data = dat)
## 
##   n= 128, number of events= 98 
## 
##                        coef exp(coef)  se(coef)      z Pr(>|z|)
## drug.sensiparTRUE -0.009363  0.990681  0.219704 -0.043    0.966
## 
##                   exp(coef) exp(-coef) lower .95 upper .95
## drug.sensiparTRUE    0.9907      1.009    0.6441     1.524
## 
## Concordance= 0.503  (se = 0.027 )
## Rsquare= 0   (max possible= 0.997 )
## Likelihood ratio test= 0  on 1 df,   p=0.966
## Wald test            = 0  on 1 df,   p=0.966
## Score (logrank) test = 0  on 1 df,   p=0.966

Survival by Warfrin (yes, no)

## Call: survfit(formula = Surv(time2death, death) ~ drug.warfrin, data = dat)
## 
##                      n events median 0.95LCL 0.95UCL
## drug.warfrin=FALSE 110     66  1.177   0.567    3.36
## drug.warfrin=TRUE   18     16  0.665   0.400    2.15

## Call: survfit(formula = Surv(time2death, death) ~ drug.warfrin, data = dat)
## 
##                 drug.warfrin=FALSE 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##   0.0    110       0    1.000  0.0000        1.000        1.000
##   0.5     61      36    0.652  0.0472        0.566        0.751
##   1.0     44      11    0.533  0.0504        0.443        0.641
##   2.0     26       7    0.436  0.0532        0.343        0.554
##   3.0     17       3    0.379  0.0556        0.284        0.505
##   4.0     12       3    0.306  0.0589        0.209        0.446
##   5.0      9       1    0.275  0.0604        0.179        0.423
## 
##                 drug.warfrin=TRUE 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##   0.0     18       0    1.000  0.0000        1.000        1.000
##   0.5      9       8    0.542  0.1201        0.351        0.836
##   1.0      6       3    0.361  0.1168        0.192        0.681
##   2.0      3       3    0.181  0.0941        0.065        0.501
## Call:
## coxph(formula = Surv(time2death, death) ~ drug.warfrin, data = dat)
## 
##   n= 128, number of events= 82 
## 
##                    coef exp(coef) se(coef)    z Pr(>|z|)  
## drug.warfrinTRUE 0.6141    1.8480   0.2844 2.16   0.0308 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                  exp(coef) exp(-coef) lower .95 upper .95
## drug.warfrinTRUE     1.848     0.5411     1.058     3.227
## 
## Concordance= 0.525  (se = 0.022 )
## Rsquare= 0.032   (max possible= 0.994 )
## Likelihood ratio test= 4.14  on 1 df,   p=0.04179
## Wald test            = 4.66  on 1 df,   p=0.0308
## Score (logrank) test = 4.81  on 1 df,   p=0.02827

Death or Hospitalization by Warfrin (yes, no)

## Call: survfit(formula = Surv(time2death, death.or.hosp) ~ drug.warfrin, 
##     data = dat)
## 
##                      n events median 0.95LCL 0.95UCL
## drug.warfrin=FALSE 110     80  1.122   0.553    1.88
## drug.warfrin=TRUE   18     18  0.579   0.268    1.62

## Call: survfit(formula = Surv(time2death, death.or.hosp) ~ drug.warfrin, 
##     data = dat)
## 
##                 drug.warfrin=FALSE 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##   0.0    110       0    1.000  0.0000       1.0000        1.000
##   0.5     61      37    0.641  0.0476       0.5546        0.742
##   1.0     44      12    0.513  0.0505       0.4229        0.622
##   2.0     26      11    0.374  0.0515       0.2856        0.490
##   3.0     17       7    0.266  0.0503       0.1835        0.385
##   4.0     12       5    0.188  0.0461       0.1160        0.304
##   5.0      9       2    0.155  0.0435       0.0893        0.269
## 
##                 drug.warfrin=TRUE 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##   0.0     18       0    1.000  0.0000       1.0000        1.000
##   0.5      9       9    0.500  0.1179       0.3150        0.794
##   1.0      6       3    0.333  0.1111       0.1734        0.641
##   2.0      3       3    0.167  0.0878       0.0593        0.468
## Call:
## coxph(formula = Surv(time2death, death.or.hosp) ~ drug.warfrin, 
##     data = dat)
## 
##   n= 128, number of events= 98 
## 
##                    coef exp(coef) se(coef)     z Pr(>|z|)  
## drug.warfrinTRUE 0.6375    1.8918   0.2677 2.381   0.0172 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##                  exp(coef) exp(-coef) lower .95 upper .95
## drug.warfrinTRUE     1.892     0.5286     1.119     3.197
## 
## Concordance= 0.528  (se = 0.021 )
## Rsquare= 0.038   (max possible= 0.997 )
## Likelihood ratio test= 5.01  on 1 df,   p=0.02521
## Wald test            = 5.67  on 1 df,   p=0.01725
## Score (logrank) test = 5.86  on 1 df,   p=0.01546