Análisis de supervivencia

http://rstudio-pubs-static.s3.amazonaws.com/316989_83cbe556125645b698c9ff6cf88c4c1a.html

https://www.datacamp.com/community/tutorials/survival-analysis-R?utm_source=adwords_ppc&utm_campaignid=898687156&utm_adgroupid=48947256715&utm_device=c&utm_keyword=&utm_matchtype=b&utm_network=g&utm_adpostion=&utm_creative=229765585183&utm_targetid=aud-299261629574:dsa-429603003980&utm_loc_interest_ms=&utm_loc_physical_ms=20300&gclid=Cj0KCQjw3duCBhCAARIsAJeFyPWoFRIksx859UbKOfsktGos52j5-v27sbxEsd43eCf-X1JAmuh2Wx4aAnokEALw_wcB

surv_object <- Surv(time = Datos$tsuper, event = Datos$exitus)

fit1 <- survfit(surv_object ~ 1, data = Datos)

fit1.km <- survfit(surv_object ~ 1, data = Datos, type = "kaplan-meier")
fit1.km
## Call: survfit(formula = surv_object ~ 1, data = Datos, type = "kaplan-meier")
## 
##       n  events  median 0.95LCL 0.95UCL 
##      63      23     190      70      NA
summary(fit1)
## Call: survfit(formula = surv_object ~ 1, data = Datos)
## 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##     1     63       1    0.984  0.0157       0.9537        1.000
##     3     62       1    0.968  0.0221       0.9259        1.000
##     5     61       1    0.952  0.0268       0.9012        1.000
##     8     59       2    0.920  0.0343       0.8553        0.990
##     9     57       1    0.904  0.0373       0.8337        0.980
##    11     55       1    0.888  0.0401       0.8124        0.970
##    12     54       1    0.871  0.0426       0.7915        0.959
##    20     47       1    0.853  0.0455       0.7678        0.947
##    23     43       1    0.833  0.0486       0.7427        0.934
##    30     40       1    0.812  0.0516       0.7168        0.920
##    34     38       1    0.791  0.0545       0.6906        0.905
##    35     37       1    0.769  0.0571       0.6651        0.890
##    37     35       1    0.747  0.0595       0.6392        0.873
##    41     33       1    0.725  0.0619       0.6129        0.857
##    47     29       1    0.700  0.0646       0.5838        0.838
##    52     26       1    0.673  0.0675       0.5526        0.819
##    58     22       1    0.642  0.0710       0.5170        0.797
##    70     19       1    0.608  0.0749       0.4779        0.774
##    86     18       1    0.574  0.0780       0.4403        0.750
##   101     15       1    0.536  0.0816       0.3979        0.723
##   190      5       1    0.429  0.1160       0.2524        0.729
##   205      2       1    0.214  0.1624       0.0486        0.946
ggsurvplot(fit1, data = Datos, pval = FALSE, conf.int = T, title = "Curva de Supervivencia", xlab = "Tiempo", ylab = "Probabilidad de supervivencia", legend.title = "Estimación", legend.labs = "Kaplan-Meier")