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DataCamp Survival Analysis in R SURVIVAL ANALYSIS IN R Why use the Weibull model? Heidi Seibold Statistician at LMU Munich DataCamp Survival Analysis in R DataCamp Survival Analysis in R Computing a Weibull model in R wbmod <-

  1. DataCamp Survival Analysis in R SURVIVAL ANALYSIS IN R Why use the Weibull model? Heidi Seibold Statistician at LMU Munich

  2. DataCamp Survival Analysis in R

  3. DataCamp Survival Analysis in R Computing a Weibull model in R wbmod <- survreg(Surv(time, cens) ~ horTh + tsize, data = GBSG2) coef(wbmod) #> (Intercept) horThyes tsize #> 7.96069769 0.31175602 -0.01218073

  4. DataCamp Survival Analysis in R SURVIVAL ANALYSIS IN R Let's practice!

  5. DataCamp Survival Analysis in R SURVIVAL ANALYSIS IN R Visualising Weibull models Heidi Seibold Statistician at LMU Munich

  6. DataCamp Survival Analysis in R

  7. DataCamp Survival Analysis in R Steps to produce visualization Compute Weibull model Decide on "imaginary patients" Compute survival curves Create data.frame with survival curve information Plot

  8. DataCamp Survival Analysis in R Step 1 Compute Weibull model wbmod <- survreg(Surv(time, cens) ~ horTh + tsize, data = GBSG2) Decide on Decide on covariate combinations ("imaginary patients") newdat <- expand.grid( horTh = levels(GBSG2$horTh), tsize = quantile(GBSG2$tsize, probs = c(0.25, 0.5, 0.75)) ) newdat #> horTh tsize #> 1 no 20 #> 2 yes 20 #> 3 no 25 #> 4 yes 25 #> 5 no 35 #> 6 yes 35

  9. DataCamp Survival Analysis in R Step 2 Compute survival curves surv <- seq(.99, .01, by = -.01) t <- predict(wbmod, type = "quantile", p = 1 - surv, newdata = newdat) dim(t) #> [1] 6 99 t[, 1:7] #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 65.86524 112.54061 154.2116 193.0603 230.0268 265.6298 300.1952 #> [2,] 89.96016 153.71037 210.6256 263.6858 314.1755 362.8029 410.0131 #> [3,] 61.97352 105.89102 145.0999 181.6531 216.4354 249.9348 282.4579 #> [4,] 84.64477 144.62823 198.1805 248.1057 295.6121 341.3663 385.7870 #> [5,] 54.86634 93.74733 128.4597 160.8209 191.6144 221.2720 250.0653 #> [6,] 74.93762 128.04211 175.4530 219.6526 261.7110 302.2180 341.5445

  10. DataCamp Survival Analysis in R Step 3 Create data.frame with survival curve information surv_wbmod_wide <- cbind(newdat, t) library("reshape2") surv_wbmod <- melt(surv_wbmod_wide, id.vars = c("horTh", "tsize"), variable.name = "surv_id", value.name = "time") surv_wbmod$surv <- surv[as.numeric(surv_wbmod$surv_id)] surv_wbmod[, c("upper", "lower", "std.err", "strata")] <- NA

  11. DataCamp Survival Analysis in R Step 3 str(surv_wbmod) #> 'data.frame': 594 obs. of 9 variables: #> $ horTh : Factor w/ 2 levels "no","yes": 1 2 1 2 1 2 1 2 1 2 ... #> $ tsize : num 20 20 25 25 35 35 20 20 25 25 ... #> $ surv_id: Factor w/ 99 levels "1","2","3","4",..: 1 1 1 1 1 1 2 2 2 2 ... #> $ time : num 65.9 90 62 84.6 54.9 ... #> $ surv : num 0.99 0.99 0.99 0.99 0.99 0.99 0.98 0.98 0.98 0.98 ... #> $ strata : logi NA NA NA NA NA NA ... #> $ std.err: logi NA NA NA NA NA NA ... #> $ lower : logi NA NA NA NA NA NA ... #> $ upper : logi NA NA NA NA NA NA ...

  12. DataCamp Survival Analysis in R If this was to fast... library("reshape2") surv_wbmod <- melt(surv_wbmod_wide, id.vars = names(newdat), variable.name = "surv_id", value.name = "time") ?melt

  13. DataCamp Survival Analysis in R Step 4 Plot ggsurvplot_df(surv_wbmod, surv.geom = geom_line, linetype = "horTh", color = "tsize", legend.title = NULL)

  14. DataCamp Survival Analysis in R SURVIVAL ANALYSIS IN R You can do it too! All you need is practice.

  15. DataCamp Survival Analysis in R SURVIVAL ANALYSIS IN R Other distributions than Weibull Heidi Seibold Statistician at LMU Munich

  16. DataCamp Survival Analysis in R

  17. DataCamp Survival Analysis in R survreg() options survreg(Surv(time, cens) ~ horTh, data = GBSG2) survreg(Surv(time, cens) ~ horTh, data = GBSG2, dist = "exponential") survreg(Surv(time, cens) ~ horTh, data = GBSG2, dist = "lognormal") More info: ?survreg

  18. DataCamp Survival Analysis in R SURVIVAL ANALYSIS IN R Let's try working with different models

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