commit 7b94e4bf64f3328fb6aaa0e61c4e2a8a9c8e2afe Author: Louis Chih-Ming Lee Date: Sat May 9 18:49:31 2026 +0200 git init diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ed2f617 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +*_cache/ +*_files/ diff --git a/Assignment_1.pdf b/Assignment_1.pdf new file mode 100644 index 0000000..71e9758 Binary files /dev/null and b/Assignment_1.pdf differ diff --git a/report.Rmd b/report.Rmd new file mode 100644 index 0000000..8d1c83f --- /dev/null +++ b/report.Rmd @@ -0,0 +1,244 @@ +--- +title: foo +execute: + cache: true + freeze: auto +number-sections: true +--- + +```{r} +library(tidyverse) +library(survival) +# library(gtsummary) +``` + +```{r} +dat <- read.csv("./unos.txt", sep = "\t") +head(dat) +``` + +```{r} +names(dat) <- c("hla.match", "age.donor", "age.rec", "cold.isc", "death", + "year", "sex", "tx.type", "follow.up") +``` + + + +# Exercise + +## Exercise 1 + +> Illustrate in a table the characteristics of the population (age, sex, race, +donor, . . . ). + +```{r} +g <- ggplot(dat) +``` + +```{r} +g + geom_point(aes(x = follow.up, y = death)) +g + geom_density(aes(x = follow.up)) + +g + geom_density(aes(x = age.donor)) +g + geom_bar(aes(x = age.rec)) + +g + geom_boxplot(aes(x = age.rec, y = age.donor, group = age.donor)) + +# dat$age.1 |> table() +``` + +## Exercise 2 + +Plot the Kaplan-Meier overall survival curve for pediatric kid- +ney transplant recipients for the first 12 years after transplantation. + +```{r} +km <- survfit(Surv(follow.up, death) ~ 1, data = dat[dat$follow.up <= 12, ]) +plot(km) + + + +``` + +## Exercise 3 + +We are going to compare mortality rates (hazard functions) +between children whose transplanted kidney was provided by a living donor +(in general a family member) and those whose source was recently deceased +(variable donor type: `txtype`). Use the life table method to calculate the death +rates for the first 5 years for each group (take in the first year intervals of 4 +months and then look at each year) and show the results in a table. Estimate +the hazard ratio in each time interval as the ratio between the mortality rates +in the two groups. What do you notice? + +```{r} +dat.5 <- dat[dat$follow.up <= 5, ] +head(dat.5) +classify_time_interval = function(fu) { + if (fu <= 1/3) { + return(1/3) + } else if (fu <= 2/3) { + return(2/3) + } else if (fu <= 1) { + return(1) + } + ceiling(fu) +} + +dat.5$fu.interval <- sapply(dat.5$follow.up, classify_time_interval) +table(dat.5$fu.interval) +head(dat.5) +``` + +```{r} +dat.5.life <- dat.5 |> + group_by(fu.interval) |> + summarize( + n.censored = sum(death == 0), + n.event = sum(death), + n.at.risk = nrow(dat), + ) + +for (i in 2:nrow(dat.5.life)) { + j <- i - 1 + + n.censored.pre <- dat.5.life$n.censored[j] + n.event.pre <- dat.5.life$n.event[j] + n.at.risk.pre <- dat.5.life$n.at.risk[j] + + n.at.risk <- n.at.risk.pre - n.event.pre - n.censored.pre + + dat.5.life$n.at.risk[i] <- n.at.risk +} + +print(nrow(dat)) +dat.5.life + +``` + +```{r} +dat.5.life <- dat.5.life |> + mutate( + hazard.rate = n.event / n.at.risk + ) + +``` + +--- + + +```{r} +get_life_table = function(dat) { + dat <- dat |> + group_by(fu.interval) |> + summarize( + n.censored = sum(death == 0), + n.event = sum(death), + n.at.risk = nrow(dat), + ) + + for (i in 2:nrow(dat)) { + j <- i - 1 + + n.censored.pre <- dat$n.censored[j] + n.event.pre <- dat$n.event[j] + n.at.risk.pre <- dat$n.at.risk[j] + + n.at.risk <- n.at.risk.pre - n.event.pre - n.censored.pre + + dat$n.at.risk[i] <- n.at.risk + } + + dat <- dat |> + mutate( + hazard.rate = n.event / n.at.risk + ) + + return(dat) +} +``` + +```{r} +dat.5.tx0 = dat.5[dat.5$tx.type == 0, ] +dat.5.tx1 = dat.5[dat.5$tx.type == 1, ] +``` + +```{r} +tx0.life <- get_life_table(dat.5.tx0) +tx0.life +``` + +```{r} +tx1.life <- get_life_table(dat.5.tx1) +tx1.life +``` + + +```{r} +tx1.life$hazard.rate / tx0.life$hazard.rate +``` + +```{r} +hazard.df <- data.frame( + fu.interval = tx1.life$fu.interval, + hazard.rate.0 = tx0.life$hazard.rate, + hazard.rate.1 = tx1.life$hazard.rate, + hazard.ratio = tx1.life$hazard.rate / tx0.life$hazard.rate +) + +ggplot(hazard.df, aes(x = fu.interval)) + + geom_line(aes(y = hazard.rate.0), color = "blue") + + geom_line(aes(y = hazard.rate.1), color = "orange") + +ggplot(hazard.df, aes(x = fu.interval)) + + geom_line(aes(y = hazard.ratio)) + +``` + + + +## Exercise 4 + +Show a plot with Kaplan-Meier survival curves for the two donor types. + +```{r} +km.tx <- survfit(Surv(follow.up, death) ~ tx.type, data = dat[dat$follow.up <= 12, ]) + +plot(km.tx, col = c("blue", "orange")) +legend(legend = c("cadaveric", "living"), "bottomleft", lwd = 2, col = c("blue", "orange")) +``` + +## Exercise 5 + +Fit a univariate Cox model with predictor donor type. Report +the hazard ratio and 95% confidence interval and interpret the result obtained. + +```{r} +cox <- coxph(Surv(follow.up, death) ~ tx.type, data = dat) +summary(cox) +``` + +```{r} +1.90539 +``` + +```{r} +1.90539 + 1.96 * exp(0.09558) +(2.298 - 1.58) / 2 |> exp() / 2 +``` + +Exercise 6 — Research shows that an important determinant of mortality +after kidney transplant is the age of the recipient. Fit a Cox model with age +as predictor and estimate the hazard ratio and its confidence interval. Consider +age first as continuous variable and then divide into categories. + +Exercise 7 — Fit a multivariate Cox model by using other predictors and +describe your results. + +Exercise 8 — Estimate the survival function for specific covariate patterns. +Based on the previous results choose the best predictors. + +Exercise 9 — Check the proportional hazards assumption. You may use the +function cox.zph. Discuss the result and possible implications. +Exercise 10 — Plot the Schoenfeld residuals and comment. + diff --git a/report.html b/report.html new file mode 100644 index 0000000..4c9fd4b --- /dev/null +++ b/report.html @@ -0,0 +1,891 @@ + + + + + + + + + +foo + + + + + + + + + + + + + + + + + + + + +
+ +
+ +
+
+

foo

+
+ + + +
+ + + + +
+ + + +
+ + +
+
library(tidyverse)
+
+
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
+✔ dplyr     1.1.4     ✔ readr     2.1.5
+✔ forcats   1.0.0     ✔ stringr   1.5.1
+✔ lubridate 1.9.4     ✔ tibble    3.3.0
+✔ purrr     1.1.0     ✔ tidyr     1.3.1
+── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
+✖ dplyr::filter() masks stats::filter()
+✖ dplyr::lag()    masks stats::lag()
+ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
+
+
library(survival)
+# library(gtsummary)
+
+
+
dat <- read.csv("./unos.txt", sep = "\t")
+head(dat)
+
+
  hlamat age age.1 cold_isc death year sex txtype fu
+1      2  25    15        6     0 2002   0      1  0
+2      1  42    10       11     1 1999   0      1  0
+3      1   9    14        8     0 2002   1      1  0
+4      0  35    12       26     0 2002   1      1  0
+5      2  17    12       28     1 1997   1      1  0
+6      3  44    13        1     0 2002   1      0  0
+
+
+
+
names(dat) <- c("hla.match", "age.donor", "age.rec", "cold.isc", "death",
+                "year", "sex", "tx.type", "follow.up")
+
+
+

1 Exercise

+
+

1.1 Exercise 1

+
+

Illustrate in a table the characteristics of the population (age, sex, race, donor, . . . ).

+
+
+
g <- ggplot(dat)
+
+
+
g + geom_point(aes(x = follow.up, y = death))
+
+
+
+

+
+
+
+
g +  geom_density(aes(x = follow.up))
+
+
+
+

+
+
+
+
g +  geom_density(aes(x = age.donor))
+
+
Warning: Removed 113 rows containing non-finite outside the scale range
+(`stat_density()`).
+
+
+
+
+

+
+
+
+
g +  geom_bar(aes(x = age.rec))
+
+
Warning: Removed 9 rows containing non-finite outside the scale range
+(`stat_count()`).
+
+
+
+
+

+
+
+
+
g + geom_boxplot(aes(x = age.rec, y = age.donor, group = age.donor))
+
+
Warning: Removed 113 rows containing missing values or values outside the scale range
+(`stat_boxplot()`).
+
+
+
Warning: Removed 7 rows containing non-finite outside the scale range
+(`stat_boxplot()`).
+
+
+
+
+

+
+
+
+
# dat$age.1 |> table()
+
+
+
+

1.2 Exercise 2

+

Plot the Kaplan-Meier overall survival curve for pediatric kid- ney transplant recipients for the first 12 years after transplantation.

+
+
km <- survfit(Surv(follow.up, death) ~ 1, data = dat[dat$follow.up  <= 12, ])
+plot(km)
+
+
+
+

+
+
+
+
+
+
+

1.3 Exercise 3

+

We are going to compare mortality rates (hazard functions) between children whose transplanted kidney was provided by a living donor (in general a family member) and those whose source was recently deceased (variable donor type: txtype). Use the life table method to calculate the death rates for the first 5 years for each group (take in the first year intervals of 4 months and then look at each year) and show the results in a table. Estimate the hazard ratio in each time interval as the ratio between the mortality rates in the two groups. What do you notice?

+
+
dat.5 <- dat[dat$follow.up <= 5, ]
+head(dat.5)
+
+
  hla.match age.donor age.rec cold.isc death year sex tx.type follow.up
+1         2        25      15        6     0 2002   0       1         0
+2         1        42      10       11     1 1999   0       1         0
+3         1         9      14        8     0 2002   1       1         0
+4         0        35      12       26     0 2002   1       1         0
+5         2        17      12       28     1 1997   1       1         0
+6         3        44      13        1     0 2002   1       0         0
+
+
classify_time_interval = function(fu) {
+  if (fu <= 1/3) {
+    return(1/3)
+  } else if (fu <= 2/3) {
+    return(2/3)
+  } else if (fu <= 1) {
+    return(1)
+  }
+  ceiling(fu)
+}
+
+dat.5$fu.interval <- sapply(dat.5$follow.up, classify_time_interval)
+table(dat.5$fu.interval)
+
+

+0.333333333333333 0.666666666666667                 1                 2 
+              982               528               640              1226 
+                3                 4                 5 
+             1184              1093               950
+
+
head(dat.5)
+
+
  hla.match age.donor age.rec cold.isc death year sex tx.type follow.up
+1         2        25      15        6     0 2002   0       1         0
+2         1        42      10       11     1 1999   0       1         0
+3         1         9      14        8     0 2002   1       1         0
+4         0        35      12       26     0 2002   1       1         0
+5         2        17      12       28     1 1997   1       1         0
+6         3        44      13        1     0 2002   1       0         0
+  fu.interval
+1   0.3333333
+2   0.3333333
+3   0.3333333
+4   0.3333333
+5   0.3333333
+6   0.3333333
+
+
+
+
dat.5.life <- dat.5 |>
+  group_by(fu.interval) |>
+  summarize(
+    n.censored = sum(death == 0),
+    n.event = sum(death),
+    n.at.risk = nrow(dat),
+  )
+
+for (i in 2:nrow(dat.5.life)) {
+  j <- i - 1
+
+  n.censored.pre <- dat.5.life$n.censored[j]
+  n.event.pre <- dat.5.life$n.event[j]
+  n.at.risk.pre <- dat.5.life$n.at.risk[j]
+
+  n.at.risk <- n.at.risk.pre - n.event.pre - n.censored.pre
+
+  dat.5.life$n.at.risk[i] <- n.at.risk
+}
+
+print(nrow(dat))
+
+
[1] 9775
+
+
dat.5.life
+
+
# A tibble: 7 × 4
+  fu.interval n.censored n.event n.at.risk
+        <dbl>      <int>   <int>     <int>
+1       0.333        830     152      9775
+2       0.667        491      37      8793
+3       1            613      27      8265
+4       2           1164      62      7625
+5       3           1143      41      6399
+6       4           1053      40      5215
+7       5            919      31      4122
+
+
+
+
dat.5.life <- dat.5.life |>
+  mutate(
+     hazard.rate = n.event / n.at.risk
+  )
+
+
+
+
get_life_table = function(dat) {
+  dat <- dat |>
+    group_by(fu.interval) |>
+    summarize(
+      n.censored = sum(death == 0),
+      n.event = sum(death),
+      n.at.risk = nrow(dat),
+    )
+
+  for (i in 2:nrow(dat)) {
+    j <- i - 1
+
+    n.censored.pre <- dat$n.censored[j]
+    n.event.pre <- dat$n.event[j]
+    n.at.risk.pre <- dat$n.at.risk[j]
+
+    n.at.risk <- n.at.risk.pre - n.event.pre - n.censored.pre
+
+    dat$n.at.risk[i] <- n.at.risk
+  }
+
+  dat <- dat |>
+    mutate(
+       hazard.rate = n.event / n.at.risk
+    )
+
+  return(dat)
+}
+
+
+
dat.5.tx0 = dat.5[dat.5$tx.type == 0, ]
+dat.5.tx1 = dat.5[dat.5$tx.type == 1, ]
+
+
+
tx0.life <- get_life_table(dat.5.tx0)
+tx0.life
+
+
# A tibble: 7 × 5
+  fu.interval n.censored n.event n.at.risk hazard.rate
+        <dbl>      <int>   <int>     <int>       <dbl>
+1       0.333        463      49      3378     0.0145 
+2       0.667        253      13      2866     0.00454
+3       1            340      11      2600     0.00423
+4       2            591      24      2249     0.0107 
+5       3            568      17      1634     0.0104 
+6       4            521      16      1049     0.0153 
+7       5            501      11       512     0.0215
+
+
+
+
tx1.life <- get_life_table(dat.5.tx1)
+tx1.life
+
+
# A tibble: 7 × 5
+  fu.interval n.censored n.event n.at.risk hazard.rate
+        <dbl>      <int>   <int>     <int>       <dbl>
+1       0.333        367     103      3225     0.0319 
+2       0.667        238      24      2755     0.00871
+3       1            273      16      2493     0.00642
+4       2            573      38      2204     0.0172 
+5       3            575      24      1593     0.0151 
+6       4            532      24       994     0.0241 
+7       5            418      20       438     0.0457
+
+
+
+
tx1.life$hazard.rate / tx0.life$hazard.rate
+
+
[1] 2.201766 1.920536 1.516975 1.615661 1.448100 1.582998 2.125363
+
+
+
+
hazard.df <- data.frame(
+  fu.interval = tx1.life$fu.interval,
+  hazard.rate.0 = tx0.life$hazard.rate,
+  hazard.rate.1 = tx1.life$hazard.rate,
+  hazard.ratio = tx1.life$hazard.rate / tx0.life$hazard.rate
+) 
+
+ggplot(hazard.df, aes(x = fu.interval)) +
+  geom_line(aes(y = hazard.rate.0), color = "blue") + 
+  geom_line(aes(y = hazard.rate.1), color = "orange")
+
+
+
+

+
+
+
+
ggplot(hazard.df, aes(x = fu.interval)) +
+  geom_line(aes(y = hazard.ratio))
+
+
+
+

+
+
+
+
+
+
+

1.4 Exercise 4

+

Show a plot with Kaplan-Meier survival curves for the two donor types.

+
+
km.tx <- survfit(Surv(follow.up, death) ~ tx.type, data = dat[dat$follow.up  <= 12, ])
+
+plot(km.tx, col = c("blue", "orange"))
+legend(legend = c("cadaveric", "living"), "bottomleft", lwd = 2, col = c("blue", "orange"))
+
+
+
+

+
+
+
+
+
+
+

1.5 Exercise 5

+

Fit a univariate Cox model with predictor donor type. Report the hazard ratio and 95% confidence interval and interpret the result obtained.

+
+
cox <- coxph(Surv(follow.up, death) ~ tx.type, data = dat)
+summary(cox)
+
+
Call:
+coxph(formula = Surv(follow.up, death) ~ tx.type, data = dat)
+
+  n= 9775, number of events= 465 
+
+           coef exp(coef) se(coef)     z Pr(>|z|)    
+tx.type 0.64469   1.90539  0.09558 6.745 1.53e-11 ***
+---
+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+        exp(coef) exp(-coef) lower .95 upper .95
+tx.type     1.905     0.5248      1.58     2.298
+
+Concordance= 0.586  (se = 0.012 )
+Likelihood ratio test= 47.1  on 1 df,   p=7e-12
+Wald test            = 45.5  on 1 df,   p=2e-11
+Score (logrank) test = 47.09  on 1 df,   p=7e-12
+
+
+
+
1.90539
+
+
[1] 1.90539
+
+
+
+
1.90539 + 1.96 * exp(0.09558)
+
+
[1] 4.061972
+
+
(2.298 - 1.58) / 2 |> exp() / 2
+
+
[1] 0.04858537
+
+
+

Exercise 6 — Research shows that an important determinant of mortality after kidney transplant is the age of the recipient. Fit a Cox model with age as predictor and estimate the hazard ratio and its confidence interval. Consider age first as continuous variable and then divide into categories.

+

Exercise 7 — Fit a multivariate Cox model by using other predictors and describe your results.

+

Exercise 8 — Estimate the survival function for specific covariate patterns. Based on the previous results choose the best predictors.

+

Exercise 9 — Check the proportional hazards assumption. You may use the function cox.zph. Discuss the result and possible implications. Exercise 10 — Plot the Schoenfeld residuals and comment.

+
+
+ +
+ + +
+ + + + + \ No newline at end of file diff --git a/unos.txt b/unos.txt new file mode 100644 index 0000000..c210346 --- /dev/null +++ b/unos.txt @@ -0,0 +1,9776 @@ +"hlamat" "age" "age.1" "cold_isc" "death" "year" "sex" "txtype" "fu" +2 25 15 6 0 2002 0 1 0 +1 42 10 11 1 1999 0 1 0 +1 9 14 8 0 2002 1 1 0 +0 35 12 26 0 2002 1 1 0 +2 17 12 28 1 1997 1 1 0 +3 44 13 1 0 2002 1 0 0 +1 34 12 1 0 2002 1 0 0 +4 38 11 2 0 2002 0 0 0 +0 23 10 6 0 2002 0 1 0 +3 26 1 1 0 2002 1 0 0 +NA 42 18 NA 0 2002 1 0 0 +4 39 9 NA 0 2002 0 0 0 +3 34 13 NA 0 2002 1 0 0 +3 NA 14 2 0 2002 0 0 0 +3 40 17 1 0 2002 0 0 0 +3 22 14 2 0 2002 0 0 0 +0 17 17 12 0 2002 1 1 0 +3 NA 7 NA 0 2002 1 0 0 +0 4 0 NA 1 2001 0 1 0 +4 NA NA 1 0 2002 0 0 0 +3 42 18 1 0 2002 1 0 0 +1 19 15 21 1 1994 1 1 0 +3 44 10 NA 0 2002 1 0 0 +0 13 14 NA 0 2001 1 1 0 +1 NA 6 1 0 2002 1 0 0 +3 37 12 0 0 1995 1 0 6.002739906 +NA NA 8 1 0 2002 1 0 0.043835618 +5 28 16 19 0 1996 0 1 3.147945166 +2 30 17 27 0 1993 1 1 4.863013744 +0 37 11 1 0 2001 0 0 1 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15 NA 0 1998 1 1 3.002739668 +2 10 6 12 0 1997 1 1 4.665753365 +4 41 17 1 0 1991 1 0 2.991780758 +2 48 17 30 0 1990 1 1 3.03287673 +3 45 16 1 0 2000 1 0 0.01369863 +0 20 5 9 0 2001 1 1 0.871232867 +1 43 18 NA 0 1998 0 0 3.002739668 +3 51 11 0 0 1995 1 0 4.235616207 +4 29 1 2 0 1993 1 0 1.347945213 +2 24 17 NA 1 1997 1 0 2.169862986 +2 20 17 11 0 1997 1 1 2.443835735 +0 6 14 16 0 1990 1 1 0.317808211 +4 6 17 NA 0 2001 1 1 0.438356161 +3 36 17 0 0 1996 0 0 3.391780853 +3 33 15 0 0 1991 0 0 5.539726257 +3 40 13 1 1 1995 0 0 5.676712513 +2 22 10 NA 0 1999 1 1 2.605479479 +2 51 16 30 0 1995 1 1 3.739726067 +4 40 16 0 0 1994 1 0 7.002739906 +0 46 16 10 0 1995 1 1 2.205479383 +3 20 12 2 0 1993 1 0 8.506849289 +3 53 18 28 0 1991 0 1 7.761643887 +0 49 17 10 0 1994 1 1 3.827397346 +NA 40 15 NA 0 2002 0 0 0.010958904 +2 35 2 19 1 1997 1 1 2.695890427 +2 20 18 36 0 1993 1 1 2.019178152 +4 45 18 1 0 2002 0 0 0.383561641 +4 32 7 NA 0 2001 1 0 0.997260273 +2 48 6 14 0 1998 0 1 3.91232872 +3 31 18 1 0 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