update

.gitignore

@@ -1,2 +1 @@
 *_cache/
-*_files/

slides.Rmd

@@ -1,12 +1,11 @@
 ---
 title: "Slides"
 execute:
-  cache: true
-  freeze: auto
   include: true
   echo: false
   warning: false
 number-sections: true
+toc: true
 ---
 
 ```{r}
@@ -33,7 +32,7 @@ dat <- dat |>
   mutate(
     sex = factor(sex, levels = c(0,1), labels = c("Female","Male")),
     tx.type = factor(tx.type, levels = c(0,1), labels = c("Cadaveric","Living")),
-    # hla.match = factor(hla.match),
+    hla.match = factor(hla.match),
     # year = factor(year)
 )
 ```
@@ -50,30 +49,26 @@ my.theme <- theme_minimal() +
   )
 ```
 
-::: {.callout-warning}
+```{r}
-There are 25 repondents with `follow.up = 0`, 21 of which with `death = 0` and
+my.tab <- function(x) {
-other 4 with `death = 1`.
+  x |>
-
+  tab_options(
-```{r, echo=TRUE}
+    row.striping.background_color = "#F6E8C3",
-dat[dat$follow.up == 0, ] |>
+    table.border.top.color = "#A83262",
-  group_by(death) |>
+    table.border.bottom.color = "#A83262",
-  summarise(n = n())
+    column_labels.border.bottom.color = "#A83262",
+    table.font.size = px(16),
+    heading.title.font.size = px(18),
+    table.background.color = "#FFF4CC"
+  )
+}
 ```
 
-Reasons to omit them, that is, have `dat <- dat[dat$follow.up > 0,]` at the 
-very beginning:
-
-- If `death = 1`, transplant failed. They shouldn't be included in the research
-  question "survival rate of a *successful* tranplant".
-- If `death = 0`, transplant was successful, but they were immediately removed
-  from populatoin at risk, contributiong nothing to the analysis.
-:::
-
 # Title 
 
-# Table of Contents (?)
+# Table of Contents
 
-# Introduction (M)
+# Introduction
 
 # Method & Result
 
@@ -81,8 +76,9 @@ very beginning:
 
 - Data source
 - Study population
-```{r}
+```{r, echo=TRUE}
 nrow(dat)
+range(dat$year)
 ```
 - Covariates
 - Outcome
@@ -104,16 +100,42 @@ dat |>
       hla.match ~ "HLA matches, n(%)",
       age.donor ~ "Donor age, median (IQR)",
       age.rec ~ "Recipient age, median (IQR)",
-      cold.isc ~ "Cold ischemic time (hours), median (IQR), ",
+      cold.isc ~ "Cold ischemic time (hours), median (IQR)",
       sex ~ "Sex, n(%)"
     ),
     missing = "ifany"
   ) |>
   add_overall() |>
-  modify_footnote(all_stat_cols() ~ NA)
+  modify_footnote(all_stat_cols() ~ NA) |>
+  as_gt() |>
+  tab_style(
+    style = list(
+      cell_fill(color = "#A83262"),
+      cell_text(color = "white", weight = "bold")
+    ),
+    locations = cells_column_labels()
+  ) |>
+  tab_style(
+    style = list(
+      cell_fill(color = "#F8EFCB"),
+      cell_text(color = "#2F2F2F")
+    ),
+    locations = cells_body()
+  ) |>
+  opt_row_striping(row_striping = TRUE) |>
+  tab_options(
+    table.background.color = "#F8EFCB",
+    row.striping.background_color = "#EFE6D0",
+    table.border.top.color = "#A83262",
+    table.border.bottom.color = "#A83262",
+    column_labels.border.top.color = "#A83262",
+    column_labels.border.bottom.color = "#A83262",
+    table.font.size = px(18),
+    column_labels.font.weight = "bold"
+  )
 ```
 
-# Elementary Analysis: Transplant Type (L)
+# Table 1: By Transplant Type
 
 ## Transplant Type vs Sex
 
@@ -190,16 +212,25 @@ plot.tx.rec <- ggplot(data = dat) +
 plot.tx.rec
 ```
 
+```{r}
+dat[dat$tx.type == "Cadaveric", ]$age.rec |> table()
+dat[dat$tx.type == "Living", ]$age.rec |> table()
+```
 
-# Elementary Analysis: Transplant Type (Cont.)
+
+# Table 1: By Transplant Type
 
 ## Transplant Type vs HLA Match
 
 Living donors have less `hla.match` then cadaveric donors.
 
 ```{r, fig.width=7, fig.height=5}
-ggplot(data = dat) +
+dat |>
-  geom_bar(aes(x = hla.match)) +
+  mutate(
+    tx.type = recode(tx.type, "Cadaveric" = "Deceased")
+  ) |>
+  ggplot() +
+  geom_bar(aes(x = hla.match, fill = tx.type)) +
   facet_wrap(tx.type ~ ., ncol = 1) +
   labs(
     title = "Distribution of HLA Match by Transplant Type",
@@ -207,7 +238,21 @@ ggplot(data = dat) +
     y = "Count",
     color = NULL
   ) +
-  my.theme
+  scale_fill_manual(
+    values = c(
+      "Deceased" = "darkorange",
+      "Living" = "darkgreen"
+    ),
+    labels = c(
+      "Deceased" = "Deceased",
+      "Living" = "Living"
+    )
+  ) +
+  my.theme +
+  theme(
+    legend.position = "none",
+    strip.text = element_text(size = 12)
+  )
 
 ```
 
@@ -242,7 +287,7 @@ plot.tx.donor <- ggplot(data = dat) +
 plot.tx.donor
 ```
 
-# Overall Kaplan-Meier Curve
+# Overall Survival
 
 ```{r}
 km.all <- survfit(Surv(follow.up, death) ~ 1, data = dat)
@@ -271,8 +316,8 @@ border_col  <- "#D8C8A5"
 ```
 
 ```{r}
-times <- seq(0, 12, 2)
+km.all.summary <- summary(km.all, times = seq(0, 12, 2))
-summary(km.all, times = times)[c("time", "n.risk", "surv")] |>
+km.all.summary[c("time", "n.risk", "surv", "lower", "upper")] |> 
   as.data.frame() |>
   gt() |>
   tab_header(
@@ -281,33 +326,13 @@ summary(km.all, times = times)[c("time", "n.risk", "surv")] |>
   cols_label(
     time = "Follow-up Time",
     n.risk = "Number at Risk",
-    surv = "Survival Probability"
+    surv = "Survival Probability",
-  ) |>
+    lower = "Upper 95% CI",
-  fmt_number(
+    upper = "Lower 95% CI",
-    columns = surv,
-    decimals = 3
-  ) |>
-  tab_options(
-    table.font.size = px(15),
-    heading.title.font.size = px(20),
-    column_labels.font.weight = "bold"
-  )
-
-```
-
-```{r}
-summary(km.all, times = times)[c("time", "n.risk", "surv")] |>
-  as.data.frame() |>
-  gt() |>
-  tab_header(
-    title = "Overall Kaplan-Meier Survival Summary"
-  ) |>
-  cols_label(
-    time = "Follow-up Time",
-    n.risk = "Number at Risk",
-    surv = "Survival Probability"
   ) |>
   fmt_number(columns = surv, decimals = 3) |>
+  fmt_number(columns = lower, decimals = 3) |>
+  fmt_number(columns = upper, decimals = 3) |>
   fmt_number(columns = time, decimals = 0) |>
   tab_style(
     style = list(
@@ -321,18 +346,234 @@ summary(km.all, times = times)[c("time", "n.risk", "surv")] |>
     locations = cells_body()
   ) |>
   opt_row_striping(row_striping = TRUE) |>
-  tab_options(
+  my.tab()
-    row.striping.background_color = "#F6E8C3",
+```
-    table.border.top.color = "#A83262",
+
-    table.border.bottom.color = "#A83262",
+# Hazard by Transplant Type
-    column_labels.border.bottom.color = "#A83262",
+
-    table.font.size = px(16),
+## Death distribution
-    heading.title.font.size = px(18),
+
-    table.background.color = "#FFF4CC"
+
+```{r}
+dat[dat$death == 1, ] |>
+  mutate(
+    first.1 = follow.up <= 1,
+    first.5 = follow.up <= 5,
+  ) |>
+  select(first.1, first.5) |>
+  summarize(
+    total = n(),
+    first.1.total = sum(first.1),
+    first.1.ratio = first.1.total / total * 100,
+    first.5.total = sum(first.5),
+    first.5.ratio = first.5.total / total * 100,
   )
 ```
 
-# Cox Model (with intervals?)
+```{r}
+dat[dat$death == 1, ] |>
+  ggplot(aes(x = follow.up)) +
+  geom_histogram(bins = 50) +
+  labs(
+    title = "Distribution of Occurence of Death",
+    x = "Years of Follow-up",
+    y = "Count"
+  ) +
+  my.theme
+```
+
+```{r}
+n.pop <- nrow(dat)
+dat |>
+  select(follow.up, death) |>
+  mutate(censored = ifelse(death == 1, 0, 1)) |>
+  group_by(follow.up) |>
+  summarize(
+    death = sum(death),
+    censored = sum(censored),
+    .groups = "drop"
+  ) |>
+  arrange(follow.up) |>
+  mutate(
+    death.accum = cumsum(death),
+    death.accum.ratio = death.accum / sum(dat$death),
+  ) |>
+  ggplot() +
+  geom_step(aes(y = death.accum.ratio, x = follow.up)) +
+  labs(
+    title = "Cumulative Proportion of Deaths Over Follow-up",
+    x = "Years of Follow-up",
+    y = "Cumulative Proportion of Deaths"
+  ) +
+  my.theme
+```
+
+# Hazard Rate by Transplant Type
+
+```{r}
+get.life.table <- function(dat, time.intervals) {
+  n.pop <- nrow(dat)
+
+  dat |>
+    recode.dat(time.intervals) |>
+    group_by(fu.interval) |>
+    summarize(
+      n.censored = sum(.data$death == 0),
+      n.event = sum(.data$death),
+    ) |>
+    ungroup() |>
+    calculate.hazard(n.pop)
+}
+
+get.life.table.by.groups <- function(dat, time.intervals, grps) {
+  grps |>
+    lapply(function(grp) {
+      dat |>
+        get.life.table.by.group(time.intervals, grp) |>
+        mutate(
+          grp.name = grp,
+          grp.value = pick(1)[[1]]
+        ) |>
+        select(-1)
+    }) |>
+    bind_rows()
+}
+
+get.life.table.by.group <- function(dat, time.intervals, grp) {
+  dat |>
+    recode.dat(time.intervals) |>
+    group_by(fu.interval, .data[[grp]]) |>
+    summarize(
+      n.censored = sum(.data$death == 0),
+      n.event = sum(.data$death),
+      .groups = "keep"
+    ) |>
+    ungroup(fu.interval) |>
+    group_modify(function(df.sub, grp) {
+      grp.name <- names(grp)
+      grp.value <- grp[[1]]
+      n.pop <- (dat[[grp.name]] == grp.value) |> sum()
+      calculate.hazard(df.sub, n.pop)
+    }) |>
+    ungroup()
+}
+
+calculate.hazard <- function(life.table, n.pop) {
+  n.removed <- life.table$n.event + life.table$n.censored
+  n.removed.accum <- c(0, cumsum(n.removed)[-length(n.removed)])
+  life.table |>
+    mutate(
+      n.at.risk = n.pop - n.removed.accum,
+      # TODO: how to account for censored? How do we adjust for uneven interval?
+      hazard.rate = n.event / n.at.risk
+    )
+}
+
+recode.dat <- function(dat, time.intervals) {
+  df <- dat[dat$follow.up <= sum(time.intervals), ]
+  time.points <- cumsum(time.intervals)
+  df$fu.interval <- sapply(df$follow.up, function(time) {
+      time.points[time <= time.points][1]
+  })
+
+  df
+}
+
+plot.hazard.rate.by.groups <- function(dat, time.intervals, grps) {
+  dat |>
+    get.life.table.by.groups(time.intervals, grps) |>
+    ggplot(aes(x = fu.interval, y = hazard.rate, color = grp.value)) +
+    geom_point() +
+    geom_line() +
+    scale_color_discrete(
+      labels = c(
+        "Cadaveric" = "Deceased",
+        "Living" = "Living"
+      )
+    ) +
+    scale_x_continuous(
+      breaks = function(x) seq(floor(x[1]), ceiling(x[2]), by = 1)
+    ) + 
+    labs(
+      title = "Hazard Rate by Transplant Type",
+      x = "Follow-up Year",
+      y = "Estimated Hazard Rate",
+      color = "Transplant Type"
+    ) +
+    my.theme
+}
+```
+
+```{r, fig.width=7, fig.height=5}
+time.intervals = c(rep(1/3, 3), rep(1, 4))
+plot.hazard.rate.by.groups(dat, time.intervals, "tx.type")
+```
+
+```{r}
+get.life.table.by.group(dat, time.intervals, "tx.type") |>
+  select(fu.interval, tx.type, hazard.rate) |>
+  pivot_wider(
+    names_from = tx.type,
+    values_from = hazard.rate
+  ) |>
+  mutate(
+    fu.interval = case_when(
+      abs(fu.interval - 1/3) < 0.001 ~ "1/3",
+      abs(fu.interval - 2/3) < 0.001 ~ "2/3",
+      fu.interval %% 1 == 0 ~ as.character(as.integer(fu.interval)),
+      TRUE ~ as.character(fu.interval)
+    ),
+    Deceased = round(Cadaveric, 3),
+    Living = round(Living, 3)
+  ) |>
+  select(fu.interval, Deceased, Living) |>
+  gt() |>
+  tab_header(
+    title = "Estimated Hazard Rate by Transplant Type"
+  ) |>
+  cols_label(
+    fu.interval = "Follow-up Interval"
+  ) |>
+  cols_align(
+    align = "center",
+    columns = everything()
+  ) |>
+  tab_style(
+    style = list(
+      cell_fill(color = "#5B5FE0"),
+      cell_text(color = "white", weight = "bold")
+    ),
+    locations = cells_column_labels()
+  ) |>
+  tab_style(
+    style = list(
+      cell_fill(color = "#FFF2CC"),
+      cell_text(color = "#2F2F2F")
+    ),
+    locations = cells_body()
+  ) |>
+  tab_style(
+    style = cell_text(
+      color = "#5B5FE0",
+      weight = "bold",
+      size = px(22)
+    ),
+    locations = cells_title(groups = "title")
+  ) |>
+  tab_options(
+    table.background.color = "#FFF2CC",
+    row.striping.background_color = "#F6E7B8",
+    table.border.top.color = "#5B5FE0",
+    table.border.bottom.color = "#5B5FE0",
+    column_labels.border.bottom.color = "#5B5FE0",
+    heading.background.color = "#FFF2CC",
+    heading.title.font.size = px(22),
+    table.font.size = px(18),
+    column_labels.font.weight = "bold"
+  ) |>
+  opt_row_striping()
+```
+
 
 # Cox Model: Transplant Type
 
@@ -356,11 +597,11 @@ km.tx |>
     labels = c("Deceased", "Living")
   ) +
   labs(
-    title = "Survival by Donor Type",
+    title = "Survival by Transplant Type",
     x = "Years of Follow-up",
     y = "Survival Probability",
-    color = "Donor Type",
+    color = "Transplant Type",
-    fill = "Donor Type"
+    fill = "Transplant Type"
   ) +
   my.theme
 ```
@@ -452,6 +693,37 @@ km.age.rec.group <- survfit(Surv(follow.up, death) ~ age.rec.group,
                             data = dat.age.rec.group)
 ```
 
+
+```{r, fig.width=7, fig.height=7}
+eme.age.rec.group$contrast |> 
+  as.data.frame() |>
+  select(contrast, ratio, SE, p.value) |>
+  mutate(
+    contrast = factor(contrast),
+    log.ratio = log(ratio),
+    SE.log = SE / ratio,
+    low = exp(log.ratio - 1.96 * SE.log),
+    up = exp(log.ratio + 1.96 * SE.log)
+  ) |>
+  ggplot(aes(y = reorder(contrast, ratio))) +
+  geom_errorbarh(aes(xmin = low, xmax = up), height = 0.2) +
+  geom_point(aes(x = ratio)) +
+  geom_vline(xintercept =  1, linetype = "dashed") +
+  scale_y_discrete(
+    labels = function(x) {
+      gsub("[()]", "", x = x)
+    }
+  ) +
+  labs(
+    x = "Hazard Ratio",
+    y = "Recipient Age Group Comparison",
+    title = "Pairwise Hazard Ratios by Recipient Age Groups"
+  ) +
+  my.theme
+```
+
+# Cox Model: Age Recipient (Categorical)
+
 ```{r, fig.width=7, fig.height=7}
 km.age.rec.group |>
   ggsurvfit(type = "survival") +
@@ -472,34 +744,6 @@ km.age.rec.group |>
   my.theme
 ```
 
-```{r, fig.width=7, fig.height=7}
-eme.age.rec.group$contrast |> 
-  as.data.frame() |>
-  select(contrast, ratio, SE, p.value) |>
-  mutate(
-    contrast = factor(contrast),
-    log.ratio = log(ratio),
-    SE.log = SE / ratio,
-    low = exp(log.ratio - 1.96 * SE.log),
-    up = exp(log.ratio + 1.96 * SE.log)
-  ) |>
-  ggplot(aes(y = reorder(contrast, ratio))) +
-  geom_errorbarh(aes(xmin = low, xmax = up)) +
-  geom_point(aes(x = ratio)) +
-  geom_vline(xintercept =  1, linetype = "dashed") +
-  scale_y_discrete(
-    labels = function(x) {
-      gsub("[()]", "", x = x)
-    }
-  ) +
-  labs(
-    x = "Hazard Ratio",
-    y = "Recipient Age Group Comparison",
-    title = "Pairwise Hazard Ratios by Recipient Age Groups"
-  ) +
-  my.theme
-```
-
 # Identifying Predictors
 
 ```{r}
@@ -528,25 +772,24 @@ test.new.var <- function(old.vars, new.var, i=1) {
 ```{r, echo=TRUE}
 old.vars <- c()
 test.new.var(old.vars, "tx.type", 1)
-test.new.var(old.vars, "hla.match", 1)
-test.new.var(old.vars, "age.donor", 1)
 test.new.var(old.vars, "age.rec", 1)
-test.new.var(old.vars, "cold.isc", 1)
+test.new.var(old.vars, "age.donor", 1)
-test.new.var(old.vars, "sex", 1)
 test.new.var(old.vars, "year", 1)
+test.new.var(old.vars, "hla.match", 1)
+test.new.var(old.vars, "sex", 1)
+test.new.var(old.vars, "cold.isc", 1)
 ```
 ## `tx.type` + ?
 
 ```{r, echo=TRUE}
 old.vars <- c("tx.type")
 
-# test.new.var(old.vars, "hla.match", 2:7)
-test.new.var(old.vars, "hla.match", 2)
-test.new.var(old.vars, "age.donor", 2)
 test.new.var(old.vars, "age.rec", 2)
-test.new.var(old.vars, "cold.isc", 2)
+test.new.var(old.vars, "age.donor", 2)
-test.new.var(old.vars, "sex", 2)
 test.new.var(old.vars, "year", 2)
+test.new.var(old.vars, "hla.match", 2:7)
+test.new.var(old.vars, "sex", 2)
+test.new.var(old.vars, "cold.isc", 2)
 ```
 
 `age.rec` has the smallest $p$-value
@@ -556,47 +799,47 @@ test.new.var(old.vars, "year", 2)
 ```{r, echo=TRUE}
 old.vars <- c("tx.type", "age.rec")
 
-test.new.var(old.vars, "hla.match", 3)
 test.new.var(old.vars, "age.donor", 3)
-test.new.var(old.vars, "cold.isc", 3)
-test.new.var(old.vars, "sex", 3)
 test.new.var(old.vars, "year", 3)
-```
+test.new.var(old.vars, "hla.match", 3:8)
-
+test.new.var(old.vars, "sex", 3)
-`hla.match` has the smallest $p$-value
+test.new.var(old.vars, "cold.isc", 3)
-
-## `tx.type` + `age.rec` + `hla.match` + ?
-
-```{r, echo=TRUE}
-old.vars <- c("tx.type", "age.rec", "hla.match")
-
-test.new.var(old.vars, "age.donor", 4)
-test.new.var(old.vars, "cold.isc", 4)
-test.new.var(old.vars, "sex", 4)
-test.new.var(old.vars, "year", 4)
 ```
 
 `age.donor` has the smallest $p$-value
 
-## `tx.type` + `age.rec` + `hla.match` + `age.donor` + ? 
+## `tx.type` + `age.rec` + `age.donor` + ?
 
 ```{r, echo=TRUE}
-old.vars <- c("tx.type", "age.rec", "hla.match", "age.donor")
+old.vars <- c("tx.type", "age.rec", "age.donor")
 
-test.new.var(old.vars, "cold.isc", 5)
+test.new.var(old.vars, "year", 4)
-test.new.var(old.vars, "sex", 5)
+test.new.var(old.vars, "hla.match", 4:9)
-test.new.var(old.vars, "year", 5)
+test.new.var(old.vars, "sex", 4)
+test.new.var(old.vars, "cold.isc", 4)
 ```
 
 `year` has the smallest $p$-value
 
-## `tx.type` + `age.rec` + `hla.match` + `age.donor` + `year` + ?
+## `tx.type` + `age.rec` + `age.donor` + `year` + ?
 
 ```{r, echo=TRUE}
-old.vars <- c("tx.type", "age.rec", "hla.match", "age.donor", "year")
+old.vars <- c("tx.type", "age.rec", "age.donor", "year")
 
-test.new.var(old.vars, "cold.isc", 6)
+test.new.var(old.vars, "hla.match", 5:10)
-test.new.var(old.vars, "sex", 6)
+test.new.var(old.vars, "sex", 5)
+test.new.var(old.vars, "cold.isc", 5)
+```
+
+`hla.match` has the smallest $p$-value
+
+## `tx.type` + `age.rec` + `age.donor` + `year` +  `hla.match` + ?
+
+```{r, echo=TRUE}
+old.vars <- c("tx.type", "age.rec", "age.donor", "year", "hla.match")
+
+test.new.var(old.vars, "sex", 11)
+test.new.var(old.vars, "cold.isc", 11)
 ```
 
 Neither has $p$-value less than 0.05. Stop.
@@ -604,16 +847,14 @@ Neither has $p$-value less than 0.05. Stop.
 # Full Model
 
 ```{r}
-surv.full <- Surv(follow.up + death) ~ tx.type + age.rec + hla.match +
+surv.full <- Surv(follow.up, death) ~ tx.type + age.rec + age.donor + year +
-                 age.donor + year
+                  hla.match
 cox.full <- coxph(surv.full, data = dat, method = "breslow")
-```
+summary(cox.full)
-
-```{r, echo=TRUE}
-cox.full
 ```
 
 ```{r, fig.width=6, fig.height=6}
+print(1)
 tidy(cox.full, exponentiate = TRUE, conf.int = TRUE) |>
   mutate(
     term = recode(
@@ -641,14 +882,60 @@ tidy(cox.full, exponentiate = TRUE, conf.int = TRUE) |>
   my.theme
 ```
 
-## What happened to `year`?
+
+
+
+# Proportional hazards assumption
 
 ```{r}
-ggplot(data = dat) +
+cox.full
-  geom_boxplot(aes(x = factor(year), y = follow.up))
+cox.zph(cox.full)
 ```
 
-```{r}
+```{r, fig.width=15, fig.height=10}
+ph_plots <- ggcoxzph(
+  cox.zph(cox.full), ggtheme = theme_minimal(), point.size = 5, size = 30
+)
+
+var.names <- c("Transplant Type", "Recipient Age", "Donor Age", "Year", 
+                "HLA Match")
+
+my_ph_theme <- theme(
+  legend.position = "bottom",
+  plot.title = element_text(size = 40, face = "bold"),
+  axis.title = element_text(size = 35),
+  axis.text = element_text(size = 30),
+  legend.text = element_text(size = 20)
+)
+
+for (i in seq_along(ph_plots)) {
+  title <- paste0("Scaled Schoenfeld Residuals: ", var.names[i])
+  single_plot <- ph_plots[[i]] + my_ph_theme + labs(title = title)
+  print(single_plot)
+}
+```
+
+
+
+
+
+
+## What happened to `year`?
+
+```{r, fig.width=10, fig.height=7}
+ggplot(data = dat) +
+  geom_boxplot(aes(x = factor(year), y = follow.up)) +
+  my.theme
+```
+
+```{r, fig.width=10, fig.height=7}
+ggplot(data = dat) +
+  geom_boxplot(aes(x = factor(year), y = follow.up, color = factor(death))) +
+  my.theme
+```
+
+
+```{r, fig.width=7, fig.height=5}
 ggplot(data = dat) +
   geom_boxplot(aes(x = factor(year), y = follow.up)) +
   facet_grid(death ~ .)
@@ -659,8 +946,8 @@ Probabily want to omit it
 ## Without `year`
 
 ```{r}
-surv.full.1 <- Surv(follow.up + death) ~ tx.type + age.rec + hla.match +
+surv.full.1 <- Surv(follow.up, death) ~ tx.type + age.rec + hla.match +
-                 age.donor
+                    age.donor
 cox.full.1 <- coxph(surv.full.1, data = dat, method = "breslow")
 ```
 
@@ -706,25 +993,16 @@ hr.compare <- bind_rows(
     mutate(model = "Model without year")
 ) |>
   mutate(
-    term = recode(
+    term = case_when(
-      term,
+      term == "tx.typeLiving" ~ "Living donor",
-      "tx.typeLiving" = "Living donor",
+      term == "age.rec" ~ "Recipient age",
-      "age.rec" = "Recipient age",
+      term == "age.donor" ~ "Donor age",
-      "hla.match" = "HLA match",
+      term == "year" ~ "Transplant year",
-      "age.donor" = "Donor age",
+      TRUE ~ term
-      "year" = "Transplant year"
-    ),
-    term = factor(
-      term,
-      levels = c(
-        "Transplant year",
-        "Living donor",
-        "Recipient age",
-        "Donor age",
-        "HLA match"
-      )
     )
   )
+
+hr.compare
 ggplot(hr.compare, aes(x = estimate, y = term, color = model)) +
   geom_vline(xintercept = 1, linetype = "dashed") +
   geom_errorbarh(
@@ -746,15 +1024,6 @@ ggplot(hr.compare, aes(x = estimate, y = term, color = model)) +
   my.theme
 ```
 
-
-
-
-
-
-
-
-
-
 ---
 
 TODO: write a more generic function (UPDATE: FAILED)
@@ -869,7 +1138,7 @@ dat.accum |>
 ```
 
 ```{r}
-dat[dat$death == 1,] |>
+dat[dat$death == 1, ] |>
   ggplot(aes(x = follow.up)) +
   geom_histogram(bins = 50)
 ```