Summary of Adverse Events

The summary of Adverse Events is a nice table just summing up the adverse events in the trial. Note the “[N] n (%)”-format which is the number of events, number of patients with events and percentage of patients with event.

arms <- c("Active", "Control")
total_n <- n_distinct(adae$subjectid)

header_ae <- adae %>%
  group_by(trt, subjectid) %>%
  summarise(n=n()) %>%
  group_by(trt) %>%
  summarise(n = n()) %>%
  ungroup() %>%
  mutate(armtxt = arms) %>%
  mutate(txt = paste0(armtxt, " (N=", n, ")")) %>%
  select(txt) %>%
  deframe


ae_summary_table <- tribble(
    ~text,  ~var, ~f,
  "Number of AEs", "anyae", "ae_N_n_pct",
  "Number of patients with any AEs?", "anyae", "ae_n_pct",
  "Number of patients with one AE", "one_ae", "ae_n_pct",
  "Number of patients with two AE", "two_ae", "ae_n_pct",
  "Number of patients with three or more AEs", "three_plus_ae", "ae_n_pct",
  "Number of SAEs", "sae", "ae_N_n_pct",
  "Number of patients with any SAEs?", "anysae","ae_n_pct"
)

ae_summary_table %>%
  mutate(data = list(adae),
         group = "trt",
         param = list(level = 1)) %>%
  mutate(res = pmap(list(f, data, var, group, param), stats_exec)) %>%
  mutate(id = map(res,names)) %>%
  unnest(c(res, id)) %>%
  mutate(id = paste0("txt", id)) %>%
  pivot_wider(values_from = res, names_from = id) %>%
  select(text, starts_with("txt")) %>%
  kable(col.names = c("Parameter", header_ae),
        caption = "Summary of Adverse Events",
        booktabs = TRUE)

Adverse Events by System Organ Class and Preferred term

This table is almost a listing, but it gives a nice overview of all Adverse Events in the trial. First we need to make function which does most of the work.

ae_table_fns <- function(data, filtervar){

  filtervar = ensym(filtervar)

data %>%
  group_by(trt) %>%
  mutate(N_pat = n_distinct(subjectid)) %>%
  filter(!!filtervar == 1)  %>%
  group_by(subjectid, trt, N_pat, soc, pt) %>%
  summarise(n_ae = n()) %>%
  filter(!is.na(pt)) %>%
  group_by(trt, N_pat, soc, pt) %>%
  summarise(n_pat = n(),
            n_ae = sum(n_ae)) %>%
  mutate(pct = round(n_pat/N_pat*100,digits = 1),
         txt = paste0("[", n_ae,"] ", n_pat, " (", pct, "%)"),
         arm = paste0("arm", trt)) %>%
  ungroup %>% select(arm, soc, pt, txt) %>%
  pivot_wider(values_from = txt, names_from = arm) %>%
  mutate_at(vars(starts_with("arm")), ~if_else(is.na(.), "", .)) %>%
  arrange(soc, pt) %>%  group_by(soc2 = soc) %>%
  mutate(soc = if_else(row_number() != 1, "", soc)) %>% ungroup() %>% select(-soc2)
}
adae %>%
  bind_rows(adae, .id="added") %>%
  filter(!is.na(pt)) %>% 
  mutate(pt = if_else(added == 2, "#Total", pt)) %>%
  mutate(all = 1) %>%
  ae_table_fns("all") %>%
  knitr::kable(col.names = c("System Organ Class", "Preferred Term", header_ae),
               caption = " Adverse Events by System Organ Class and Preferred term*",
         booktabs = TRUE,
         longtable = TRUE)

Serious Adverse Events by System Organ Class and Preferred term

Then the sub-table with only the serious adverse events.

adae %>%
  bind_rows(adae, .id="added") %>%
  filter(!is.na(pt)) %>% 
  mutate(pt = if_else(added == 2, "#Total", pt)) %>%
  ae_table_fns("sae") %>%
  knitr::kable( col.names = c("System Organ Class", "Preferred Term", header_ae),
               caption = "Serious Adverse Events by System Organ Class and Preferred term*",
         booktabs = TRUE,
         longtable = TRUE)

Usually there is also a table of probable/possible study treatment related AE/SAEs, and maybe also a AE/SAE of special interest table. They are made similarly to the SAE table.

Most common Adverse events

Lastly a table of the most common Adverse events. It is easy to change the treshold.

adae %>%
  filter(!is.na(pt)) %>% 
  group_by(trt) %>%
  mutate(N_pat = n_distinct(subjectid)) %>%
  group_by(subjectid, trt, N_pat, soc, pt) %>%
  summarise(n_ae = n()) %>%
  filter(!is.na(pt)) %>%
  group_by(trt, N_pat, soc, pt) %>%
  summarise(n_pat = n(),
            n_ae = sum(n_ae)) %>%
  mutate(pct = round(n_pat/N_pat*100,digits = 1),
         txt = paste0("[", n_ae,"] ", n_pat, " (", pct, "%)"),
         arm = paste0("arm", trt)) %>%
  group_by(pt) %>%
  mutate(N_pat = sum(n_pat),
         pct_tot = N_pat /total_n) %>%
  filter(pct_tot>0.05) %>%
  ungroup %>%
  select(arm, soc, pt, txt, pct_tot) %>%
  pivot_wider(values_from = txt, names_from = arm) %>%
  mutate_at(vars(starts_with("arm")), ~if_else(is.na(.), "", .)) %>%
  arrange(desc(pct_tot)) %>% select( pt, everything()) %>% select(-pct_tot, -soc)  %>%
  knitr::kable( col.names = c( "Preferred Term", header_ae),
               caption = "Most common Adverse Events (more than 5 percent) by Preferred Term",
         booktabs = TRUE,
         longtable = TRUE)