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Exposure-Adjusted Incidence Rate

Source: R/a_eair_j.R
a_eair100_j.Rd

Statistical/Analysis Function for presenting Exposure-Adjusted Incidence Rate summary data

Usage

a_eair100_j(
  df,
  labelstr = NULL,
  .var,
  .df_row,
  .spl_context,
  .alt_df_full = NULL,
  id = "USUBJID",
  drop_levels = FALSE,
  riskdiff = TRUE,
  ref_path = NULL,
  .stats = c("eair"),
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL,
  na_str = rep("NA", 3),
  conf_level = 0.95,
  fup_var,
  occ_var,
  occ_dy
)

Arguments

df

(data.frame)
data set containing all analysis variables.

labelstr

(string)
label string for the row.

.var

(string)
variable name for analysis.

.df_row

(data.frame)
data frame across all of the columns for the given row split.

.spl_context

(data.frame)
gives information about ancestor split states.

.alt_df_full

(dataframe)
denominator dataset for calculations.

id

(string)
subject variable name.

drop_levels

(logical)
if TRUE, non-observed levels will not be included.

riskdiff

(logical)
if TRUE, risk difference calculations will be performed.

ref_path

(string)
column path specifications for the control group.

.stats

(character)
statistics to select for the table.

.formats

(named 'character' or 'list')
formats for the statistics.

.labels

(named 'character')
labels for the statistics.

.indent_mods

(named integer)
indent modifiers for the labels.

na_str

(string)
string used to replace all NA or empty values in the output.

conf_level

(proportion)
confidence level of the interval.

fup_var

(string)
variable name for follow-up time.

occ_var

(string)
variable name for occurrence.

occ_dy

(string)
variable name for occurrence day.

Value

Functions

  • a_eair100_j(): Formatted analysis function for exposure adjusted incidence rate summary which is used as afun in analyze or cfun in summarize_row_groups.

Examples

library(tern)
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
trtvar <- "ARM"
ctrl_grp <- "B: Placebo"
cutoffd <- as.Date("2023-09-24")


adexsum <- ex_adsl |>
  create_colspan_var(
    non_active_grp          = ctrl_grp,
    non_active_grp_span_lbl = " ",
    active_grp_span_lbl     = "Active Study Agent",
    colspan_var             = "colspan_trt",
    trt_var                 = trtvar
  ) |>
  mutate(
    rrisk_header = "Risk Difference (95% CI)",
    rrisk_label = paste(!!rlang::sym(trtvar), "vs", ctrl_grp),
    TRTDURY = case_when(
      !is.na(EOSDY) ~ EOSDY,
      TRUE ~ as.integer(cutoffd - as.Date(TRTSDTM) + 1)
    )
  ) |>
  select(USUBJID, !!rlang::sym(trtvar), colspan_trt, rrisk_header, rrisk_label, TRTDURY)

adexsum$TRTDURY <- as.numeric(adexsum$TRTDURY)

adae <- ex_adae |>
  group_by(USUBJID, AEDECOD) |>
  select(USUBJID, AEDECOD, ASTDY) |>
  mutate(rwnum = row_number()) |>
  mutate(AOCCPFL = case_when(
    rwnum == 1 ~ "Y",
    TRUE ~ NA
  )) |>
  filter(AOCCPFL == "Y")

aefup <- left_join(adae, adexsum, by = "USUBJID")

colspan_trt_map <- create_colspan_map(adexsum,
  non_active_grp = ctrl_grp,
  non_active_grp_span_lbl = " ",
  active_grp_span_lbl = "Active Study Agent",
  colspan_var = "colspan_trt",
  trt_var = trtvar
)

ref_path <- c("colspan_trt", " ", trtvar, ctrl_grp)

################################################################################
# Define layout and build table:
################################################################################

lyt <- basic_table(show_colcounts = TRUE, colcount_format = "N=xx", top_level_section_div = " ") |>
  split_cols_by("colspan_trt", split_fun = trim_levels_to_map(map = colspan_trt_map)) |>
  split_cols_by(trtvar) |>
  split_cols_by("rrisk_header", nested = FALSE) |>
  split_cols_by(trtvar, labels_var = "rrisk_label", split_fun = remove_split_levels(ctrl_grp)) |>
  analyze("TRTDURY",
    nested = FALSE,
    show_labels = "hidden",
    afun = a_patyrs_j
  ) |>
  analyze(
    vars = "AEDECOD",
    nested = FALSE,
    afun = a_eair100_j,
    extra_args = list(
      fup_var = "TRTDURY",
      occ_var = "AOCCPFL",
      occ_dy = "ASTDY",
      ref_path = ref_path,
      drop_levels = TRUE
    )
  )

result <- build_table(lyt, aefup, alt_counts_df = adexsum)
head(result, 5)
#>                     Active Study Agent                                   Risk Difference (95% CI)               
#>                 A: Drug X   C: Combination   B: Placebo   A: Drug X vs B: Placebo   C: Combination vs B: Placebo
#>                   N=134         N=132          N=134               N=134                       N=132            
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Patient years    96200.0       91753.0        91431.0                                                           
#>                                                                                                                 
#> dcd A.1.1.1.1      0.1           0.1            0.1         0.00 (-0.03, 0.04)           0.05 (0.01, 0.09)      
#> dcd A.1.1.1.2      0.1           0.1            0.1         0.00 (-0.03, 0.03)           0.01 (-0.02, 0.04)     
#> dcd B.1.1.1.1      0.1           0.1            0.1         -0.01 (-0.04, 0.02)         -0.02 (-0.05, 0.02)     
#> dcd B.2.1.2.1      0.1           0.1            0.1         0.01 (-0.02, 0.05)           0.03 (-0.01, 0.06)