Summary for Poisson Negative Binomial. — summarize_glm

Summarize results of a Poisson Negative Binomial Regression. This can be used to analyze count and/or frequency data using a linear model.

Usage

s_glm_count(
  df,
  .var,
  .df_row,
  variables,
  .ref_group,
  .in_ref_col,
  distribution,
  conf_level,
  rate_mean_method,
  weights,
  scale = 1
)

a_glm_count(
  df,
  .var,
  .df_row,
  variables,
  .ref_group,
  .in_ref_col,
  distribution,
  conf_level,
  rate_mean_method,
  weights,
  scale = 1
)

summarize_glm_count(
  lyt,
  vars,
  var_labels,
  ...,
  show_labels = "visible",
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

df: (data.frame)
data set containing all analysis variables.
.var: (string)
single variable name that is passed by rtables when requested by a statistics function.
.df_row: (data.frame)
data frame across all of the columns for the given row split.
variables: (named list of string)
list of additional analysis variables.
.ref_group: (data.frame or vector)
the data corresponding to the reference group.
.in_ref_col: (logical)
TRUE when working with the reference level, FALSE otherwise.
conf_level: (proportion)
confidence level of the interval.
lyt: (layout)
input layout where analyses will be added to.
vars: (character)
variable names for the primary analysis variable to be iterated over.
var_labels: (character)
character for label.
...: additional arguments for the lower level functions.
show_labels: (string)
label visibility: one of "default", "visible" and "hidden".
table_names: (character)
this can be customized in case that the same vars are analyzed multiple times, to avoid warnings from rtables.
.stats: (character)
statistics to select for the table.
.formats: (named character or list)
formats for the statistics.
.labels: (named character)
labels for the statistics (without indent).
.indent_mods: (named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

Value

s_glm_count() returns a named list of 5 statistics:
- n: Count of complete sample size for the group.
- rate: Estimated event rate per follow-up time.
- rate_ci: Confidence level for estimated rate per follow-up time.
- rate_ratio: Ratio of event rates in each treatment arm to the reference arm.
- rate_ratio_ci: Confidence level for the rate ratio.
- pval: p-value.

a_glm_count() returns the corresponding list with formatted rtables::CellValue().

summarize_glm_count() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_glm_count() to the table layout.

Functions

s_glm_count(): Statistics function that produces a named list of results of the investigated Poisson model.
a_glm_count(): Formatted analysis function which is used as afun in summarize_glm_count().
summarize_glm_count(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

Examples

# Internal function - s_change_from_baseline
if (FALSE) {
s_glm_count(
  df = anl %>%
    filter(ARMCD == "ARM B"),
  .df_row = anl,
  .var = "AVAL",
  .in_ref_col = TRUE,
  variables = list(arm = "ARMCD", offset = "lgTMATRSK", covariates = c("REGION1")),
  conf_level = 0.95,
  distribution = "quasipoisson",
  rate_mean_method = "ppmeans"
)
}

# Internal function - s_change_from_baseline
if (FALSE) {
a_glm_count(
  df = anl %>%
    filter(ARMCD == "ARM A"),
  .var = "AVAL",
  .df_row = anl,
  variables = list(arm = "ARMCD", offset = "lgTMATRSK", covariates = c("REGION1")),
  .ref_group = "ARM B", .in_ref_col = TRUE,
  conf_level = 0.95,
  distribution = "poisson",
  rate_mean_method = "ppmeans"
)
}

library(dplyr)
anl <- tern_ex_adtte %>% filter(PARAMCD == "TNE")
anl$AVAL_f <- as.factor(anl$AVAL)

lyt <- basic_table() %>%
  split_cols_by("ARM", ref_group = "B: Placebo") %>%
  add_colcounts() %>%
  summarize_vars(
    "AVAL_f",
    var_labels = "Number of exacerbations per patient",
    .stats = c("count_fraction"),
    .formats = c("count_fraction" = "xx (xx.xx%)"),
    .label = c("Number of exacerbations per patient")
  ) %>%
  summarize_glm_count(
    vars = "AVAL",
    variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = NULL),
    conf_level = 0.95,
    distribution = "poisson",
    rate_mean_method = "emmeans",
    var_labels = "Unadjusted exacerbation rate (per year)",
    table_names = "unadj",
    .stats = c("rate"),
    .labels = c(rate = "Rate")
  ) %>%
  summarize_glm_count(
    vars = "AVAL",
    variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = c("REGION1")),
    conf_level = 0.95,
    distribution = "quasipoisson",
    rate_mean_method = "ppmeans",
    var_labels = "Adjusted (QP) exacerbation rate (per year)",
    table_names = "adj",
    .stats = c("rate", "rate_ci", "rate_ratio", "rate_ratio_ci", "pval"),
    .labels = c(
      rate = "Rate", rate_ci = "Rate CI", rate_ratio = "Rate Ratio",
      rate_ratio_ci = "Rate Ratio CI", pval = "p value"
    )
  )
build_table(lyt = lyt, df = anl)
#>                                                 B: Placebo           A: Drug X        C: Combination  
#>                                                   (N=73)              (N=69)              (N=58)      
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————
#> Number of exacerbations per patient                                                                   
#>   0                                             8 (10.96%)           3 (4.35%)          6 (10.34%)    
#>   1                                             9 (12.33%)          11 (15.94%)         6 (10.34%)    
#>   2                                             15 (20.55%)         18 (26.09%)         9 (15.52%)    
#>   3                                             11 (15.07%)         14 (20.29%)         15 (25.86%)   
#>   4                                             9 (12.33%)          10 (14.49%)         9 (15.52%)    
#>   5                                             9 (12.33%)          7 (10.14%)          8 (13.79%)    
#>   6                                              4 (5.48%)           4 (5.80%)           4 (6.90%)    
#>   7                                             8 (10.96%)           2 (2.90%)           0 (0.00%)    
#>   10                                             0 (0.00%)           0 (0.00%)           1 (1.72%)    
#> Unadjusted exacerbation rate (per year)                                                               
#>   Rate                                            9.1554              8.2061              7.8551      
#> Adjusted (QP) exacerbation rate (per year)                                                            
#>   Rate                                            10.2061             9.1386              7.6566      
#>   Rate CI                                    (5.6275, 18.5099)   (4.9399, 16.9060)   (4.0222, 14.5749)
#>   Rate Ratio                                                          0.8954              0.7502      
#>   Rate Ratio CI                                                  (0.3975, 2.0170)    (0.3067, 1.8348) 
#>   p value                                                             0.7897              0.5288