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Summary for Poisson Negative Binomial.

Source: R/summarize_glm_count.R
summarize_glm_count.Rd

[Experimental]

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,
  na_str = NA_character_,
  nested = TRUE,
  ...,
  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.

distribution

(character)
a character value specifying the distribution used in the regression (poisson, quasipoisson).

conf_level

(proportion)
confidence level of the interval.

weights

(character)
a character vector specifying weights used in averaging predictions. Number of weights must equal the number of levels included in the covariates. Weights option passed to emmeans::emmeans().

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.

na_str

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

nested

(flag)
whether this layout instruction should be applied within the existing layout structure if possible (TRUE, the default) or as a new top-level element (FALSE). Ignored if it would nest a split. underneath analyses, which is not allowed.

...

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. See Details in analyze_vars for more information on the "auto" setting.

.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.

  • 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

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() %>%
  analyze_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                                            3.4860             3.1214             2.6152     
#>   Rate CI                                    (1.9833, 6.1272)   (1.7307, 5.6294)   (1.3661, 5.0065)
#>   Rate Ratio                                                         0.8954             0.7502     
#>   Rate Ratio CI                                                 (0.3975, 2.0170)   (0.3067, 1.8348)
#>   p value                                                            0.7897             0.5288