Summarize Poisson negative binomial regression — 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

summarize_glm_count(
  lyt,
  vars,
  variables,
  distribution,
  conf_level,
  rate_mean_method,
  weights = stats::weights,
  scale = 1,
  var_labels,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  show_labels = "visible",
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

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
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

variables

(named list of string)
list of additional analysis variables, with expected elements:

arm (string)
group variable, for which the covariate adjusted means of multiple groups will be summarized. Specifically, the first level of arm variable is taken as the reference group.
covariates (character)
a vector that can contain single variable names (such as "X1"), and/or interaction terms indicated by "X1 * X2".
offset (numeric)
a numeric vector or scalar adding an offset.

distribution

(character)
a character value specifying the distribution used in the regression (Poisson, Quasi-Poisson, negative binomial).

conf_level

(proportion)
value used to derive the confidence interval for the rate.

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().

var_labels

(character)
variable labels.

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 the case that the same vars are analyzed multiple times, to avoid warnings from rtables.

.stats

(character)
statistics to select for the table. Run get_stats("summarize_glm_count") to see available statistics for this function.

.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.

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)
dataset that includes all the variables that are called in .var and variables.

.ref_group

(data.frame or vector)
the data corresponding to the reference group.

.in_ref_col

(flag)
TRUE when working with the reference level, FALSE otherwise.

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.

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().

Functions

summarize_glm_count(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().
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().

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%)"),
    .labels = 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)
#>                                                 A: Drug X          B: Placebo       C: Combination 
#>                                                   (N=69)             (N=73)             (N=58)     
#> ———————————————————————————————————————————————————————————————————————————————————————————————————
#> Number of exacerbations per patient                                                                
#>   0                                             3 (4.35%)          8 (10.96%)         6 (10.34%)   
#>   1                                            11 (15.94%)         9 (12.33%)         6 (10.34%)   
#>   2                                            18 (26.09%)        15 (20.55%)         9 (15.52%)   
#>   3                                            14 (20.29%)        11 (15.07%)        15 (25.86%)   
#>   4                                            10 (14.49%)         9 (12.33%)         9 (15.52%)   
#>   5                                             7 (10.14%)         9 (12.33%)         8 (13.79%)   
#>   6                                             4 (5.80%)          4 (5.48%)          4 (6.90%)    
#>   7                                             2 (2.90%)          8 (10.96%)         0 (0.00%)    
#>   10                                            0 (0.00%)          0 (0.00%)          1 (1.72%)    
#> Unadjusted exacerbation rate (per year)                                                            
#>   Rate                                            8.2061             9.1554             7.8551     
#> Adjusted (QP) exacerbation rate (per year)                                                         
#>   Rate                                            3.1214             3.4860             2.6152     
#>   Rate CI                                    (1.7307, 5.6294)   (1.9833, 6.1272)   (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