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Summarize Poisson negative binomial regression

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. It is specifically useful for analyzing count data (using the Poisson or Negative Binomial distribution) that is result of a generalized linear model of one (e.g. arm) or more covariates.

Usage

summarize_glm_count(
  lyt,
  vars,
  variables,
  distribution,
  conf_level,
  rate_mean_method = c("emmeans", "ppmeans")[1],
  weights = stats::weights,
  scale = 1,
  var_labels,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  show_labels = "visible",
  table_names = vars,
  .stats = get_stats("summarize_glm_count"),
  .formats = NULL,
  .labels = NULL,
  .indent_mods = c(n = 0L, rate = 0L, rate_ci = 1L, rate_ratio = 0L, rate_ratio_ci = 1L,
    pval = 1L)
)

s_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)
confidence level of the interval.

rate_mean_method

(character(1))
method used to estimate the mean odds ratio. Defaults to emmeans. see details for more information.

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().

scale

(numeric(1))
linear scaling factor for rate and confidence intervals. Defaults to 1.

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.

Details

summarize_glm_count() uses s_glm_count() to calculate the statistics for the table. This analysis function uses h_glm_count() to estimate the GLM with stats::glm() for Poisson and Quasi-Poisson distributions or MASS::glm.nb() for Negative Binomial distribution. All methods assume a logarithmic link function.

At this point, rates and confidence intervals are estimated from the model using either emmeans::emmeans() when rate_mean_method = "emmeans" or h_ppmeans() when rate_mean_method = "ppmeans".

If a reference group is specified while building the table with split_cols_by(ref_group), no rate ratio or p-value are calculated. Otherwise, we use emmeans::contrast() to calculate the rate ratio and p-value for the reference group. Values are always estimated with method = "trt.vs.ctrl" and ref equal to the first arm value.

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.

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 = "Adjusted (P) exacerbation rate (per year)",
    table_names = "adjP",
    .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 = "adjQP",
    .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%)    
#> Adjusted (P) 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