Summary for analysis of covariance (ANCOVA). — summarize

Summarize results of ANCOVA. This can be used to analyze multiple endpoints and/or multiple timepoints within the same response variable .var.

Usage

s_ancova(
  df,
  .var,
  .df_row,
  variables,
  .ref_group,
  .in_ref_col,
  conf_level,
  interaction_y = FALSE,
  interaction_item = NULL
)

a_ancova(
  df,
  .var,
  .df_row,
  variables,
  .ref_group,
  .in_ref_col,
  conf_level,
  interaction_y = FALSE,
  interaction_item = NULL
)

summarize_ancova(
  lyt,
  vars,
  var_labels,
  nested = TRUE,
  ...,
  show_labels = "visible",
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL,
  interaction_y = FALSE,
  interaction_item = 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.
interaction_y: (character)
a selected item inside of the interaction_item column which will be used to select the specific ANCOVA results. if the interaction is not needed, the default option is FALSE.
interaction_item: (character)
name of the variable that should have interactions with arm. if the interaction is not needed, the default option is NULL.
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.
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.
.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_ancova() returns a named list of 5 statistics:
- n: Count of complete sample size for the group.
- lsmean: Estimated marginal means in the group.
- lsmean_diff: Difference in estimated marginal means in comparison to the reference group. If working with the reference group, this will be empty.
- lsmean_diff_ci: Confidence level for difference in estimated marginal means in comparison to the reference group.
- pval: p-value (not adjusted for multiple comparisons).

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

summarize_ancova() 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_ancova() to the table layout.

Functions

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

Examples

library(dplyr)

df <- iris %>% filter(Species == "virginica")
.df_row <- iris
.var <- "Petal.Length"
variables <- list(arm = "Species", covariates = "Sepal.Length * Sepal.Width")
.ref_group <- iris %>% filter(Species == "setosa")
conf_level <- 0.95

basic_table() %>%
  split_cols_by("Species", ref_group = "setosa") %>%
  add_colcounts() %>%
  summarize_ancova(
    vars = "Petal.Length",
    variables = list(arm = "Species", covariates = NULL),
    table_names = "unadj",
    conf_level = 0.95, var_labels = "Unadjusted comparison",
    .labels = c(lsmean = "Mean", lsmean_diff = "Difference in Means")
  ) %>%
  summarize_ancova(
    vars = "Petal.Length",
    variables = list(arm = "Species", covariates = c("Sepal.Length", "Sepal.Width")),
    table_names = "adj",
    conf_level = 0.95, var_labels = "Adjusted comparison (covariates: Sepal.Length and Sepal.Width)"
  ) %>%
  build_table(iris)
#>                                                                  setosa    versicolor     virginica  
#>                                                                  (N=50)      (N=50)         (N=50)   
#> —————————————————————————————————————————————————————————————————————————————————————————————————————
#> Unadjusted comparison                                                                                
#>   n                                                                50          50             50     
#>   Mean                                                            1.46        4.26           5.55    
#>   Difference in Means                                                         2.80           4.09    
#>     95% CI                                                                (2.63, 2.97)   (3.92, 4.26)
#>     p-value                                                                 <0.0001        <0.0001   
#> Adjusted comparison (covariates: Sepal.Length and Sepal.Width)                                       
#>   n                                                                50          50             50     
#>   Adjusted Mean                                                   2.02        4.19           5.07    
#>   Difference in Adjusted Means                                                2.17           3.05    
#>     95% CI                                                                (1.96, 2.38)   (2.81, 3.29)
#>     p-value                                                                 <0.0001        <0.0001