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Summary for analysis of covariance (ANCOVA).

Source: R/summarize_ancova.R
summarize_ancova.Rd

[Stable]

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,
  na_str = NA_character_,
  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.

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_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).

  • 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