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Summarize analysis of covariance (ANCOVA) results

Source: R/summarize_ancova.R
summarize_ancova.Rd

[Stable]

The analyze function summarize_ancova() creates a layout element to summarize ANCOVA results.

This function can be used to analyze multiple endpoints and/or multiple timepoints within the response variable(s) specified as vars.

Additional variables for the analysis, namely an arm (grouping) variable and covariate variables, can be defined via the variables argument. See below for more details on how to specify variables. An interaction term can be implemented in the model if needed. The interaction variable that should interact with the arm variable is specified via the interaction_term parameter, and the specific value of interaction_term for which to extract the ANCOVA results via the interaction_y parameter.

Usage 

summarize_ancova(
  lyt,
  vars,
  variables,
  conf_level,
  interaction_y = FALSE,
  interaction_item = NULL,
  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_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
)

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".

conf_level

(proportion)
confidence level of the interval.

interaction_y

(string or flag)
a selected item inside of the interaction_item variable which will be used to select the specific ANCOVA results. if the interaction is not needed, the default option is FALSE.

interaction_item

(string or NULL)
name of the variable that should have interactions with arm. if the interaction is not needed, the default option is NULL.

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.

Options are: 'n', 'lsmean', 'lsmean_diff', 'lsmean_diff_ci', 'pval'

.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)
data set 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_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.

  • 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).

Functions

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

  • 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().

Examples 

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