Odds ratio estimation

The analyze function estimate_odds_ratio() creates a layout element to compare bivariate responses between two groups by estimating an odds ratio and its confidence interval.

The primary analysis variable specified by vars is the group variable. Additional variables can be included in the analysis via the variables argument, which accepts arm, an arm variable, and strata, a stratification variable. If more than two arm levels are present, they can be combined into two groups using the groups_list argument.

Usage

estimate_odds_ratio(
  lyt,
  vars,
  variables = list(arm = NULL, strata = NULL),
  conf_level = 0.95,
  groups_list = NULL,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  show_labels = "hidden",
  table_names = vars,
  .stats = "or_ci",
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_odds_ratio(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  .df_row,
  variables = list(arm = NULL, strata = NULL),
  conf_level = 0.95,
  groups_list = NULL
)

a_odds_ratio(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  .df_row,
  variables = list(arm = NULL, strata = NULL),
  conf_level = 0.95,
  groups_list = 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.
conf_level: (proportion)
confidence level of the interval.
groups_list: (named list of character)
specifies the new group levels via the names and the levels that belong to it in the character vectors that are elements of the list.
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.
...: arguments passed to s_odds_ratio().
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("estimate_odds_ratio") 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.
.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.
.df_row: (data.frame)
data frame across all of the columns for the given row split.

Value

estimate_odds_ratio() 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_odds_ratio() to the table layout.

s_odds_ratio() returns a named list with the statistics or_ci (containing est, lcl, and ucl) and n_tot.

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

Functions

estimate_odds_ratio(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().
s_odds_ratio(): Statistics function which estimates the odds ratio between a treatment and a control. A variables list with arm and strata variable names must be passed if a stratified analysis is required.
a_odds_ratio(): Formatted analysis function which is used as afun in estimate_odds_ratio().

Note

This function uses logistic regression for unstratified analyses, and conditional logistic regression for stratified analyses. The Wald confidence interval is calculated with the specified confidence level.
For stratified analyses, there is currently no implementation for conditional likelihood confidence intervals, therefore the likelihood confidence interval is not available as an option.
When vars contains only responders or non-responders no odds ratio estimation is possible so the returned values will be NA.

Examples