Odds Ratio Estimation

Usage

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
)

estimate_odds_ratio(
  lyt,
  vars,
  ...,
  show_labels = "hidden",
  table_names = vars,
  .stats = "or_ci",
  .formats = NULL,
  .labels = NULL,
  .indent_mods = 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.

.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.

.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.

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.

lyt

(layout)
input layout where analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

...

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 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_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().

• 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.

Details

This function uses either logistic regression for unstratified analyses, or conditional logistic regression for stratified analyses. The Wald confidence interval with the specified confidence level is calculated.

Functions

• 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().

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

Note

For stratified analyses, there is currently no implementation for conditional likelihood confidence intervals, therefore the likelihood confidence interval is not yet available as an option. Besides, when rsp contains only responders or non-responders, then the result values will be NA, because no odds ratio estimation is possible.

See also

Examples

set.seed(12)
dta <- data.frame(
  rsp = sample(c(TRUE, FALSE), 100, TRUE),
  grp = factor(rep(c("A", "B"), each = 50), levels = c("B", "A")),
  strata = factor(sample(c("C", "D"), 100, TRUE))
)

# Unstratified analysis.
s_odds_ratio(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  .df_row = dta
)
#> $or_ci
#>       est       lcl       ucl 
#> 0.8484848 0.3831831 1.8788053 
#> attr(,"label")
#> [1] "Odds Ratio (95% CI)"
#> 
#> $n_tot
#> n_tot 
#>   100 
#> attr(,"label")
#> [1] "Total n"
#> 

# Stratified analysis.
s_odds_ratio(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  .df_row = dta,
  variables = list(arm = "grp", strata = "strata")
)
#> $or_ci
#>       est       lcl       ucl 
#> 0.7689750 0.3424155 1.7269154 
#> attr(,"label")
#> [1] "Odds Ratio (95% CI)"
#> 
#> $n_tot
#> n_tot 
#>   100 
#> attr(,"label")
#> [1] "Total n"
#> 

a_odds_ratio(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  .df_row = dta
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>   row_name     formatted_cell indent_mod           row_label
#> 1    or_ci 0.85 (0.38 - 1.88)          1 Odds Ratio (95% CI)
#> 2    n_tot                100          0             Total n

dta <- data.frame(
  rsp = sample(c(TRUE, FALSE), 100, TRUE),
  grp = factor(rep(c("A", "B"), each = 50))
)

l <- basic_table() %>%
  split_cols_by(var = "grp", ref_group = "B") %>%
  estimate_odds_ratio(vars = "rsp")

build_table(l, df = dta)
#>                       B           A         
#> ————————————————————————————————————————————
#> Odds Ratio (95% CI)       0.72 (0.33 - 1.60)