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Odds ratio estimation

Source: R/odds_ratio.R
odds_ratio.Rd

`r lifecycle::badge('stable')`

Usage

a_odds_ratio_j(
  df,
  .var,
  .df_row,
  ref_path,
  .spl_context,
  ...,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_odds_ratio_j(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  .df_row,
  variables = list(arm = NULL, strata = NULL),
  conf_level = 0.95,
  groups_list = NULL,
  na_if_no_events = TRUE,
  method = c("exact", "approximate", "efron", "breslow", "cmh")
)

Arguments

df

(`data.frame`)
input data frame.

.var

(`string`)
name of the response variable.

.df_row

(`data.frame`)
data frame containing all rows.

ref_path

(`character`)
path to the reference group.

.spl_context

(`environment`)
split context environment.

...

Additional arguments passed to the statistics function.

.stats

(`character`)
statistics to calculate.

.formats

(`list`)
formats for the statistics.

.labels

(`list`)
labels for the statistics.

.indent_mods

(`list`)
indentation modifications for the statistics.

.ref_group

(`data.frame`)
reference group data frame.

.in_ref_col

(`logical`)
whether the current column is the reference column.

variables

(`list`)
list with arm and strata variable names.

conf_level

(`numeric`)
confidence level for the confidence interval.

groups_list

(`list`)
list of groups for combination.

na_if_no_events

(`flag`)
whether the point estimate should be `NA` if there are no events in one arm. The p-value and confidence interval will still be computed.

method

(`string`)
whether to use the correct (`'exact'`) calculation in the conditional likelihood or one of the approximations, or the CMH method. See [survival::clogit()] for details.

Value

* `a_odds_ratio_j()` returns the corresponding list with formatted [rtables::CellValue()].

* `s_odds_ratio_j()` returns a named list with the statistics `or_ci` (containing `est`, `lcl`, and `ucl`), `pval` and `n_tot`.

Functions

  • a_odds_ratio_j(): Formatted analysis function which is used as `afun`. Note that the junco specific `ref_path` and `.spl_context` arguments are used for reference column information.

  • s_odds_ratio_j(): 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.

Note

The `a_odds_ratio_j()` and `s_odds_ratio_j()` functions have the `_j` suffix to distinguish them from [tern::a_odds_ratio()] and [tern::s_odds_ratio()], respectively. These functions differ as follows:

* Additional `method = 'cmh'` option is provided to calculate the Cochran-Mantel-Haenszel estimate. * The p-value is returned as an additional statistic.

Once these updates are contributed back to `tern`, they can later be replaced by the `tern` versions.

Examples

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

a_odds_ratio_j(
  df = subset(dta, grp == 'A'),
  .var = 'rsp',
  ref_path = c('grp', 'B'),
  .spl_context = data.frame(
    cur_col_split = I(list('grp')),
    cur_col_split_val = I(list(c(grp = 'A'))),
    full_parent_df = I(list(dta))
  ),
  .df_row = dta
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>   row_name  formatted_cell indent_mod           row_label
#> 1    n_tot              50          0             Total n
#> 2    or_ci NE (0.00 - Inf)          1 Odds Ratio (95% CI)
#> 3     pval          >0.999          1             p-value


l <- basic_table() |>
  split_cols_by(var = 'grp') |>
  analyze(
    'rsp',
    afun = a_odds_ratio_j,
    show_labels = 'hidden',
    extra_args = list(
      ref_path = c('grp', 'B'),
      .stats = c('or_ci', 'pval')
    )
  )

build_table(l, df = dta)
#>                                 A            B
#> ——————————————————————————————————————————————
#>   Odds Ratio (95% CI)   0.85 (0.38 - 1.88)    
#>   p-value                     0.685           

l2 <- basic_table() |>
  split_cols_by(var = 'grp') |>
  analyze(
    'rsp',
    afun = a_odds_ratio_j,
    show_labels = 'hidden',
    extra_args = list(
      variables = list(arm = 'grp', strata = 'strata'),
      method = 'cmh',
      ref_path = c('grp', 'A'),
      .stats = c('or_ci', 'pval')
    )
  )

build_table(l2, df = dta)
#>                         A           B         
#> ——————————————————————————————————————————————
#>   Odds Ratio (95% CI)       1.31 (0.58 - 2.96)
#>   p-value                         0.524       
# Unstratified analysis.
s_odds_ratio_j(
  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 
#> 
#> $pval
#> [1] 0.6854057
#> 

# Stratified analysis.
s_odds_ratio_j(
  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 
#> 
#> $pval
#> [1] 0.5245098
#> 

# Stratified analysis with CMH.
s_odds_ratio_j(
  df = subset(dta, grp == 'A'),
  method = 'cmh',
  .var = 'rsp',
  .ref_group = subset(dta, grp == 'B'),
  .in_ref_col = FALSE,
  .df_row = dta,
  variables = list(arm = 'grp', strata = c('strata'))
)
#> $or_ci
#>       est       lcl       ucl 
#> 0.7647498 0.3376522 1.7320849 
#> attr(,"label")
#> [1] "Odds Ratio (95% CI)"
#> 
#> $n_tot
#> n_tot 
#>   100 
#> 
#> $pval
#> [1] 0.5241419
#>