Skip to contents

Relative risk estimation

Source: R/relative_risk.R
relative_risk.Rd

The analysis function [a_relative_risk()] is used to create a layout element to estimate the relative risk for response within a studied population. Only the CMH method is available currently. The primary analysis variable, `vars`, is a logical variable indicating whether a response has occurred for each record. A stratification variable must be supplied via the `strata` element of the `variables` argument.

Usage

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

s_relative_risk(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  variables = list(strata = NULL),
  conf_level = 0.95,
  method = "cmh",
  weights_method = "cmh"
)

Arguments

df

(`data.frame`)
input data frame.

.var

(`string`)
name of the response variable.

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 strata variable names.

conf_level

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

method

(`string`)
method to use for relative risk calculation.

weights_method

(`string`)
method to use for weights calculation in stratified analysis.

Value

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

* `s_relative_risk()` returns a named list of elements `rel_risk_ci` and `pval`.

Details

The variance of the CMH relative risk estimate is calculated using the Greenland and Robins (1985) variance estimation.

Functions

  • a_relative_risk(): 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_relative_risk(): Statistics function estimating the relative risk for response.

Note

This has been adapted from the `odds_ratio` functions in the `tern` package.

Examples

## 'Mid' case: 4/4 respond in group A, 1/2 respond in group B.
nex <- 100 # Number of example rows
dta <- data.frame(
  'rsp' = sample(c(TRUE, FALSE), nex, TRUE),
  'grp' = sample(c('A', 'B'), nex, TRUE),
  'f1' = sample(c('a1', 'a2'), nex, TRUE),
  'f2' = sample(c('x', 'y', 'z'), nex, TRUE),
  stringsAsFactors = TRUE
)

l <- basic_table() |>
  split_cols_by(var = 'grp') |>
  analyze(
    vars = 'rsp',
    afun = a_relative_risk,
    extra_args = list(
      conf_level = 0.90,
      variables = list(strata = 'f1'),
      ref_path = c('grp', 'B')
    )
  )

build_table(l, df = dta)
#>                                    A            B
#> —————————————————————————————————————————————————
#>   Relative risk (90% CI)   0.97 (0.68 - 1.37)    
#>   p-value                        0.872           
nex <- 100 # Number of example rows
dta <- data.frame(
  'rsp' = sample(c(TRUE, FALSE), nex, TRUE),
  'grp' = sample(c('A', 'B'), nex, TRUE),
  'f1' = sample(c('a1', 'a2'), nex, TRUE),
  'f2' = sample(c('x', 'y', 'z'), nex, TRUE),
  stringsAsFactors = TRUE
)

s_relative_risk(
  df = subset(dta, grp == 'A'),
  .var = 'rsp',
  .ref_group = subset(dta, grp == 'B'),
  .in_ref_col = FALSE,
  variables = list(strata = c('f1', 'f2')),
  conf_level = 0.90
)
#> $rel_risk_ci
#>       est       lcl       ucl 
#> 0.8284753 0.5998172 1.1443009 
#> attr(,"label")
#> [1] "Relative risk (90% CI)"
#> 
#> $pval
#> [1] 0.3341692
#>