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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

  • 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

nex <- 100
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)   1.14 (0.79 - 1.65)    
#>   p-value                        0.548           
nex <- 100
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 
#> 1.0317456 0.7006869 1.5192220 
#> attr(,"label")
#> [1] "Relative risk (90% CI)"
#> 
#> $pval
#> [1] 0.8962701
#>