Skip to contents

Helper functions for odds ratio estimation

Source: R/odds_ratio.R
h_odds_ratio.Rd

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

Functions to calculate odds ratios in [s_odds_ratio_j()].

Usage

or_glm(data, conf_level)

or_clogit(data, conf_level, method = "exact")

or_cmh(data, conf_level)

Arguments

data

(`data.frame`)
data frame containing at least the variables `rsp` and `grp`, and optionally `strata` for [or_clogit()]. # New: pval here

conf_level

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

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 named `list` of elements `or_ci`, `n_tot` and `pval`.

Functions

  • or_glm(): Estimates the odds ratio based on [stats::glm()]. Note that there must be exactly 2 groups in `data` as specified by the `grp` variable.

  • or_clogit(): Estimates the odds ratio based on [survival::clogit()]. This is done for the whole data set including all groups, since the results are not the same as when doing pairwise comparisons between the groups.

  • or_cmh(): Estimates the odds ratio based on CMH. Note that there must be exactly 2 groups in `data` as specified by the `grp` variable.

See also

[odds_ratio]

Examples

# Data with 2 groups.
data <- data.frame(
  rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1)),
  grp = letters[c(1, 1, 1, 2, 2, 2, 1, 2)],
  strata = letters[c(1, 2, 1, 2, 2, 2, 1, 2)],
  stringsAsFactors = TRUE
)

# Odds ratio based on glm.
or_glm(data, conf_level = 0.95)
#> $or_ci
#>        est        lcl        ucl 
#> 0.33333333 0.01669735 6.65441589 
#> 
#> $n_tot
#> n_tot 
#>     8 
#> 
#> $pval
#> [1] 0.472011
#> 

# Data with 3 groups.
data <- data.frame(
  rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)),
  grp = letters[c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3)],
  strata = LETTERS[c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)],
  stringsAsFactors = TRUE
)


# Odds ratio based on stratified estimation by conditional logistic regression.
or_clogit(data, conf_level = 0.95)
#> $or_ci_pvals
#> $or_ci_pvals$b
#>        est        lcl        ucl       pval 
#> 0.28814553 0.02981009 2.78522598 0.28237516 
#> 
#> $or_ci_pvals$c
#>       est       lcl       ucl      pval 
#> 0.5367919 0.0673365 4.2791881 0.5569374 
#> 
#> 
#> $n_tot
#> n_tot 
#>    20 
#> 

# Data with 2 groups.
set.seed(123)
data <- data.frame(
  rsp = as.logical(rbinom(n = 40, size = 1, prob = 0.5)),
  grp = letters[sample(1:2, size = 40, replace = TRUE)],
  strata = LETTERS[sample(1:2, size = 40, replace = TRUE)],
  stringsAsFactors = TRUE
)

# Odds ratio based on CMH.
or_cmh(data, conf_level = 0.95)
#> $or_ci
#>       est       lcl       ucl 
#> 0.9969199 0.2877116 3.4543244 
#> 
#> $n_tot
#> n_tot 
#>    40 
#> 
#> $pval
#> [1] 0.9960178
#>