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Compute (r)OR from a model summary

Source: R/compute_or_mod.R
compute_or_mod.Rd

[Stable] Compute and format Odds-Ratio from a model summary.

Usage

compute_or_mod(.coef_table, estimate, std_er, p_val = NULL, alpha = 0.05)

Arguments

.coef_table

A coefficient table, see details.

estimate

Quasiquoted name of estimate parameter.

std_er

Quasiquoted name of standard error parameter.

p_val

Quasiquoted name of p-value parameter. Optional.

alpha

alpha risk.

Value

A data.table with OR, confidence intervals (at 1 - alpha), significance (low_ci > 1) and (optionally) p-value.

Details

Helper to compute and format Odds-Ratio based on summary(glm)$coefficients, or any equivalent in other modelling packages. (see examples). Preferably, it is transformed into a data.table or data.frame before being evaluated in the function. Otherwise, compute_or_mod() will transform it. Significant OR-or column means low_ci is > 1. The p_val argument is only required if you wished to display a nice_p().

Output is a data.table. Actually, the function computes an Odds-Ratio, which is not necessarily a reporting Odds-Ratio.

See also

compute_dispro(), add_drug(), add_adr()

Examples


# Reporting Odds-Ratio of colitis with nivolumab among ICI cases.

demo <-
  demo_ |>
  add_drug(
    d_code = ex_$d_drecno,
    drug_data = drug_
  ) |>
  add_adr(
    a_code = ex_$a_llt,
    adr_data = adr_
  )
#> ℹ `.data` detected as `demo` table.
#> ℹ `.data` detected as `demo` table.

# Compute the model
mod <- glm(a_colitis ~ nivolumab, data = demo, family = "binomial")

# Extract coefficients
mod_summary <-
 mod |>
 summary()

coef_table <-
 mod_summary$coefficients

# Transform coefficients into ORs with their CI

coef_table |>
  compute_or_mod(
  estimate = Estimate,
  std_er = Std..Error,
  p_val = Pr...z..)
#>             rn   Estimate Std..Error    z.value     Pr...z..        or
#>         <char>      <num>      <num>      <num>        <num>     <num>
#> 1: (Intercept) -2.0476928  0.1371736 -14.927752 2.174746e-50 0.1290323
#> 2:   nivolumab  0.6333854  0.2169532   2.919456 3.506429e-03 1.8839779
#>        low_ci     up_ci    orl          ci ci_level signif_ror  p_val
#>         <num>     <num> <char>      <char>   <char>      <num> <char>
#> 1: 0.09861341 0.1688343   0.13 (0.10-0.17)      95%          0 <.0001
#> 2: 1.23141622 2.8823501   1.88 (1.23-2.88)      95%          1   <.01

# Also works if you don't have a p_val column
 coef_table |>
  compute_or_mod(
  estimate = Estimate,
  std_er = Std..Error)
#>             rn   Estimate Std..Error    z.value     Pr...z..        or
#>         <char>      <num>      <num>      <num>        <num>     <num>
#> 1: (Intercept) -2.0476928  0.1371736 -14.927752 2.174746e-50 0.1290323
#> 2:   nivolumab  0.6333854  0.2169532   2.919456 3.506429e-03 1.8839779
#>        low_ci     up_ci    orl          ci ci_level signif_ror
#>         <num>     <num> <char>      <char>   <char>      <num>
#> 1: 0.09861341 0.1688343   0.13 (0.10-0.17)      95%          0
#> 2: 1.23141622 2.8823501   1.88 (1.23-2.88)      95%          1