Logistic regression association analysis — assoc

Fits one or more logistic regression models for each exposure variable and returns a tidy result table suitable for downstream forest plots. By default, two standard adjustment models are always included:

Usage

assoc_logistic(
  data,
  outcome_col,
  exposure_col,
  covariates = NULL,
  base = TRUE,
  ci_method = c("wald", "profile"),
  conf_level = 0.95
)

assoc_logit(
  data,
  outcome_col,
  exposure_col,
  covariates = NULL,
  base = TRUE,
  ci_method = c("wald", "profile"),
  conf_level = 0.95
)

Arguments

data: (data.frame or data.table) Analysis dataset.
outcome_col: (character) Binary outcome column (0/1 or TRUE/FALSE).
exposure_col: (character) One or more exposure variable names.
covariates: (character or NULL) Covariate column names for the Fully adjusted model. Default: NULL.
base: (logical) Include Unadjusted and Age and sex adjusted models. Default: TRUE.
ci_method: (character) CI calculation method: "wald" (default) or "profile".
conf_level: (numeric) Confidence level. Default: 0.95.

Value

A data.table with one row per exposure \(\times\) term \(\times\) model combination, and columns:

exposure: Exposure variable name.
term: Coefficient name (e.g. "bmi_categoryObese").
model: Ordered factor: Unadjusted < Age and sex adjusted < Fully adjusted.
n: Participants in model (after NA removal).
n_cases: Number of cases (outcome = 1) in model.
OR: Odds ratio (point estimate).
CI_lower: Lower confidence bound.
CI_upper: Upper confidence bound.
p_value: Wald test p-value.
OR_label: Formatted string, e.g. "1.23 (1.05-1.44)".

Details

Unadjusted - no covariates (crude).
Age and sex adjusted - age + sex auto-detected from the data via UKB field IDs (21022 and 31). Skipped with a warning if either column cannot be found.
Fully adjusted - the covariates supplied via the covariates argument. Only run when covariates is non-NULL.

Outcome coding: outcome_col may be logical (TRUE/FALSE) or integer/numeric (0/1). Logical values are converted to integer internally.

CI methods:

"wald" (default) - fast, appropriate for large UKB samples.
"profile" - profile likelihood CI via confint.glm(); slower but more accurate for small or sparse data.

Examples

dt <- ops_toy(scenario = "association", n = 500)
#> ✔ ops_toy: 500 participants | 33 columns | scenario = "association" | seed = 42

res <- assoc_logistic(
  data         = dt,
  outcome_col  = "dm_status",
  exposure_col = "p20116_i0",
  covariates   = c("bmi_cat", "tdi_cat"),
  base         = FALSE
)
#> ℹ outcome_col dm_status: logical detected, converting TRUE/FALSE -> 1/0
#> 
#> ── assoc_logistic ──────────────────────────────────────────────────────────────
#> ℹ 1 exposure x 1 model = 1 logistic regression
#> ℹ Input cohort: 500 participants | CI method: wald (n/n_cases reflect each model's actual analysis set)
#> 
#> ── p20116_i0 ──
#> 
#> ✔   Fully adjusted | p20116_i0Previous: OR 0.73 (0.39-1.35), p = 0.313
#> ✔   Fully adjusted | p20116_i0Current: OR 0.70 (0.30-1.64), p = 0.406
#> ✔ Done: 2 result rows across 1 exposure and 1 model.