Identify confounders and effect modifiers

Review whether one or more candidate variables may act as confounders or effect modifiers for one or more exposures.

Usage

identify_confounder(
  data,
  outcome = NULL,
  exposure,
  potential_confounder,
  approach = "logit",
  time = NULL,
  event = NULL,
  distribution = "weibull",
  method = "change",
  threshold = 10,
  emm_threshold = 10,
  emm_test = c("interaction", "both", "estimate"),
  interaction_alpha = 0.05,
  format = c("flextable", "gt"),
  theme = c("minimal")
)

Arguments

data: A data frame.
outcome: Outcome variable name. Quoted and bare names are accepted. Required for ordinary regression approaches. Leave unset for Cox and parametric survival approaches and supply time and event instead.
exposure: Exposure variable name(s). Can be a character scalar or vector. Quoted names are recommended in scripts, and bare names are also accepted.
potential_confounder: Candidate confounder/effect-modifier variable name(s). Can be a character scalar or vector. Quoted names are recommended in scripts, and bare names are also accepted.
approach: Regression approach. One of "logit", "logbinomial", "poisson", "robpoisson", "linear", "negbin", "cox", or "survreg".
time: Survival time variable name for approach = "cox" or approach = "survreg". Quoted and bare names are accepted.
event: Event indicator variable name for survival approaches. Values may be coded as 0/1, 1/2, logical, or a two-level factor/character variable.
distribution: Parametric survival distribution for approach = "survreg". One of "weibull", "exponential", "lognormal", or "loglogistic".
method: Confounding assessment method. One of "change", "mh", or "both". "change" compares crude and adjusted model estimates. "mh" compares crude and Mantel-Haenszel pooled estimates and is available for binary outcome, binary exposure, and categorical strata. "both" uses either method.
threshold: Percent change threshold for confounding assessment.
emm_threshold: Threshold for relative spread in stratum-specific estimates when using estimate-based effect-modification screening.
emm_test: One of "interaction", "both", or "estimate".
interaction_alpha: Alpha threshold for interaction p-values.
format: Output table format. One of "flextable" (default) or "gt".
theme: Table theme preset or primitives.

Value

If a single exposure-candidate pair is supplied, returns a detailed list.

If multiple combinations are supplied, returns a list with:

summary: A tibble with one row per exposure-candidate combination.
details: A named list of detailed results for each combination.

Details

The function first assesses possible effect modification using stratum-specific estimates and/or an interaction test. If no important effect modification is detected, it then assesses confounding using the selected method.

This is a screening aid for viewing and organising results. Confounding and effect modification should be interpreted using subject-matter knowledge, study design, and causal diagrams such as DAGs. Automated change-in-estimate and interaction checks should not be used as the sole basis for model adjustment.

Use this function when you want to screen one or more candidate variables and organise crude, adjusted, Mantel-Haenszel, and effect-modification signals in one place. For a focused comparison of models with and without a planned exposure-by-modifier interaction term, use interaction_models().

Examples

birthwt_data <- data_birthwt |>
  dplyr::mutate(
    low = factor(low, levels = c(0, 1), labels = c("Normal BW", "Low BW")),
    smoke = factor(smoke, levels = c(0, 1), labels = c("No", "Yes")),
    race = factor(race, levels = c(1, 2, 3),
                  labels = c("White", "Black", "Other"))
  )

identify_confounder(
  data = birthwt_data,
  outcome = low,
  exposure = smoke,
  potential_confounder = race,
  approach = logit
)
#> Confounder and effect-modifier screening
#> # A tibble: 1 × 13
#>   exposure candidate crude_est adjusted_est mh_est percent_change
#>   <chr>    <chr>         <dbl>        <dbl>  <dbl>          <dbl>
#> 1 smoke    race           2.02         3.05   3.09           51.0
#> # ℹ 7 more variables: percent_change_model <dbl>, percent_change_mh <dbl>,
#> #   is_confounder <lgl>, interaction_p <dbl>, is_effect_modifier <lgl>,
#> #   decision <chr>, recommendation <chr>
#> 
#> Use `$table` for the formatted display table.

lung_data <- data_lungcancer |>
  dplyr::mutate(
    trt = factor(trt, levels = c(1, 2),
                 labels = c("Standard treatment", "Test treatment")),
    prior = factor(prior, levels = c(0, 10), labels = c("No", "Yes"))
  )

identify_confounder(
  data = lung_data,
  time = time,
  event = status,
  exposure = trt,
  potential_confounder = prior,
  approach = cox
)
#> Confounder and effect-modifier screening
#> # A tibble: 1 × 13
#>   exposure candidate crude_est adjusted_est mh_est percent_change
#>   <chr>    <chr>         <dbl>        <dbl>  <dbl>          <dbl>
#> 1 trt      prior          1.02         1.03     NA           0.84
#> # ℹ 7 more variables: percent_change_model <dbl>, percent_change_mh <dbl>,
#> #   is_confounder <lgl>, interaction_p <dbl>, is_effect_modifier <lgl>,
#> #   decision <chr>, recommendation <chr>
#> 
#> Use `$table` for the formatted display table.

Usage

Arguments

Value

Details

See also

Examples