Unadjusted Differential Variance Inference — unadjdiffvar • cmldiffvar

unadjdiffvar() infers the differential variance of two treatments using an unadjusted estimator.

Usage

unadjdiffvar(
  data_tbl,
  estimand_type = "absolute",
  confidence_level = 0.95,
  treatment_var_name,
  propensity_score_var_name = NULL,
  outcome_var_name
)

Arguments

data_tbl: A data.frame or tibble.
estimand_type: A character indicating whether to estimate the absolute ("absolute") or relative ("relative") differential variance.
confidence_level: A numeric between $0.1$ and $0.99$ providing the confidence level used to compute confidence intervals. Defaults to 0.95.
treatment_var_name: A character providing the column name of the treatment assignment indicator stored in data_tbl.
propensity_score_var_name: An optional character providing the column name of the treatment assignment indicator stored in data_tbl. Defaults to NULL. See the Details section for more information.
outcome_var_name: A character providing the column name of the outcome variable stored in data_tbl.

Value

A one-row tibble containing the following columns:

estimand: The scale of the differential variance estimand
estimate: The differential variance estimate
se: The estimator's standard error
ci_low: The lower bound of the Wald-type confidence interval
ci_high: The upper bound of the Wald-type confidence interval
p_value: The p-value of a two-sided test using the z-score of the differential variance estimate

Details

Under standard causal identifiability conditions — namely, consistency, positivity, and full exchangeability — unadjdiffvar() performs inference on the differential variance of the potential outcomes. Differential variance is defined on the absolute scale as the difference in potential outcome variances of two treatments. On the relative scale, differential variance is defined as the ratio of the potential outcome variances. The scale of the differential variance estimated by unadjdiffvar() is specified by the estimand_type parameter.

unadjdiffvar() assumes that the data in data_tbl are generated according to a parallel group study design with a binary treatment variable and a continuous outcome. Treatment is assumed to be assigned at random. If the treatment assignment probabilities are known, such as in a randomized controlled trial, then these may be passed to the estimator using the propensity_score_var_name argument. If there are (un)measured confounders, this estimator will be biased.