doseSens (version 1.0.0)

constant_effects_test: Asymptotic sensitivity analysis for weak nulls with continuous exposures assuming constant effects across matched sets.

Description

Asymptotic sensitivity analysis for weak nulls with continuous exposures assuming constant effects across matched sets.

Usage

constant_effects_test(
  Z,
  R,
  index,
  gamma = 0,
  theta = 0,
  X = NA,
  estimand_function = extract_OLS,
  gamma_star_vec = NULL
)

Value

A list containing the deviate, one-sided p-value, observed value of the test statistic in each matched set, and conservative standard deviation estimate.

Arguments

Z

A length N vector of observed doses.

R

A length N vector of observed outcomes.

index

A length N vector of indices indicating matched set membership.

gamma

The nonnegative sensitivity parameter; gamma = 0 means no unmeasured confounding.

theta

The value at which to test the weak null.

X

A matrix with I rows and less than I columns that contains covariate information.

estimand_function

A function that takes in values z and r and outputs a scalar; this function governs the causal estimand to estimate

gamma_star_vec

that contains the maximum ratio of any two probabilities of permutations for each matched set.

Examples

 # Load the data
data <- lead_bmd
# prepare data
threshold <- log(0.74675)
match_info = data |> dplyr::group_by(matched_sets) |>
dplyr::summarise(below = sum(log_lead < threshold) > 0, disc = var(log_lead) > 0,
above = sum(log_lead > threshold) > 0)
below_indices <- match_info$matched_sets[match_info$below]
disc_indices <- match_info$matched_sets[match_info$disc]
above_indices <- match_info$matched_sets[match_info$above]
# outcome analysis using the stochastic intervention statistic, weak null
below_nbp <- data |> dplyr::filter(matched_sets %in% below_indices & matched_sets
%in% disc_indices)
above_below <- below_nbp |> dplyr::filter(matched_sets %in% above_indices)
extract_below_threshold_vs_baseline_function <- function(z, r) {
  extract_below_threshold_vs_baseline(z, r, threshold)
}
# one-sided test that estimand defined by estimand_function is 0 at gamma = 0
result <- constant_effects_test(Z = above_below$log_lead,
R = above_below$lumbar_spine_bmd,
index = above_below$matched_sets, gamma = 0, theta = 0,
estimand_function = extract_below_threshold_vs_baseline_function)