doseSens (version 1.0.0)

normal_test_gen: Sharp null sensitivity analysis for continuous exposures and binary outcomes using normal approximation.

Description

Sharp null sensitivity analysis for continuous exposures and binary outcomes using normal approximation.

Usage

normal_test_gen(
  Z,
  Q,
  index,
  gamma,
  trans = identity,
  weights = NA,
  obsT = NULL,
  direct = "upper"
)

Value

A list containing the following:

obsT

The observed value of the test statistic

exp

The worst-case expectation

var

The worst-case variance.

deviate

The normal approximation deviate.

Arguments

Z

A length N vector of (nonnegative) observed doses.

Q

A length N vector of observed binary outcomes.

index

A length N vector of indices indicating matched set membership.

gamma

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

trans

The transformation of the doses to use for the test statistic. Default is the identity function.

weights

Weights to apply for the test statistic

obsT

The observed value of the test statistic; default is NULL.

direct

The direction of the test - "upper" or "lower"; default is upper.

Examples

# Load the data
data <- lead_crime
# Make a threshold at log(3.5) transformation function.
above = function(Z) { return(Z > log(3.5)) }
# Conduct randomization test using normal approximation.
solution <- normal_test_gen(data$log_lead, data$complain, data$matched_sets,
gamma = 0, trans = above)