doseSens (version 1.0.0)

binary_thresh_attribute: Separable algorithm for threshold attributable effect in a sensitivity analysis with at most one over-exposed unit in each matched set. For a greater than alternative, finds the 'a' matched sets that most decrease the mean and/or variance.

Description

Separable algorithm for threshold attributable effect in a sensitivity analysis with at most one over-exposed unit in each matched set. For a greater than alternative, finds the 'a' matched sets that most decrease the mean and/or variance.

Usage

binary_thresh_attribute(
  Z,
  Q,
  index,
  gamma,
  thresh = 0,
  a = 1,
  trans = identity,
  mc = 50000
)

Value

Either "reject" if the value a is deemed not plausible/compatible, "feasible" if the value a is deemed so, else a list containing a p-value and dataframe of matched sets that have contribution to the test statistic sorted in order of smallest mean reduction followed by smallest variance reduction.

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.

thresh

The dose threshold for the TAE.

a

The number of attributable effects to test for.

trans

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

mc

Number of monte-carlo samples if testing the sharp null, i.e. a = 0.

Examples

# Load the data
data <- lead_crime
# Solve by the separable algorithm
solution <- binary_thresh_attribute(data$log_lead, data$complain, data$matched_sets,
gamma = 0, thresh = log(3.5), a = 5, trans = identity)