PER_CI {ivdesign}

Construct a two-sided confidence interval for the pooled effect ratio

Description

PER_CI returns the two-sided level-alpha confidence interval of the pooled effect ratio in a cluster-randomized encouragement experiment.

Usage

PER_CI(
  R_t,
  R_c,
  d_t,
  d_c,
  lower,
  upper,
  Q = NULL,
  meshsize = 0.001,
  alpha = 0.05
)

Arguments

R_t

A length-K vector where K is equal to the number of clusters and the kth entry equal to the sum of unit-level outcomes in the encouraged cluster of the kth matched pair of two clusters.

R_c

A length-K vector where K is equal to the number of clusters and the kth entry equal to the sum of unit-level outcomes in the control cluster of the kth matched pair of two clusters.

d_t

A length-K vector where K is equal to the number of clusters and the kth entry equal to the sum of unit-level treatment received in the encouraged cluster of the kth matched pair of two clusters.

d_c

A length-K vector where K is equal to the number of clusters and the kth entry equal to the sum of unit-level treatment received in the control cluster of the kth matched pair of two clusters.

lower, upper

The lower and upper endpoints of the interval to be searched.

Q

A K times p design matrix containing the covariate information. See Details of the function PER.

meshsize

The meshsize of the grid search.

alpha

The level of the confidence interval.

Details

PER_CI constructs a two-sided level-alpha confidence interval by interting the corresponding hypothesis test for the pooled effect ratio. See PER for details on the hypothesis tesing. PER_CI conducts a grid search with user-specified endpoints and meshsize in order to construct the confidence interval.

Value

A length-2 vector of two endpoints of the confidence interval.

Examples

R_t = encouraged_clusters$aggregated_outcome
R_c = control_clusters$aggregated_outcome
d_t = encouraged_clusters$aggregated_treatment
d_c = control_clusters$aggregated_treatment

# Construct 95% CI for the pooled effect ratio estimand
# using the default sample variance estimator, i.e.,
# setting Q = NULL.
CI = PER_CI(R_t, R_c, d_t, d_c, lower = -0.1, upper = 0.1,
           alpha = 0.05)