CI_block_boot: Construct the 95% confidence interval of the treatment effect...

In sensitivityCalibration: A Calibrated Sensitivity Analysis for Matched Observational Studies

Description

This is the main function in the package. Given a dataset and sensitivity parameters (p, lambda, delta), the function returns 95% CI for the estimated treatment effect.

Usage

CI_block_boot(q, u, p, lambda, delta, data_matched, n_boot = 2000)

Arguments

q	Number of matched covariates plus treatment.
u	Unmeasured confounder; u = c(1,0) if the unmeasured confounder is assumed to be binary.
p	The probability vector corresponding to u; p = c(0.5, 0.5) if the unmeasured confounder is assumed to be Bernoulli(0.5).
lambda	Sensitivity parameter that controls association between U and treatment assignment.
delta	Sensitivity parameter that controls association between U and response.
data_matched	The dataset after matching.
n_boot	Number of boostrap samples.

Details

If the number of matched covariates is k, then q = k + 1.

If the hypothesized unmeasured confounder is binary, then u = c(1,0) and p = c(p, 1-p).

data_matched should be in the following format: the first (q-1) columns are matched covariates, the qth column is the treatment status, and the (q+1)th column is the response. See the NHANES_blood_lead_small_matched dataset for an example.

Note the input for this function is a dataset before matching. To run this function, optmatch package needs to be installed and loaded.

Examples

data(NHANES_blood_lead_small_matched)
attach(NHANES_blood_lead_small_matched)

CI_block_boot(9, c(1,0), c(0.5,0.5), 0, 0, NHANES_blood_lead_small_matched, n_boot = 10)

detach(NHANES_blood_lead_small_matched)

sensitivityCalibration documentation built on May 2, 2019, 1:07 p.m.