find_border: Find the lambda-delta boundary for a fixed sensitivity...

In sensitivityCalibration: A Calibrated Sensitivity Analysis for Matched Observational Studies

Description

Given the dataset, unmeasured confounder, sensitivity parameter p, and a sequence of lambda values, the function uses binary search to find a sequence of delta corresponding to each lambda in the lambda_vec such that the estimated 95% for the treatment effect barely covers 0. The function returns a dataframe consisting of lambda_vec and the corresponding deltas. See below for an example.

Usage

find_border(q, u, p, lambda_vec, start_value_low, start_value_high,
data_matched, n_boot = 2000, tol = 0.01)

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_vec	A sequence of lambda values.
start_value_low	Starting value for the binary search (the lower endpoint).
start_value_high	Starting value for the binary search (the higher endpoint).
data_matched	The dataset after matching.
n_boot	Number of boostrap samples used to approximate the CI.
tol	Tolerance for the binary search.

Details

start_value_low and start_value_high are user supplied numbers to start the binary search.

Examples

data(NHANES_blood_lead_small_matched)
attach(NHANES_blood_lead_small_matched)

find_border(9, c(1,0), c(0.5,0.5), c(0.5,1,1.5), 0, 4,
NHANES_blood_lead_small_matched, n_boot = 1000)

detach(NHANES_blood_lead_small_matched)

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