sensitivityCalibration (version 0.0.1)

NHANES_blood_lead_small_matched: NHANES_blood_lead_small data after matching.

Description

NHANES_blood_lead_small data after a full matching using the optmatch package

Usage

data(NHANES_blood_lead_small_matched)

Format

NHANES_blood_lead_small dataset after a full matching. It consists of 500 instances and the following 12 variables:

COP

treatment, 1 if cotinine level is between 0.563-14.9 ng/ml and 0 otherwise

DMARETHN

1 if white, 0 if others

DMPPIR

Poverty income ratio

HFE1

1 if the house is built before 1974, 0 if after 1974

HFE2

number of rooms in the house

HFHEDUCR

education level of the reference adult

HSAGEIR

age at the time of interview

HSFSIZER

size of the family

HSSEX

1 if male, 0 if female

PBP

blood lead level

U0

placeholder for the hypothesized unmeasured confounder U

matches

matched set assignment

Details

We perform a full matching on the NHANES_blood_lead_small dataset using the optmatch package. The code for constructing this matched dataset from the original dataset is given in the examples section. We add a column U0 as placeholder for the unmeasurefor confounder U.

References

D. M. Mannino, R. Albalak, S. D. Grosse, and J. Repace. Second-hand smoke exposureand blood lead levels in U.S. children.Epidemiology, 14:719-727, 2003

A. Gelman. Scaling regression inputs by dividing by two standard deviations.Statisticsin Medicine, 27:2865-2873, 2008.

Examples

# NOT RUN {
# To run this example, optmatch must be installed
set.seed(1)
library(optmatch)
data(NHANES_blood_lead_small)
attach(NHANES_blood_lead_small)

# Perform a fullmatch
fm = fullmatch(COP ~. , data = NHANES_blood_lead_small[, 1:9], min.controls = 1/4, max.controls = 4)
NHANES_blood_lead_small_matched = cbind(NHANES_blood_lead_small, matches = fm)

# Add a U0 row
U0 = rep(1, dim(NHANES_blood_lead_small_matched)[1])
NHANES_blood_lead_small_matched = cbind(NHANES_blood_lead_small_matched[,1:9], U0,
NHANES_blood_lead_small_matched[, 10:11])
# }