matchMulti (version 1.1.12.1)

matchMultisens: Rosenbaum Bounds after Multilevel Matching

Description

Function to calculate Rosenbaum bounds for continuous outcomes after multilevel matching.

Usage

matchMultisens(
  obj,
  out.name = NULL,
  schl_id_name = NULL,
  treat.name = NULL,
  Gamma = 1
)

Value

pval

Upper bound on one-sided approximate p-value for test of the sharp null.

Arguments

obj

A multilevel match object

out.name

Outcome variable name

schl_id_name

Level 2 ID variable name, that is this variable identifies clusters matched in the data.

treat.name

Treatment indicator name

Gamma

Sensitivity analysis parameter value. Default is one.

Author

Luke Keele, University of Pennsylvania,

Sam Pimentel, University of California, Berkeley,

Details

This function returns a single p-value, but actually conducts two tests. The first assumes that the treatment effect does not vary with cluster size. The second allows the treatment effect to vary with cluster size. The function returns a single p-value that is corrected for multiple testing. This p-value is the upper bound for a single Gamma value

References

Rosenbaum, Paul R. (2002) Observational Studies. Springer-Verlag.

See Also

See Also as matchMulti, matchMultioutcome

Examples

if (FALSE) {
# Load Catholic school data
data(catholic_schools)

student.cov <- c('minority','female','ses','mathach')

# Check balance student balance before matching
balanceTable(catholic_schools[c(student.cov,'sector')],  treatment = 'sector')

#Match schools but not students within schools
match.simple <- matchMulti(catholic_schools, treatment = 'sector',
school.id = 'school', match.students = FALSE)

#Check balance after matching - this checks both student and school balance
balanceMulti(match.simple, student.cov = student.cov)

#Estimate treatment effect
output <- matchMultioutcome(match.simple, out.name = "mathach",
schl_id_name = "school",  treat.name = "sector")

# Perform sensitivity analysis using Rosenbaum bound -- increase Gamma to increase effect of
# possible hidden confounder 
         
matchMultisens(match.simple, out.name = "mathach",
          schl_id_name = "school", 
          treat.name = "sector", Gamma=1.3)
          
          }