rbounds (version 2.1)

psens: Rosenbaum Bounds for Sign Rank

Description

Function to calculate Rosenbaum bounds for continuous or ordinal outcomes based on Wilcoxon sign rank test.

Usage

# Default Method 
psens(x, y = NULL, Gamma = 6, GammaInc = 1)

Arguments

x

Treatment group outcomes in same order as treatment group outcomes or an objects from a Match.

y

Control group outcomes in same order as treatment group outcomes unnecessary when using a Match object.

Gamma

Upper-bound on gamma parameter.

GammaInc

To set user-specified increments for gamma parameter.

References

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

See Also

See also data.prep, binarysens, hlsens, Match, mcontrol

Examples

# Replication of Rosenbaum Sensitivity Tests From Chapter 4 of
# Observational Studies

# Data:  Matched Data of Lead Blood Levels in Children
trt <- c(38, 23, 41, 18, 37, 36, 23, 62, 31, 34, 24, 14, 21, 17, 16, 20,
         15, 10, 45, 39, 22, 35, 49, 48, 44, 35, 43, 39, 34, 13, 73, 25,
         27)
ctrl <- c(16, 18, 18, 24, 19, 11, 10, 15, 16, 18, 18, 13, 19, 10, 16,
          16, 24, 13, 9, 14, 21, 19, 7, 18, 19, 12, 11, 22, 25, 16, 13,
          11, 13)

psens(trt, ctrl)

# Example With Match()

# Load Matching Software and Data
library(Matching)
data(lalonde)

# Estimate Propensity Score
DWglm <- glm(treat ~ age + I(age^2) + educ + I(educ^2) + black + hisp +
             married + nodegr + re74 + I(re74^2) + re75 + I(re75^2) +
             u74 + u75, family=binomial, data=lalonde)

# Save data objects
Y  <- lalonde$re78   #the outcome of interest
Tr <- lalonde$treat #the treatment of interest

# Match - without replacement
mDW  <- Match(Y=Y, Tr=Tr, X=DWglm$fitted, replace=FALSE)

# One should check balance, but let's skip that step for now.

# Sensitivity Test
psens(mDW, Gamma = 2, GammaInc = 0.1)