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rbounds (version 0.2)

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 Match().
y
Control group outcomes in same order as treatment group outcomes unnecessary when using 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

Run this code
#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
#             
mDW  <- Match(Y=Y, Tr=Tr, X=DWglm$fitted)

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

#
# Sensitivity Test
#
psens(mDW, Gamma=2, GammaInc=.1)

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