stddiff: Calculate the Standardized Difference for Numeric, Binary and Category Variables

Description

Contains three main functions including stddiff.numeric(), stddiff.binary() and stddiff.category(). These are used to calculate the standardized difference between two groups. It is especially used to evaluate the balance between two groups before and after propensity score matching.

Usage

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stddiff.numeric(data,gcol,vcol)
stddiff.binary(data,gcol,vcol)
stddiff.category(data,gcol,vcol)

Arguments

data

a dataframe

gcol

a column number of group variable in data, 0 for control group, 1 for treatment group

vcol

one or more column numbers of different types variables in data

Details

stddiff.numeric() is used for numeric variables. stddiff.binary() is used for binomial variables. stddiff.category() is used for categorical variables. stddiff should be less than 0.2.

Value

for stddiff.numeric function

mean.c

the mean of control group

sd.c

the standard deviation of control group

mean.t

the mean of treatment group

sd.t

the standard deviation of treatment group

missing.c

the counts of missing value of control group

missing.t

the counts of missing value of treatment group stddiff: the standardized difference between two groups

stddiff.l

the lower limit of the 95 percentage confidence interval of standardized difference

stddiff.u

the upper limit of the 95 percentage confidence interval of standardized difference

for stddiff.binary function:

p.c

the proportion of last level in the control group

p.t

the proportion of last level in the treatment group

missing.c

the counts of missing value of control group

missing.t

the counts of missing value of treatment group

stddiff

the standardized difference between two groups

stddiff.l

the lower limit of the 95 percentage confidence interval of standardized difference

stddiff.u

the upper limit of the 95 percentage confidence interval of standardized difference

for stddiff.category function:

p.c

the proportion of each level in the control group

p.t

the proportion of each level in the treatment group

missing.c

the counts of missing value of control group

missing.t

the counts of missing value of treatment group

stddiff

the standardized difference between two groups

stddiff.l

the lower limit of the 95 percentage confidence interval of standardized difference

stddiff.u

the upper limit of the 95 percentage confidence interval of standardized difference

Note

nothing

Author(s)

Zhicheng Du<dgdzc@hotmail.com>, Yuantao Hao<haoyt@mail.sysu.edu.cn>

References

Yang DS, Dalton JE. A Unified Approach to Measuring the Effect Size Between Two Groups Using SAS. SAS Global Forum 2012. paper 335

See Also

nothing

Examples

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#set.seed(2016)
#treat=round(abs(rnorm(100)+1)*10,0) %% 2
#numeric=round(abs(rnorm(100)+1)*10,0)
#binary=round(abs(rnorm(100)+1)*10,0) %% 2
#category=round(abs(rnorm(100)+1)*10,0) %% 3
#data=data.frame(treat,numeric,binary,category)
#stddiff.numeric(data=data,gcol=1,vcol=c(2,2))
#stddiff.binary(data=data,gcol=1,vcol=c(3,3))
#stddiff.category(data=data,gcol=1,vcol=c(4,4))

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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