met: Balance evaluation of matched samples
In met: Evaluating and Improving Matched Samples in Observational Studies
Description
Usage
Arguments
Value
References
Examples
Description
Evaluate a matched sample with minimum p-value of multiple measures of imbalance. This function and its use are discussed in Yu (2020).
Usage
1
2
Arguments
x
A matrix with length(z) rows giving the discretized covariates.
z
A vector whose ith coordinate is 1 for a treated unit and is 0 for a control.
method
Balance evaluation methods, can be chosen from 'GFKS-1', 'GFKS-2','SMD','KS','t','wilcoxon'.
prob
If method is chosen as 'GFKS-1'or 'GFKS-2', continuous variables in x are discretized based on prob, i.e., only the quantiles at prob are considered.
continuous
A vector of length dim(x)[2] indicating whether the covariates are discrete (F) or continuous (T). If continuous, GFKS statistics are evaluted at the quantiles at prob.
nperm
Number of simulated randomized experiments to approximate the p-values.
xf
A matrix with length(zf) rows giving the covariates before matching. xf should be specified if method is chosen as 'GFKS-1', 'GFKS-2', 'SMD'.
zf
A vector whose ith coordinate is 1 for a treated unit and is 0 for a control. zf should be specified if method is chosen as 'GFKS-1', 'GFKS-2', 'SMD'.
Value
statistic
A summary statistic to combine all statistics – the minimum p-value.
pvalue
A vector of locations v that the GFKS statistics occur, i.e., the location of the maximum difference of the empirical distribution function in the treated and control groups.
imbalance
A vector indicating which marginal distribution or joint distribution is most imbalanced. Not NULL if method is chosen as 'GFKS-1' or 'GFKS-2'.
location
Location that statistic occur. Not NULL if method is chosen as 'GFKS-1' or 'GFKS-2'.
direction
Direction indicating which quadrant statistic occur. 1 for '<=' and '<=', 2 for '<=' and '>', 3 for '>' and '<=', 4 for '>' and '>'. Not NULL if method is chosen as 'GFKS-1' or 'GFKS-2'.
ind.pvalue
P-values for the observational match.
null.pvalues
P-values for the simulated randomized experiments.
variable
A new binary variable that the next iteration match should balance. Not NULL if method is chosen as 'GFKS-1' or 'GFKS-2'.
References
Yu, R. (2020) Evaluating and Improving a Matched Comparison of Antidepressants and Bone Density. Under revision.
Examples
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library(optmatch)
data("SSRI")
attach(SSRI)
X<-cbind(female,black,education,age,bmi)
dist<-DiPs::maha_dense(z,X)
o<-DiPs::match(z, dist, SSRI)
M0<-o$data
Xm<-cbind(M0$female,M0$black,M0$education,M0$age,M0$bmi)
met(Xm,M0$z,'GFKS-1',continuous=c(FALSE,FALSE,FALSE,TRUE,TRUE),nperm=100,xf=X,zf=z)
detach(SSRI)
#real data application in Yu(2020)
library(DiPs)
data("SSRI")
attach(SSRI)
X<-cbind(age,female,black,hispanic,povertyNA,povertyFill,education,height,weight,
bmi,cotinine,hd,diabetes,insurance,weighmore,weighless,weightchange,physicalact,dietsup)
XX<-cbind(age,female,black,hispanic,povertyNA,povertyFill,education,height,weight,
bmi,cotinine,hd,diabetes,insurance,weighmore,weighless,weightchange,physicalact,dietsup,pr)
detach(SSRI)
dat=SSRI
#basic match
dist<-maha_dense(dat$z,X)
dist<-addcaliper(dist, dat$z, dat$pr, c(-.2,.2), stdev = TRUE, penalty = 1000)
o<-match(dat$z,dist, dat,fine=factor(dat$education), ncontrol = 4)
M0<-o$data
Xm<-subset(M0, select=c('age','female','black','hispanic','povertyNA','povertyFill',
'education','height','weight','bmi','cotinine','hd','diabetes','insurance','weighmore','weighless',
'weightchange','physicalact','dietsup','pr'))
btb0<-check(XX,Xm,dat$z,M0$z)
round(btb0,3)
Result0_t=met(Xm,M0$z,method='t',nperm=2000)
Result0_t$pvalue
Result0_smd=met(Xm,M0$z,method='SMD',nperm=2000,xf=XX,zf=dat$z)
Result0_smd$pvalue
Result0_w=met(Xm,M0$z,method='wilcoxon',nperm=2000)
Result0_w$pvalue
Result0_ks=met(Xm,M0$z,method='KS',nperm=2000)
Result0_ks$pvalue
Result0=met(Xm,M0$z,method='GFKS-2',continuous=c(TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,
TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE),nperm=2000,xf=XX,zf=dat$z)
Result0$statistic
Result0$pvalue
Result0$imbalance
Result0$location
Result0$direction
#iteration 1
dist<-maha_dense(dat$z,X)
dist<-addcaliper(dist, dat$z, dat$pr, c(-.2,.2), stdev = TRUE, penalty = 1000)
i1=as.factor(dat$diabetes<=0 & dat$weighmore<=0)
o<-match(dat$z, dist, dat, fine=as.numeric(factor(dat$education):i1), ncontrol = 4)
M1<-o$data
Xm<-subset(M1, select=c('age','female','black','hispanic','povertyNA','povertyFill',
'education','height','weight','bmi','cotinine','hd','diabetes','insurance','weighmore','weighless',
'weightchange','physicalact','dietsup','pr'))
btb1<-check(XX,Xm,dat$z,M1$z)
round(btb1,3)
Result1_t=met(Xm,M1$z,method='t',nperm=2000)
Result1_t$pvalue
Result1_smd=met(Xm,M1$z,method='SMD',nperm=2000,xf=XX,zf=dat$z)
Result1_smd$pvalue
Result1_w=met(Xm,M1$z,method='wilcoxon',nperm=2000)
Result1_w$pvalue
Result1_ks=met(Xm,M1$z,method='KS',nperm=2000)
Result1_ks$pvalue
Result1=met(Xm,M1$z,method='GFKS-2',continuous=c(TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,
TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE),nperm=2000,xf=XX,zf=dat$z)
Result1$statistic
Result1$pvalue
Result1$imbalance
Result1$location
Result1$direction
#iteration 2
dist<-maha_dense(dat$z,X)
dist<-addcaliper(dist, dat$z, dat$pr, c(-.2,.2), stdev = TRUE, penalty = 1000)
i2=as.factor(dat$weightchange<=quantile(dat$weightchange[dat$z==1],probs=0.65) &
as.numeric(dat$pr)<=quantile(dat$pr[dat$z==1],probs=0.89))
o<-match(dat$z, dist, dat, fine=as.numeric(factor(dat$education):i1:i2), ncontrol = 4)
M2<-o$data
Xm<-subset(M2, select=c('age','female','black','hispanic','povertyNA','povertyFill',
'education','height','weight','bmi','cotinine','hd','diabetes','insurance','weighmore','weighless',
'weightchange','physicalact','dietsup','pr'))
btb2<-check(XX,Xm,dat$z,M2$z)
round(btb2,3)
Result2_t=met(Xm,M2$z,method='t',nperm=2000)
Result2_t$pvalue
Result2_smd=met(Xm,M2$z,method='SMD',nperm=2000,xf=XX,zf=dat$z)
Result2_smd$pvalue
Result2_w=met(Xm,M2$z,method='wilcoxon',nperm=2000)
Result2_w$pvalue
Result2_ks=met(Xm,M2$z,method='KS',nperm=2000)
Result2_ks$pvalue
Result2=met(Xm,M2$z,method='GFKS-2',continuous=c(TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,
TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE),nperm=2000,xf=XX,zf=dat$z)
Result2$statistic
Result2$pvalue
Result2$imbalance
Result2$location
Result2$direction
#iteration 3
dist<-maha_dense(dat$z,X)
dist<-addcaliper(dist, dat$z, dat$pr, c(-.2,.2), stdev = TRUE, penalty = 1000)
i3=as.factor(dat$weightchange<=quantile(dat$weightchange[dat$z==1],probs=0.35))
o<-match(dat$z, dist, dat, fine=as.numeric(factor(dat$education):i1:i2:i3), ncontrol = 4)
M3<-o$data
Xm<-subset(M3, select=c('age','female','black','hispanic','povertyNA','povertyFill',
'education','height','weight','bmi','cotinine','hd','diabetes','insurance','weighmore','weighless',
'weightchange','physicalact','dietsup','pr'))
btb3<-check(XX,Xm,dat$z,M3$z)
round(btb3,3)
Result3_t=met(Xm,M3$z,method='t',nperm=2000)
Result3_t$pvalue
Result3_smd=met(Xm,M3$z,method='SMD',nperm=2000,xf=XX,zf=dat$z)
Result3_smd$pvalue
Result3_w=met(Xm,M3$z,method='wilcoxon',nperm=2000)
Result3_w$pvalue
Result3_ks=met(Xm,M3$z,method='KS',nperm=2000)
Result3_ks$pvalue
Result3=met(Xm,M3$z,method='GFKS-2',continuous=c(TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,
TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE),nperm=2000,xf=XX,zf=dat$z)
Result3$statistic
Result3$pvalue
Result3$imbalance
Result3$location
Result3$direction
met documentation built on Aug. 14, 2020, 5:07 p.m.
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Description Usage Arguments Value References Examples
Description
Evaluate a matched sample with minimum p-value of multiple measures of imbalance. This function and its use are discussed in Yu (2020).
Usage
1 2 |
Arguments
x |
A matrix with length(z) rows giving the discretized covariates. |
z |
A vector whose ith coordinate is 1 for a treated unit and is 0 for a control. |
method |
Balance evaluation methods, can be chosen from 'GFKS-1', 'GFKS-2','SMD','KS','t','wilcoxon'. |
prob |
If method is chosen as 'GFKS-1'or 'GFKS-2', continuous variables in x are discretized based on prob, i.e., only the quantiles at prob are considered. |
continuous |
A vector of length dim(x)[2] indicating whether the covariates are discrete (F) or continuous (T). If continuous, GFKS statistics are evaluted at the quantiles at prob. |
nperm |
Number of simulated randomized experiments to approximate the p-values. |
xf |
A matrix with length(zf) rows giving the covariates before matching. xf should be specified if method is chosen as 'GFKS-1', 'GFKS-2', 'SMD'. |
zf |
A vector whose ith coordinate is 1 for a treated unit and is 0 for a control. zf should be specified if method is chosen as 'GFKS-1', 'GFKS-2', 'SMD'. |
Value
statistic |
A summary statistic to combine all statistics – the minimum p-value. |
pvalue |
A vector of locations v that the GFKS statistics occur, i.e., the location of the maximum difference of the empirical distribution function in the treated and control groups. |
imbalance |
A vector indicating which marginal distribution or joint distribution is most imbalanced. Not NULL if method is chosen as 'GFKS-1' or 'GFKS-2'. |
location |
Location that statistic occur. Not NULL if method is chosen as 'GFKS-1' or 'GFKS-2'. |
direction |
Direction indicating which quadrant statistic occur. 1 for '<=' and '<=', 2 for '<=' and '>', 3 for '>' and '<=', 4 for '>' and '>'. Not NULL if method is chosen as 'GFKS-1' or 'GFKS-2'. |
ind.pvalue |
P-values for the observational match. |
null.pvalues |
P-values for the simulated randomized experiments. |
variable |
A new binary variable that the next iteration match should balance. Not NULL if method is chosen as 'GFKS-1' or 'GFKS-2'. |
References
Yu, R. (2020) Evaluating and Improving a Matched Comparison of Antidepressants and Bone Density. Under revision.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | library(optmatch)
data("SSRI")
attach(SSRI)
X<-cbind(female,black,education,age,bmi)
dist<-DiPs::maha_dense(z,X)
o<-DiPs::match(z, dist, SSRI)
M0<-o$data
Xm<-cbind(M0$female,M0$black,M0$education,M0$age,M0$bmi)
met(Xm,M0$z,'GFKS-1',continuous=c(FALSE,FALSE,FALSE,TRUE,TRUE),nperm=100,xf=X,zf=z)
detach(SSRI)
#real data application in Yu(2020)
library(DiPs)
data("SSRI")
attach(SSRI)
X<-cbind(age,female,black,hispanic,povertyNA,povertyFill,education,height,weight,
bmi,cotinine,hd,diabetes,insurance,weighmore,weighless,weightchange,physicalact,dietsup)
XX<-cbind(age,female,black,hispanic,povertyNA,povertyFill,education,height,weight,
bmi,cotinine,hd,diabetes,insurance,weighmore,weighless,weightchange,physicalact,dietsup,pr)
detach(SSRI)
dat=SSRI
#basic match
dist<-maha_dense(dat$z,X)
dist<-addcaliper(dist, dat$z, dat$pr, c(-.2,.2), stdev = TRUE, penalty = 1000)
o<-match(dat$z,dist, dat,fine=factor(dat$education), ncontrol = 4)
M0<-o$data
Xm<-subset(M0, select=c('age','female','black','hispanic','povertyNA','povertyFill',
'education','height','weight','bmi','cotinine','hd','diabetes','insurance','weighmore','weighless',
'weightchange','physicalact','dietsup','pr'))
btb0<-check(XX,Xm,dat$z,M0$z)
round(btb0,3)
Result0_t=met(Xm,M0$z,method='t',nperm=2000)
Result0_t$pvalue
Result0_smd=met(Xm,M0$z,method='SMD',nperm=2000,xf=XX,zf=dat$z)
Result0_smd$pvalue
Result0_w=met(Xm,M0$z,method='wilcoxon',nperm=2000)
Result0_w$pvalue
Result0_ks=met(Xm,M0$z,method='KS',nperm=2000)
Result0_ks$pvalue
Result0=met(Xm,M0$z,method='GFKS-2',continuous=c(TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,
TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE),nperm=2000,xf=XX,zf=dat$z)
Result0$statistic
Result0$pvalue
Result0$imbalance
Result0$location
Result0$direction
#iteration 1
dist<-maha_dense(dat$z,X)
dist<-addcaliper(dist, dat$z, dat$pr, c(-.2,.2), stdev = TRUE, penalty = 1000)
i1=as.factor(dat$diabetes<=0 & dat$weighmore<=0)
o<-match(dat$z, dist, dat, fine=as.numeric(factor(dat$education):i1), ncontrol = 4)
M1<-o$data
Xm<-subset(M1, select=c('age','female','black','hispanic','povertyNA','povertyFill',
'education','height','weight','bmi','cotinine','hd','diabetes','insurance','weighmore','weighless',
'weightchange','physicalact','dietsup','pr'))
btb1<-check(XX,Xm,dat$z,M1$z)
round(btb1,3)
Result1_t=met(Xm,M1$z,method='t',nperm=2000)
Result1_t$pvalue
Result1_smd=met(Xm,M1$z,method='SMD',nperm=2000,xf=XX,zf=dat$z)
Result1_smd$pvalue
Result1_w=met(Xm,M1$z,method='wilcoxon',nperm=2000)
Result1_w$pvalue
Result1_ks=met(Xm,M1$z,method='KS',nperm=2000)
Result1_ks$pvalue
Result1=met(Xm,M1$z,method='GFKS-2',continuous=c(TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,
TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE),nperm=2000,xf=XX,zf=dat$z)
Result1$statistic
Result1$pvalue
Result1$imbalance
Result1$location
Result1$direction
#iteration 2
dist<-maha_dense(dat$z,X)
dist<-addcaliper(dist, dat$z, dat$pr, c(-.2,.2), stdev = TRUE, penalty = 1000)
i2=as.factor(dat$weightchange<=quantile(dat$weightchange[dat$z==1],probs=0.65) &
as.numeric(dat$pr)<=quantile(dat$pr[dat$z==1],probs=0.89))
o<-match(dat$z, dist, dat, fine=as.numeric(factor(dat$education):i1:i2), ncontrol = 4)
M2<-o$data
Xm<-subset(M2, select=c('age','female','black','hispanic','povertyNA','povertyFill',
'education','height','weight','bmi','cotinine','hd','diabetes','insurance','weighmore','weighless',
'weightchange','physicalact','dietsup','pr'))
btb2<-check(XX,Xm,dat$z,M2$z)
round(btb2,3)
Result2_t=met(Xm,M2$z,method='t',nperm=2000)
Result2_t$pvalue
Result2_smd=met(Xm,M2$z,method='SMD',nperm=2000,xf=XX,zf=dat$z)
Result2_smd$pvalue
Result2_w=met(Xm,M2$z,method='wilcoxon',nperm=2000)
Result2_w$pvalue
Result2_ks=met(Xm,M2$z,method='KS',nperm=2000)
Result2_ks$pvalue
Result2=met(Xm,M2$z,method='GFKS-2',continuous=c(TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,
TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE),nperm=2000,xf=XX,zf=dat$z)
Result2$statistic
Result2$pvalue
Result2$imbalance
Result2$location
Result2$direction
#iteration 3
dist<-maha_dense(dat$z,X)
dist<-addcaliper(dist, dat$z, dat$pr, c(-.2,.2), stdev = TRUE, penalty = 1000)
i3=as.factor(dat$weightchange<=quantile(dat$weightchange[dat$z==1],probs=0.35))
o<-match(dat$z, dist, dat, fine=as.numeric(factor(dat$education):i1:i2:i3), ncontrol = 4)
M3<-o$data
Xm<-subset(M3, select=c('age','female','black','hispanic','povertyNA','povertyFill',
'education','height','weight','bmi','cotinine','hd','diabetes','insurance','weighmore','weighless',
'weightchange','physicalact','dietsup','pr'))
btb3<-check(XX,Xm,dat$z,M3$z)
round(btb3,3)
Result3_t=met(Xm,M3$z,method='t',nperm=2000)
Result3_t$pvalue
Result3_smd=met(Xm,M3$z,method='SMD',nperm=2000,xf=XX,zf=dat$z)
Result3_smd$pvalue
Result3_w=met(Xm,M3$z,method='wilcoxon',nperm=2000)
Result3_w$pvalue
Result3_ks=met(Xm,M3$z,method='KS',nperm=2000)
Result3_ks$pvalue
Result3=met(Xm,M3$z,method='GFKS-2',continuous=c(TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,
TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,TRUE),nperm=2000,xf=XX,zf=dat$z)
Result3$statistic
Result3$pvalue
Result3$imbalance
Result3$location
Result3$direction
|
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