balance: Check standardized mean differences (SMDs) of the matched...
In RBestMatch: Graded Matching for Large Observational Studies
Description
Usage
Arguments
Value
References
Examples
Description
The function is used to create a table of mean and SMDs to check the balance before and after matching.
Usage
1
balance(fdata, mdata, fz, mz)
Arguments
fdata
A full data frame with length(fz) rows and columns being variables that need to check SMDs. fdata and mdata must have the same variables with same column names in the same order.
mdata
A matched data frame with length(mz) rows and columns being variables that need to check SMDs. fdata and mdata must have the same variables with same column names in the same order.
fz
A vector whose ith coordinate is 1 for a treated unit and is 0 for a control for subjects in the full data set.
mz
A vector whose ith coordinate is 1 for a treated unit and is 0 for a control for subjects in the matched data set.
Value
A matrix with one row for each variable and six columns being the mean of treated group, mean of full control group, mean of matched treated group, mean of matched control group, SMD of matched control group and SMD of full control group.
References
Rosenbaum, P. R. (2010) Design of Observational Studies. New York: Springer.
Examples
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# To run this example, you MUST install the optmatch package.
data(rhc)
z=as.numeric(rhc$swang1=='RHC')
attach(rhc)
female=as.numeric(sex=="Female")
race_black=as.numeric(race=="black")
race_other=as.numeric(race=="other")
income1=as.numeric(income=='$11-$25k')
income2=as.numeric(income=='$25-$50k')
income3=as.numeric(income=='> $50k')
ins_care=as.numeric(ninsclas=='Medicare')
ins_pcare=as.numeric(ninsclas=='Private & Medicare')
ins_caid=as.numeric(ninsclas=='Medicaid')
ins_no=as.numeric(ninsclas=='No insurance')
ins_carecaid=as.numeric(ninsclas=='Medicare & Medicaid')
cat1_copd=as.numeric(cat1=='COPD')
cat1_mosfsep=as.numeric(cat1=='MOSF w/Sepsis')
cat1_mosfmal=as.numeric(cat1=='MOSF w/Malignancy')
cat1_chf=as.numeric(cat1=='CHF')
cat1_coma=as.numeric(cat1=='Coma')
cat1_cirr=as.numeric(cat1=='Cirrhosis')
cat1_lung=as.numeric(cat1=='Lung Cancer')
cat1_colon=as.numeric(cat1=='Colon Cancer')
cat2_mosfsep=as.numeric(match(cat2,'MOSF w/Sepsis',nomatch = 0)>0)
cat2_coma=as.numeric(match(cat2,'Coma',nomatch = 0)>0)
cat2_mosfmal=as.numeric(match(cat2,'MOSF w/Malignancy',nomatch = 0)>0)
cat2_lung=as.numeric(match(cat2,'Lung Cancer',nomatch = 0)>0)
cat2_cirr=as.numeric(match(cat2,'Cirrhosis',nomatch = 0)>0)
cat2_colon=as.numeric(match(cat2,'Colon Cancer',nomatch = 0)>0)
adld3p_na=as.numeric(is.na(adld3p))
adld3p_impute=adld3p
adld3p_impute[is.na(adld3p)]=mean(adld3p,na.rm=TRUE)
ca_yes=as.numeric(ca=='Yes')
ca_meta=as.numeric(ca=='Metastatic')
wt0=as.numeric(wtkilo1==0)
urin1_na=as.numeric(is.na(urin1))
urin1_impute=urin1
urin1_impute[is.na(urin1)]=mean(urin1,na.rm=TRUE)
NumComorbid=cardiohx+chfhx+dementhx+psychhx+chrpulhx+renalhx+liverhx+
gibledhx+malighx+immunhx+transhx+amihx
Resp=as.numeric(resp=='Yes')
Card=as.numeric(card=='Yes')
Neuro=as.numeric(neuro=='Yes')
Gastr=as.numeric(gastr=='Yes')
Renal=as.numeric(renal=='Yes')
Meta=as.numeric(meta=='Yes')
Hema=as.numeric(hema=='Yes')
Seps=as.numeric(seps=='Yes')
Trauma=as.numeric(trauma=='Yes')
Ortho=as.numeric(ortho=='Yes')
Dnr1=as.numeric(dnr1=='Yes')
pr<-glm(z~age+female+race_black+race_other+edu+income1+income2+income3+
ins_care+ins_pcare+ins_caid+ins_no+ins_carecaid+
cat1_copd+cat1_mosfsep+cat1_mosfmal+cat1_chf+cat1_coma+cat1_cirr+cat1_lung+cat1_colon+
cat2_mosfsep+cat2_coma+cat2_mosfmal+cat2_lung+cat2_cirr+cat2_colon+
Resp+Card+Neuro+Gastr+Renal+Meta+Hema+Seps+Trauma+Ortho+
adld3p_impute+adld3p_na+das2d3pc+Dnr1+ca_yes+ca_meta+surv2md1+aps1+scoma1+
wtkilo1+wt0+temp1+meanbp1+resp1+hrt1+pafi1+paco21+ph1+wblc1+hema1+
sod1+pot1+crea1+bili1+alb1+urin1_impute+urin1_na+
cardiohx+chfhx+dementhx+psychhx+chrpulhx+renalhx+liverhx+
gibledhx+malighx+immunhx+transhx+amihx,family=binomial)$fitted.values
X<-cbind(aps1,surv2md1,age,NumComorbid,adld3p_impute,adld3p_na,das2d3pc,temp1,hrt1,meanbp1,
resp1,wblc1,pafi1,paco21,ph1,crea1,alb1,scoma1,
cat1_copd,cat1_mosfsep,cat1_mosfmal,cat1_chf,cat1_coma,cat1_cirr,cat1_lung,cat1_colon)
XX=cbind(X,pr)
Xfull<-cbind(age,female,race_black,race_other,edu,income1,income2,income3,
ins_care,ins_pcare,ins_caid,ins_no,ins_carecaid,
cat1_copd,cat1_mosfsep,cat1_mosfmal,cat1_chf,cat1_coma,cat1_cirr,cat1_lung,cat1_colon,
cat2_mosfsep,cat2_coma,cat2_mosfmal,cat2_lung,cat2_cirr,cat2_colon,
Resp,Card,Neuro,Gastr,Renal,Meta,Hema,Seps,Trauma,Ortho,
adld3p_impute,adld3p_na,das2d3pc,Dnr1,ca_yes,ca_meta,surv2md1,aps1,scoma1,
wtkilo1,wt0,temp1,meanbp1,resp1,hrt1,pafi1,paco21,ph1,wblc1,hema1,
sod1,pot1,crea1,bili1,alb1,urin1_impute,urin1_na,
cardiohx,chfhx,dementhx,psychhx,chrpulhx,renalhx,liverhx,
gibledhx,malighx,immunhx,transhx,amihx)
XXf=cbind(Xfull,pr)
pr_st4=as.integer(cut(pr,quantile(pr,c(0,.16,.5,.84,1)),include.lowest = TRUE))
detach(rhc)
data<-cbind(rhc,cbind(pr,pr_st4,pr_st4,z,female,race_black,race_other,income1,income2,income3,
ins_care,ins_pcare,ins_caid,ins_no,ins_carecaid,cat1_copd,cat1_mosfsep,
cat1_mosfmal,cat1_chf,cat1_coma,cat1_cirr,cat1_lung,cat1_colon,
cat2_mosfsep,cat2_coma,cat2_mosfmal,cat2_lung,cat2_cirr,cat2_colon,
adld3p_na,adld3p_impute,ca_yes,ca_meta,wt0,urin1_na,urin1_impute,NumComorbid,
Resp,Card,Neuro,Gastr,Renal,Meta,Hema,Seps,Trauma,Ortho,Dnr1))
rm(pr,pr_st4,z,female,race_black,race_other,income1,income2,income3,
ins_care,ins_pcare,ins_caid,ins_no,ins_carecaid,cat1_copd,cat1_mosfsep,
cat1_mosfmal,cat1_chf,cat1_coma,cat1_cirr,cat1_lung,cat1_colon,
cat2_mosfsep,cat2_coma,cat2_mosfmal,cat2_lung,cat2_cirr,cat2_colon,
adld3p_na,adld3p_impute,ca_yes,ca_meta,wt0,urin1_na,urin1_impute,NumComorbid,
Resp,Card,Neuro,Gastr,Renal,Meta,Hema,Seps,Trauma,Ortho,Dnr1)
data$id=numeric(nrow(data))
data$id[which(data$z==1)]=1:sum(data$z)
data$id[which(data$z==0)]=(sum(data$z)+1):nrow(data)
data$cat2[is.na(data$cat2)]='Missing'
##r best matching
fbv=as.factor(data$pr_st4):as.factor(data$cat1)
tb=table(data$z,fbv)
result=rbestmatch(z=data$z,data=data,Xvar=Xfull,Pvar=X,score=data$pr,fine=fbv,
calipers=c(0.01,0.02,0.03,0.03,1),cutdist=c(15.5741,24.2996,30.0723,37.1716,Inf),
nranks=4,ncontrol=1,rank=TRUE,s.cost=10,rpenalty=50)
result$nmatchedpairs
tb_match=table(result$rank_after)
tb_match
tb_match/sum(tb_match)
tb_edge=table(result$rank_before)
tb_edge[5]=sum(data$z)*sum(1-data$z)-sum(tb_edge[1:4])
tb_edge
tb_edge/sum(tb_edge)
mdata=result$data
Xmf=mdata[,c('age','female','race_black','race_other','edu','income1','income2','income3',
'ins_care','ins_pcare','ins_caid','ins_no','ins_carecaid','cat1_copd',
'cat1_mosfsep','cat1_mosfmal','cat1_chf','cat1_coma','cat1_cirr','cat1_lung',
'cat1_colon','cat2_mosfsep','cat2_coma','cat2_mosfmal','cat2_lung','cat2_cirr',
'cat2_colon','Resp','Card','Neuro','Gastr','Renal','Meta','Hema','Seps',
'Trauma','Ortho','adld3p_impute','adld3p_na','das2d3pc','Dnr1','ca_yes',
'ca_meta','surv2md1','aps1','scoma1','wtkilo1','wt0','temp1','meanbp1','resp1',
'hrt1','pafi1','paco21','ph1','wblc1','hema1','sod1','pot1','crea1','bili1',
'alb1','urin1_impute','urin1_na','cardiohx','chfhx','dementhx','psychhx',
'chrpulhx','renalhx','liverhx','gibledhx','malighx','immunhx','transhx','amihx',
'pr')]
btb_f=balance(XXf,Xmf,data$z,mdata$z)
btb_f
##4 largest absolute SMD
sort(abs(btb_f[,5]),decreasing = TRUE)[1:4]
###fine balance
tb=table(mdata$z,mdata$pr_st4,mdata$cat1)
finebalance_table=cbind(
rbind(tb[2,,1],tb[1,,1],tb[2,,2],tb[1,,2],tb[2,,3],tb[1,,3],
tb[2,,4],tb[1,,4],tb[2,,5],tb[1,,5],tb[2,,6],tb[1,,6],
tb[2,,7],tb[1,,7],tb[2,,8],tb[1,,8],tb[2,,9],tb[1,,9]))
finebalance_table
RBestMatch documentation built on Dec. 21, 2021, 9:07 a.m.
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Description Usage Arguments Value References Examples
Description
The function is used to create a table of mean and SMDs to check the balance before and after matching.
Usage
1 | balance(fdata, mdata, fz, mz)
|
Arguments
fdata |
A full data frame with length(fz) rows and columns being variables that need to check SMDs. fdata and mdata must have the same variables with same column names in the same order. |
mdata |
A matched data frame with length(mz) rows and columns being variables that need to check SMDs. fdata and mdata must have the same variables with same column names in the same order. |
fz |
A vector whose ith coordinate is 1 for a treated unit and is 0 for a control for subjects in the full data set. |
mz |
A vector whose ith coordinate is 1 for a treated unit and is 0 for a control for subjects in the matched data set. |
Value
A matrix with one row for each variable and six columns being the mean of treated group, mean of full control group, mean of matched treated group, mean of matched control group, SMD of matched control group and SMD of full control group.
References
Rosenbaum, P. R. (2010) Design of Observational Studies. New York: Springer.
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 | # To run this example, you MUST install the optmatch package.
data(rhc)
z=as.numeric(rhc$swang1=='RHC')
attach(rhc)
female=as.numeric(sex=="Female")
race_black=as.numeric(race=="black")
race_other=as.numeric(race=="other")
income1=as.numeric(income=='$11-$25k')
income2=as.numeric(income=='$25-$50k')
income3=as.numeric(income=='> $50k')
ins_care=as.numeric(ninsclas=='Medicare')
ins_pcare=as.numeric(ninsclas=='Private & Medicare')
ins_caid=as.numeric(ninsclas=='Medicaid')
ins_no=as.numeric(ninsclas=='No insurance')
ins_carecaid=as.numeric(ninsclas=='Medicare & Medicaid')
cat1_copd=as.numeric(cat1=='COPD')
cat1_mosfsep=as.numeric(cat1=='MOSF w/Sepsis')
cat1_mosfmal=as.numeric(cat1=='MOSF w/Malignancy')
cat1_chf=as.numeric(cat1=='CHF')
cat1_coma=as.numeric(cat1=='Coma')
cat1_cirr=as.numeric(cat1=='Cirrhosis')
cat1_lung=as.numeric(cat1=='Lung Cancer')
cat1_colon=as.numeric(cat1=='Colon Cancer')
cat2_mosfsep=as.numeric(match(cat2,'MOSF w/Sepsis',nomatch = 0)>0)
cat2_coma=as.numeric(match(cat2,'Coma',nomatch = 0)>0)
cat2_mosfmal=as.numeric(match(cat2,'MOSF w/Malignancy',nomatch = 0)>0)
cat2_lung=as.numeric(match(cat2,'Lung Cancer',nomatch = 0)>0)
cat2_cirr=as.numeric(match(cat2,'Cirrhosis',nomatch = 0)>0)
cat2_colon=as.numeric(match(cat2,'Colon Cancer',nomatch = 0)>0)
adld3p_na=as.numeric(is.na(adld3p))
adld3p_impute=adld3p
adld3p_impute[is.na(adld3p)]=mean(adld3p,na.rm=TRUE)
ca_yes=as.numeric(ca=='Yes')
ca_meta=as.numeric(ca=='Metastatic')
wt0=as.numeric(wtkilo1==0)
urin1_na=as.numeric(is.na(urin1))
urin1_impute=urin1
urin1_impute[is.na(urin1)]=mean(urin1,na.rm=TRUE)
NumComorbid=cardiohx+chfhx+dementhx+psychhx+chrpulhx+renalhx+liverhx+
gibledhx+malighx+immunhx+transhx+amihx
Resp=as.numeric(resp=='Yes')
Card=as.numeric(card=='Yes')
Neuro=as.numeric(neuro=='Yes')
Gastr=as.numeric(gastr=='Yes')
Renal=as.numeric(renal=='Yes')
Meta=as.numeric(meta=='Yes')
Hema=as.numeric(hema=='Yes')
Seps=as.numeric(seps=='Yes')
Trauma=as.numeric(trauma=='Yes')
Ortho=as.numeric(ortho=='Yes')
Dnr1=as.numeric(dnr1=='Yes')
pr<-glm(z~age+female+race_black+race_other+edu+income1+income2+income3+
ins_care+ins_pcare+ins_caid+ins_no+ins_carecaid+
cat1_copd+cat1_mosfsep+cat1_mosfmal+cat1_chf+cat1_coma+cat1_cirr+cat1_lung+cat1_colon+
cat2_mosfsep+cat2_coma+cat2_mosfmal+cat2_lung+cat2_cirr+cat2_colon+
Resp+Card+Neuro+Gastr+Renal+Meta+Hema+Seps+Trauma+Ortho+
adld3p_impute+adld3p_na+das2d3pc+Dnr1+ca_yes+ca_meta+surv2md1+aps1+scoma1+
wtkilo1+wt0+temp1+meanbp1+resp1+hrt1+pafi1+paco21+ph1+wblc1+hema1+
sod1+pot1+crea1+bili1+alb1+urin1_impute+urin1_na+
cardiohx+chfhx+dementhx+psychhx+chrpulhx+renalhx+liverhx+
gibledhx+malighx+immunhx+transhx+amihx,family=binomial)$fitted.values
X<-cbind(aps1,surv2md1,age,NumComorbid,adld3p_impute,adld3p_na,das2d3pc,temp1,hrt1,meanbp1,
resp1,wblc1,pafi1,paco21,ph1,crea1,alb1,scoma1,
cat1_copd,cat1_mosfsep,cat1_mosfmal,cat1_chf,cat1_coma,cat1_cirr,cat1_lung,cat1_colon)
XX=cbind(X,pr)
Xfull<-cbind(age,female,race_black,race_other,edu,income1,income2,income3,
ins_care,ins_pcare,ins_caid,ins_no,ins_carecaid,
cat1_copd,cat1_mosfsep,cat1_mosfmal,cat1_chf,cat1_coma,cat1_cirr,cat1_lung,cat1_colon,
cat2_mosfsep,cat2_coma,cat2_mosfmal,cat2_lung,cat2_cirr,cat2_colon,
Resp,Card,Neuro,Gastr,Renal,Meta,Hema,Seps,Trauma,Ortho,
adld3p_impute,adld3p_na,das2d3pc,Dnr1,ca_yes,ca_meta,surv2md1,aps1,scoma1,
wtkilo1,wt0,temp1,meanbp1,resp1,hrt1,pafi1,paco21,ph1,wblc1,hema1,
sod1,pot1,crea1,bili1,alb1,urin1_impute,urin1_na,
cardiohx,chfhx,dementhx,psychhx,chrpulhx,renalhx,liverhx,
gibledhx,malighx,immunhx,transhx,amihx)
XXf=cbind(Xfull,pr)
pr_st4=as.integer(cut(pr,quantile(pr,c(0,.16,.5,.84,1)),include.lowest = TRUE))
detach(rhc)
data<-cbind(rhc,cbind(pr,pr_st4,pr_st4,z,female,race_black,race_other,income1,income2,income3,
ins_care,ins_pcare,ins_caid,ins_no,ins_carecaid,cat1_copd,cat1_mosfsep,
cat1_mosfmal,cat1_chf,cat1_coma,cat1_cirr,cat1_lung,cat1_colon,
cat2_mosfsep,cat2_coma,cat2_mosfmal,cat2_lung,cat2_cirr,cat2_colon,
adld3p_na,adld3p_impute,ca_yes,ca_meta,wt0,urin1_na,urin1_impute,NumComorbid,
Resp,Card,Neuro,Gastr,Renal,Meta,Hema,Seps,Trauma,Ortho,Dnr1))
rm(pr,pr_st4,z,female,race_black,race_other,income1,income2,income3,
ins_care,ins_pcare,ins_caid,ins_no,ins_carecaid,cat1_copd,cat1_mosfsep,
cat1_mosfmal,cat1_chf,cat1_coma,cat1_cirr,cat1_lung,cat1_colon,
cat2_mosfsep,cat2_coma,cat2_mosfmal,cat2_lung,cat2_cirr,cat2_colon,
adld3p_na,adld3p_impute,ca_yes,ca_meta,wt0,urin1_na,urin1_impute,NumComorbid,
Resp,Card,Neuro,Gastr,Renal,Meta,Hema,Seps,Trauma,Ortho,Dnr1)
data$id=numeric(nrow(data))
data$id[which(data$z==1)]=1:sum(data$z)
data$id[which(data$z==0)]=(sum(data$z)+1):nrow(data)
data$cat2[is.na(data$cat2)]='Missing'
##r best matching
fbv=as.factor(data$pr_st4):as.factor(data$cat1)
tb=table(data$z,fbv)
result=rbestmatch(z=data$z,data=data,Xvar=Xfull,Pvar=X,score=data$pr,fine=fbv,
calipers=c(0.01,0.02,0.03,0.03,1),cutdist=c(15.5741,24.2996,30.0723,37.1716,Inf),
nranks=4,ncontrol=1,rank=TRUE,s.cost=10,rpenalty=50)
result$nmatchedpairs
tb_match=table(result$rank_after)
tb_match
tb_match/sum(tb_match)
tb_edge=table(result$rank_before)
tb_edge[5]=sum(data$z)*sum(1-data$z)-sum(tb_edge[1:4])
tb_edge
tb_edge/sum(tb_edge)
mdata=result$data
Xmf=mdata[,c('age','female','race_black','race_other','edu','income1','income2','income3',
'ins_care','ins_pcare','ins_caid','ins_no','ins_carecaid','cat1_copd',
'cat1_mosfsep','cat1_mosfmal','cat1_chf','cat1_coma','cat1_cirr','cat1_lung',
'cat1_colon','cat2_mosfsep','cat2_coma','cat2_mosfmal','cat2_lung','cat2_cirr',
'cat2_colon','Resp','Card','Neuro','Gastr','Renal','Meta','Hema','Seps',
'Trauma','Ortho','adld3p_impute','adld3p_na','das2d3pc','Dnr1','ca_yes',
'ca_meta','surv2md1','aps1','scoma1','wtkilo1','wt0','temp1','meanbp1','resp1',
'hrt1','pafi1','paco21','ph1','wblc1','hema1','sod1','pot1','crea1','bili1',
'alb1','urin1_impute','urin1_na','cardiohx','chfhx','dementhx','psychhx',
'chrpulhx','renalhx','liverhx','gibledhx','malighx','immunhx','transhx','amihx',
'pr')]
btb_f=balance(XXf,Xmf,data$z,mdata$z)
btb_f
##4 largest absolute SMD
sort(abs(btb_f[,5]),decreasing = TRUE)[1:4]
###fine balance
tb=table(mdata$z,mdata$pr_st4,mdata$cat1)
finebalance_table=cbind(
rbind(tb[2,,1],tb[1,,1],tb[2,,2],tb[1,,2],tb[2,,3],tb[1,,3],
tb[2,,4],tb[1,,4],tb[2,,5],tb[1,,5],tb[2,,6],tb[1,,6],
tb[2,,7],tb[1,,7],tb[2,,8],tb[1,,8],tb[2,,9],tb[1,,9]))
finebalance_table
|
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