Command for Propensity Score Matching

[Mohamed Eldesouki]

I have a group of 237700 patient with 770 of them being the treatment group. I want to apply propensity score matching to choose 770 from the control group matching with the treatment patients. Then I do the post matching analysis for the outcomes. I did the Logistic and the predict pscore commands, but I have problems with the code for the matching

I would appreciate any help on how to get the right appropriate code.

Attached the dataex for the sample of my observations. Listed 100 out of 77035 observations. PEG is my treatment

* Example generated by -dataex-. For more info, type help dataex
clear
input float PEG int AGE byte(RACE FEMALE) float(ynel1 ynel6 ynel9 ynel10 ynel11)
0 61 1 1 1 0 0 0 1
0 73 1 1 1 0 1 0 1
0 66 2 0 1 0 0 0 0
0 41 1 0 1 0 0 1 0
0 72 1 0 0 0 1 0 1
0 55 6 1 1 0 1 0 1
0 72 2 0 0 0 0 0 0
0 53 3 1 0 0 0 0 0
0 87 1 0 0 0 1 0 1
0 54 2 1 1 0 0 0 1
0 68 1 0 1 0 1 1 0
0 77 1 1 1 0 0 0 0
0 69 1 0 1 0 1 1 0
0 70 1 0 0 0 0 0 0
0 84 1 0 1 0 0 0 0
0 52 1 0 0 0 0 0 1
0 55 1 0 0 0 0 0 0
0 54 1 1 1 0 0 0 0
0 81 1 1 0 0 0 0 1
0 67 1 0 0 0 0 0 0
0 71 1 1 0 0 0 0 0
0 64 4 0 0 0 0 0 0
0 53 1 0 0 0 0 1 0
0 70 2 1 1 0 0 0 1
0 42 2 1 0 0 0 0 1
0 36 1 0 0 1 1 0 0
0 48 2 1 1 0 0 0 0
0 72 2 1 0 0 0 1 0
0 30 1 0 1 0 0 0 0
0  8 3 0 0 0 0 0 0
0 34 6 1 0 0 0 0 0
0 45 3 0 1 0 1 0 0
0 61 1 0 0 0 0 0 1
0 53 3 1 0 0 0 0 0
0 69 1 1 1 0 0 0 1
0 61 2 1 1 0 1 0 0
0 48 1 1 0 0 0 0 1
0 76 6 0 0 0 1 0 1
0 42 2 1 1 0 0 0 1
0 36 2 1 0 0 0 0 0
0 29 3 1 0 0 0 0 0
0 64 1 0 1 0 0 0 1
0 71 1 0 1 0 0 0 1
0 35 1 0 0 0 0 0 1
0 50 1 0 0 0 0 0 1
0 40 1 0 0 0 0 0 0
0 55 1 1 0 0 0 0 0
0 68 1 0 1 0 0 0 0
0 75 1 0 0 0 0 0 0
0 71 1 0 0 0 0 0 1
0 83 1 0 1 0 0 0 1
0 82 1 1 0 0 0 0 1
0 46 1 0 0 0 0 0 1
0 76 4 0 0 0 0 0 0
0 58 1 1 0 0 0 0 1
0 75 1 0 1 0 0 0 0
0 64 4 0 0 0 0 0 0
0 84 1 0 1 0 1 0 0
0 73 4 1 0 0 0 0 0
0 34 2 1 1 0 0 0 0
0 50 1 1 0 0 0 0 0
0 69 1 1 1 0 1 0 0
0 65 1 1 0 0 0 0 0
0 51 1 0 1 0 0 0 1
0 56 3 0 1 0 0 0 1
0 48 2 1 1 0 0 0 0
0 77 1 0 1 0 0 0 0
0 59 2 1 1 0 0 0 1
0 80 1 1 0 0 0 0 0
0 69 1 0 1 0 1 0 1
0 42 6 1 0 0 0 0 0
0 67 1 1 1 0 0 0 0
0 69 1 0 0 0 0 0 0
0 67 1 0 0 0 0 0 1
0 34 1 1 0 0 0 0 1
0 29 1 0 1 0 0 0 1
0 45 1 1 0 0 0 0 1
0 40 1 0 1 0 0 0 1
0 66 1 0 1 0 0 0 0
0 65 1 1 1 0 0 0 1
0 67 1 1 1 0 0 0 1
0 60 3 0 0 0 0 0 1
0 70 2 0 0 0 0 0 1
0 76 1 0 0 0 0 1 0
0 70 1 0 0 0 1 0 1
0 64 1 0 1 0 0 0 0
0 78 1 1 1 0 0 0 0
0 41 2 1 0 0 0 0 0
0  0 1 1 0 0 0 0 0
0 64 1 0 1 0 0 0 1
0 32 1 0 0 0 0 0 0
0 79 4 0 0 0 0 0 1
0 67 1 1 0 0 0 0 1
0 28 1 1 0 0 0 0 0
0 28 1 1 0 0 0 0 0
0 54 6 0 0 0 0 0 1
0 56 2 0 0 0 0 0 1
0  0 1 1 0 0 0 0 0
0 87 1 0 1 0 0 0 0
0 63 2 0 0 0 0 0 1
end
label values ynel1 ynlab
label values ynel6 ynlab
label values ynel9 ynlab
label values ynel10 ynlab
label values ynel11 ynlab
label def ynlab 0 "Absent", modify
label def ynlab 1 "Present", modify
label var AGE "Age in years at admission" 
label var RACE "Race (uniform)" 
label var FEMALE "Indicator of sex" 
label var ynel1 "Congestive Heart Failure" 
label var ynel6 "Hypertension, Uncomplicated" 
label var ynel9 "Chronic Pulmonary Disease" 
label var ynel10 "Diabetes, Uncomplicated" 
label var ynel11 "Diabetes, Complicated"

[Felix Bittmann]

Maybe:

ssc install kmatch, replace
kmatch ps PEG control1 control2 control3 (outcomevar), nn(1) wor comsup

Make sure to study the alternatives of kmatch in detail as this program has so much more to offer. 1:1 matching is outdated, see: https://www.stata.com/meeting/uk17/slides/uk17_Jann.pdf

[Mohamed Eldesouki]

Thank you so much Felix, it was very useful. Now can I do the analysis for the outcome directly? It will be done directly for only the matched patients in the treatment and control? or should I do other commands before starting the analysis?

I have Stata 17 MP

Another point: there are 387 missing observations, will this affect the analysis?



Thank you!