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st: propensity score (diff group sizes: treatment >>> control)

From   <>
To   <>
Subject   st: propensity score (diff group sizes: treatment >>> control)
Date   Tue, 18 Feb 2014 17:04:03 +0000

Dear list,

I am evaluating an intervention for with I have a control group (N=40) and a treatment group (N=500).
I am using propensity score matching to match the two groups by sociodemographics (age, gender, living status).
I am considering two methods and I would be delighted to receive advices from anybody having encountered the same problem.

1/ First method
I calculated the pscore, and then performed the PSM (Kernel method).

pscore Group Age Gender Living, pscore(myscore) blockid(myblock)

psmatch2 Group, outcome(GP_Times) pscore(myscore) kernel(normal)

2/ Second method
Due to the high difference in the observations and the fact that my treatment is now the more numerous, I tried to inverse the groups.
I created a new variable ('Group_opposite') with control group (N=500) and treatment group (N=40), then I calculated the new pscore, and finally I performed the PSM (Kernel method).

pscore Group Age Gender Living, pscore(myscore2) blockid(myblock2)

psmatch2 Group_opposite, outcome(GP_Times) pscore(myscore2) kernel(normal)

The values calculated using the second pscore seems to be more conservative that the first ones, and the results more acceptable.
Would be right to use the second method instead of the first one?

Looking forward for your advices, many thanks in advance,

Best wishes,


Valentina Iemmi | Research Officer 
London School of Economics and Political Science | Personal Social Services Research Unit - PSSRU
Houghton Street | London WC2A 2AE 

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