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RE: st: RE: psmatch2 and stcox


From   "Villa Lora, Juan Miguel" <[email protected]>
To   "[email protected]" <[email protected]>
Subject   RE: st: RE: psmatch2 and stcox
Date   Thu, 12 Nov 2009 13:40:04 -0500

Your attention should no be paid on that table. Rather you must check a new variable named _n1 that contains the identification (also _id is generated by the program) of the nearest neighbor. This variable (_n1) must add up 338 observations.
JM

-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of moleps islon
Sent: Jueves, 12 de Noviembre de 2009 11:29 a.m.
To: [email protected]
Subject: Re: st: RE: psmatch2 and stcox

Thx - for the first suggestion. However despite using the noreplacement option I get the same output, ie not 1:1 matching :

xi:psmatch2 treatment `var',noreplacement neighbor(1) common out(dead)


           | psmatch2:
 psmatch2: |   Common
 Treatment |  support
assignment | On suppor |     Total
-----------+-----------+----------
 Untreated |       628 |       628
   Treated |       338 |       338
-----------+-----------+----------
     Total |       966 |       966


Any clues?


Regards,

M




On Thu, Nov 12, 2009 at 3:23 PM, Villa Lora, Juan Miguel <[email protected]> wrote:
> Hi!
> I've got suggestions:
> 1. Try to estimate the propensity score firstly (with a probit or logit) and set the prediction in the option p(your_prediction). This saves you time when running the command several times.
> 2. Specify the option "noreplacement" which yields a 1-1 matching as wanted.
>
> JM.
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of moleps
> islon
> Sent: Martes, 10 de Noviembre de 2009 03:28 p.m.
> To: [email protected]
> Subject: st: psmatch2 and stcox
>
> I´m trying to match 336 exposed patients from a cohort with a total of
> 966 patients using psmatch2:
>
> xi:psmatch2 E `var', outcome(out) neighbor(1) common
>
> I thought this would give me 1:1 matching, but I get 336 exposed and
> 662 control patients.
>
> Then doing
>
> ttest -different variables-, by(_t)
>
> doesnt really match very well despite reasonable overlapping propensity distributions. Is this due to omitting the weighting option?
>
> I´d like to use these propensity matched data for a logrank test, but I´m uncertain to the type of weight I should use. I assumed it should be pweight, but I found another posting here where they said fweight leaving me uncertain....
>
> Regards
>
> M
>
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