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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