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st: RE: R: Simulation using Stata (flag: 9.2/SE version)


From   "Martin Weiss" <martin.weiss1@gmx.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: R: Simulation using Stata (flag: 9.2/SE version)
Date   Sat, 17 Oct 2009 12:09:29 +0200

<>


" Unfortunately, Stata textbooks entirely devoted to this topic haven't be
published, so far."


Microeconometrics Using Stata, by Cameron and Trivedi (2008), is full of
examples for -simulate-...
 
 


HTH
Martin

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Carlo Lazzaro
Sent: Samstag, 17. Oktober 2009 11:24
To: statalist@hsphsun2.harvard.edu
Cc: 'Ali Rowhani-Rahbar'
Subject: st: R: Simulation using Stata (flag: 9.2/SE version)

Dear Ali,
as far as my knowledge is concerned, the only examples of simulations with
Stata (9.2/SE version) are reported in - help simulate -, that you might
want to invoke from within Stata. 
Unfortunately, Stata textbooks entirely devoted to this topic haven't be
published, so far.
However, sticking to your query, performing a probabilistic sensitivity
analysis on the observed OR is straightforward with Stata. 

----------------------------begin example --------------------------------
sysuse nlsw88.dta
logistic union married 
set obs 10000
g Sens_OR= (.7800495 +.0863818 *invnorm(uniform()))
----------------------------end example --------------------------------

In the example sketched above you can draw 10,000 random values from the OR
sampling distribution by simply plugging in the observed OR and its standard
error. 

HTH and Kind Regards,
Carlo

-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Ali
Rowhani-Rahbar
Inviato: sabato 17 ottobre 2009 2.04
A: statalist@hsphsun2.harvard.edu
Oggetto: st: Simulation using Stata

Hello all:

Does anyone know if there is a book (or any other resource) that
introduces simulation and provides practical examples using Stata?
This is mainly for quantifying the impact of change in a particular
predictor of interest on the observed odds ratio or relative risk in a
regression model.

Thank you for your help,
Ali
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