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st: Epidemiological Confounder Selection

From   "Salah Mahmud" <>
To   <>
Subject   st: Epidemiological Confounder Selection
Date   Wed, 30 Jul 2003 10:49:51 -0400

Dear all,

Does anyone know of a Stata routine to perform confounder selection
using a selected change-in-estimate criterion and backwards deletion?

What?! I hear you say. In the rest of this message, the initiator of
this request, a friend and colleague, describes precisely what that
entails. Until now, she had done all of that "manually" in Stata and she
wonders if there is an easier way. Notice that the selection criterion
used is change in relative risk estimates and not change in P value (as
epiconf.ado does).

Salah Mahmud

Dr. Salaheddin Mahmud
Division of Cancer Epidemiology
Department of Oncology, McGill University
Gerald Bronfman Centre
546 Pine Avenue West
Montreal, Quebec
Canada H2W 1S6
Ph: (514) 398-8191.
Fax: (514) 398-5002.

-----Original Message-----
From: Anita Koushik [] 
Sent: Wednesday, July 30, 2003 9:29 AM
Subject: Epidemiological Confounder Selection

Procedure for covariate selection using a selected change-in-estimate 
criterion using backwards deletion.

1. Start with a full model including the exposure variable of interest
potential covariates, determine the parameter estimate (or RR) for the 
exposure variable of interest in that full model.

2. Determine the RR for the exposure variable when each covariate is 
deleted separately.

3. Calculate the absolute value of the % change in the RR between the
model and with the given covariate deleted.

4. After the % change has been calculated for each covariate dropped,
these % changes. Delete the variable with the lowest % change from the
model (the variable with the least impact on confounding).

5. Now start with a new full model with one less covariate (the variable

deleted previously).

6. Repeat above steps. Continue until the change in estimate is at the 
criterion level selected (e.g. 10% - will not drop any variable that 
changes the OR by 10% or more).

7. Issue: Can a variable be forced into the procedure so that it never 
drops out while other covariates are being assessed. For instance, might

want to leave age in the model regardless of whether it changes the OR 
estimate substantially or not.

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