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st: SV: RE: risk ratio
From
"Tomas Lind" <[email protected]>
To
<[email protected]>
Subject
st: SV: RE: risk ratio
Date
Mon, 22 Mar 2010 16:36:16 +0100
Hi Joseph,
Thanks for your kind response.
I am working with Stata v10 (but we are going to upgrade to v11 soon). You
find my code below to generate data according to a logit model.
In the first example I generate data with an odds-model. The
beta-coefficient used to generate data is 0.49.
When analyzing these data with logistic regression I get my beta-coefficient
(0.50 in this run). When analyzing data with a Poisson-model, beta is
estimated to 0.069. I suppose this is because a Poisson-model is measuring
RR not OR.
clear *
set obs 100000
* Expo is logNf mean=16,2 sd=8,2
gen pm10 = exp(2.66 + 0.49 * invnorm(uniform()))
generate z=(-11.2 + (0.5*(pm10) ))
generate p_case=(1/(1+exp(-z))) // p_case=0.2
generate case=0
replace case=1 if(uniform()<p_case & p_case !=.)
glm fall pm10 , link(logit) fam(bin) // beta = 0.50
glm fall pm10 , link(log) fam(poi) robust // beta = 0,069
In example 2 I generate data with the -genbinomial- with a log link to
generate data where exposure is proportional to risk. In this case Poisson
regression gives me the correct beta but the logistic regression does not.
clear *
set obs 200000
gen x1 = invnorm(uniform())
gen x2 = invnorm(uniform())
gen xb = -1 + 0.5*x1 + 1.5*x2
genbinomial y, xbeta(xb) n(1) link(log) // link(LOG)
rename y case
// genbinomial might give values outside 0, 1.
drop if case==. // p(case)=0.3
glm case x1 x2 , link(log) fam(po) vce(robust) // OK beta1=0,50 beta2=1,51
glm case x1 x2 , link(logit) fam(bin) // Wrong beta1=0,82 beta2=2,44
Yours
Tomas
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Ämne: st: RE: risk ratio
In response to the StataLister asking about creating a synthetic binary
response model that
can be used to estimate a relative risk ratio:
I have an article coming out in the next Stata Journal that details how
to create synthetic models for a wide variety
of discrete response regression models. For your problem though, I
think that the best approach is to create a synthetic
binary logistic model with a single predictor - as you specified. Then
model the otherwise logistic data as
Poisson with a robust variance estimator. And the coefficient must be
exponentiated. It can be interpreted as a relative
risk ratio.
Below is code to create a simple binary logistic model. Then model as
mentioned above. You asked for a
continuous pseudo-random variate, so I generated it from a normal
distribution. I normally like to use pseudo-random
uniform variates rather normal variates when creating these types of
models, but it usually makes little difference.
Recall that without a seed the model results will differ each time run.
If you want the same results, pick a seed. I used my birthday.
I hope that this is what you were looking for.
Joseph Hilbe
* intercept = 2; Beta for X1=0.75
clear
set obs 50000
set seed 1230
gen x1 = invnorm(runiform())
gen xb = 2 + 0.75*x1
gen exb = 1/(1+exp(-xb))
gen by = rbinomial(1, exb)
glm by x1, nolog fam(bin 1)
glm by x1, nolog fam(poi) eform robust
. glm by x1, nolog fam(bin 1)
Generalized linear models No. of obs =
50000
Optimization : ML Residual df =
49998
Scale parameter = 1
Deviance = 37672.75548 (1/df) Deviance =
.7534852
Pearson = 49970.46961 (1/df) Pearson =
.9994494
Variance function: V(u) = u*(1-u) [Bernoulli]
Link function : g(u) = ln(u/(1-u)) [Logit]
AIC =
.7535351
Log likelihood = -18836.37774 BIC =
-503294.5
-------------------------------------------------------------------------
-----
| OIM
by | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+-----------------------------------------------------------
-----
x1 | .7534291 .0143134 52.64 0.000 .7253754
.7814828
_cons | 1.993125 .0149177 133.61 0.000 1.963887
2.022363
-------------------------------------------------------------------------
-----
. glm by x1, nolog fam(poi) eform robust
Generalized linear models No. of obs
=50000
Optimization : ML Residual df =
49998
Scale parameter = 1
Deviance = 12673.60491 (1/df) Deviance =
.2534822
Pearson = 7059.65518 (1/df) Pearson =
.1411988
Variance function: V(u) = u [Poisson]
Link function : g(u) = ln(u) [Log]
AIC =
1.970592
Log pseudolikelihood = -49262.80246 BIC =
-528293.7
-------------------------------------------------------------------------
-----
| Robust
by | IRR Std. Err. z P>|z| [95% Conf.
Interval]
-------------+-----------------------------------------------------------
-----
x1 | 1.104476 .0021613 50.78 0.000 1.100248
1.10872
-------------------------------------------------------------------------
-----
.
Tomas Lind wrote:
Does anyone know how to generate fake data for a dichotomous outcome
(0, 1)
that is dependent on a continuous exposure variable in an
epidemiological
relative risk context. I know how to use the logit transformation but in
that case exposure is proportional to log(ods) and not to risk.
*
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