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help for oddsrisk
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Conversion from Logistic Odds Ratios to Risk Ratios

oddsrisk y(1/0) riskfactor(1/0) varlist [fw=countvariable] <if> <in>

Description

oddsrisk converts logistic regression odds ratios to relative risk ratios by
the formula described below. Source: Zhang and K. Yu, 1998. Frequency weights
are allowed in order to calculate odds and risk ratios from 2 x 2 tables. The
response must be binary, as does the first predictor, which is considered to be
the risk factor or exposure.

When the incidence of an outcome is common in the study population; i.e. greate
> r than
10%, the logistic regression odds ratio no longer approximates the risk ratio.
> As the
incidence rate becomes more frequent, the more the odds ratio overestiamtes the
>  risk
ratio when its value is greater than 1, and the more it underestiamtes the risk
>  ratio
when under one. J. Zhang and K. Yu proposed a method of adjusting the logistic
regression odds ratio in a cohort study or clinical trial so that it approximat
> es
the risk ratio. This is particularly important when the odds ratio is greater t
> han
2.5 or under 0.5. The method has also been shown to be applicable for retrospec
> tive
and observational studies as well.

The method is based on the response having a binary risk factor, or primary pre
> dictor
of interest. Other confounding predictors may also be included in the model. Th
> e
risk ratio is defined as
Rho  =  pe / pu

and represents how much more or less likely it is for y to be the case; i.e.
(y==1) given a risk factor of 1, in comparison to {y==1} given a risk factor of
>  0.

For example, let us denote the risk of death within 48 hours of admission to a
hospital (y) occurring for those sustaining an anterior infarct as pe and the r
> isk
of death (y) among those having an other-site infarct as pu.  The subscripts e
> and
u signify exposed (1) and unexposed (0); i.e. e is having had an anterior infar
> ct
and u is having an other-site infarct.  The risk ratio, Rho, represents how muc
> h
more or less likely it is for an individual to die within 48 hours of admission
>
among those sustaining an anterior infarct in comparison to those who died havi
> ng
an infarct at another site.

When pe is small; i.e. the incidence rate for the exposed group is under 0.1,
then the values of Rho, the risk ratio, and OR, the odds ratio, are nearly the
same. As the incidence rate grows, the odds ratio will overestimate the risk ra
> tio
when the latter is greater than 1.0 and underestimate the risk ratio when less
than 1.0. Simulation studies have shown that values of Pe less than 0.1.

Under this formulation, the odds ratio of the risk factor, or predictor of
interest, is defined as:

pe/(1- pe)
OR  =  ------------
pu/(1- pu)

where  pe is the incidence rate for exposed patients (RF==1),
and    pu is the incidence rate for unexposed patients (RF==0),
where RF is the risk factor, a binary predictor of interest.

The formula for risk ratio in terms of the odds ratio is:

OR
RR ~  ---------------------
(1- pu) + (pu *OR)

One must first calculate the incidence rate of the unexposed group; ie when
the risk factor equals 0. This is the value of pu.

The fomula may be used to convert all predictors in the model to extimated
risk ratios. The same formula may also be used for the confidence intervals
of the odds ratio.

Example 1

The anterior data set was created from a tabulation of death and anterior
taken from the heart01 data set. A listing of the data, together with a
schemata of the underlying table, is shown first, followed by the use of
oddsrisk to estimate the odds and risk ratios, as well as 95% confidence
intervals. These examples taken from
Hilbe J.M. (2008). Logistic Regression Models, Chapman & Hall/CRC Press

. use anterior

. list

+--------------------------+
| count   death   anterior |
|--------------------------|
1. |   120       1          1 |
2. |    67       1          0 |
3. |  2005       0          1 |
4. |  2504       0          0 |
+--------------------------+

Table represented by data

Response (death)
1      0
---------------
Risk       1 |  120   2005 | 2125
(Anterior MI)    |             |
0 |   67   2504 | 2571
---------------
187   4509   4696

Use of oddsrisk, using count as a frequency weight

. oddsrisk death anterior [fw=count]

---------------------------------------------------------------------
Incidence for unexposed risk group =     0.0261
---------------------------------------------------------------------
Predictor    Odds Ratio   Risk Ratio     [95% Conf. Interval]
---------------------------------------------------------------------
anterior        2.2368       2.1670       1.6220       2.8807
---------------------------------------------------------------------

Use of the csi command. Risk ratio CIs calculated using Woolfe's method.

. csi 120 67 2005 2504

|   Exposed   Unexposed  |      Total
-----------------+------------------------+------------
Cases |       120          67  |        187
Noncases |      2005        2504  |       4509
-----------------+------------------------+------------
Total |      2125        2571  |       4696
|                        |
Risk |  .0564706    .0260599  |   .0398211
|                        |
|      Point estimate    |    [95% Conf. Interval]
|------------------------+------------------------
Risk difference |         .0304107       |    .0188244     .041997
Risk ratio |         2.166953       |    1.616054    2.905651
Attr. frac. ex. |         .5385226       |    .3812087     .655843
Attr. frac. pop |          .345576       |
+-------------------------------------------------
chi2(1) =    28.14  Pr>chi2 = 0.0000

Simulation studies have demonstrated that a Poisson model with robust
SEs closely estimate the relative risk. Likewise, a log-binomial can be
used with the same result, but convergence problems many times occur
with multivariate models.

We use a Poisson with robust standard errors for comparison of results.
Note its close similarity to Woolfe's method, as well as to Zhang and Yu's
method (oddsrisk].

. poisson death anterior [fw=count], nolog irr robust
[ Header excluded from display ]

------------------------------------------------------------------------------
|               Robust
death |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
anterior |   2.166953   .3243484     5.17   0.000     1.616003    2.905741
------------------------------------------------------------------------------

Example 2

Use of full data.

. use heart01

. logit death anterior hcabg kk2-kk4, nolog or
[ Header excluded from display ]

------------------------------------------------------------------------------
death | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
anterior |    2.05989   .3391907     4.39   0.000       1.4917    2.844505
hcabg |   2.051369   .7098963     2.08   0.038     1.041063    4.042131
kk2 |   2.573692   .4558429     5.34   0.000     1.818841    3.641818
kk3 |   3.244251   .8504555     4.49   0.000      1.94079    5.423135
kk4 |   18.55298    6.12317     8.85   0.000     9.715963     35.4276
------------------------------------------------------------------------------

. oddsrisk death anterior hcabg kk2-kk4

---------------------------------------------------------------------
Incidence for unexposed risk group =     0.0261
---------------------------------------------------------------------
Predictor    Odds Ratio   Risk Ratio     [95% Conf. Interval]
---------------------------------------------------------------------
anterior        2.0599       2.0045       1.4728       2.7140
hcabg           2.0514       1.9967       1.0400       3.7452
kk2             2.5737       2.4723       1.7808       3.4072
kk3             3.2443       3.0650       1.8943       4.8626
kk4            18.5530      12.7299       7.9176      18.6738
---------------------------------------------------------------------

Compare with a Poisson model with robust standard errors, which estimates
incident rate ratios. Simulation studies have confirmed that this model,
as well as a log-binomial model estimate relative risk ratios.

. poisson death anterior hcabg kk2-kk4, nolog irr robust
[ Header excluded from display ]

------------------------------------------------------------------------------
|               Robust
death |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
anterior |   1.963766   .3099786     4.28   0.000     1.441213    2.675784
hcabg |   1.937465   .5851856     2.19   0.029     1.071864    3.502096
kk2 |   2.464633   .4128878     5.38   0.000     1.774822    3.422551
kk3 |   3.044356   .7604601     4.46   0.000     1.865825    4.967295
kk4 |   12.33746   2.883896    10.75   0.000      7.80291    19.50721
------------------------------------------------------------------------------

Author
Joseph M. Hilbe, Arizona State University ---- Hilbe@asu.edu; jhilbe@aol.com

References

J. Zhang and K. Yu, 1998.  What's the Relative Risk, JAMA, Vol 280, No 19, pp
1690-1691.

J.M. Hilbe, 2008 Logistic Regression Models, Chapman & Hall/CRC Press.

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