## Credits

This page was developed and written by Karla Lindquist, Senior Statistician in the Division of Geriatrics at UCSF. We are very grateful to Karla for taking the time to develop this page and giving us permission to post it on our site.

## Introduction

Binary outcomes in cohort studies are commonly analyzed by applying a logistic regression model to the data to obtain odds ratios for comparing groups with different sets of characteristics. Although this is often appropriate, there may be situations in which it is more desirable to estimate a relative risk or risk ratio (RR) instead of an odds ratio (OR). Several articles in recent medical and public health literature point out that when the outcome event is common (incidence of 10% or more), it is often more desirable to estimate an RR since there is an increasing differential between the RR and OR with increasing incidence rates, and there is a tendency for some to interpret ORs as if they are RRs ([1]-[3]). There are some who hold the opinion that the OR should be used even when the outcome is common, however ([4]). Here the purpose is to demonstrate methods for calculating the RR, assuming that it is the appropriate thing to do.

There are several options for how to estimate RRs directly in Stata. Two of
these methods will be demonstrated here using hypothetical data created for this
purpose. Both methods use command ** glm**. One estimates the RR with a log-binomial regression model, and the other uses a
Poisson regression model with a robust error variance.

## Example Data: Odds ratio versus relative risk

A hypothetical data set was created to illustrate two methods of estimating relative risks using Stata. The outcome generated is called lenses, to indicate if the hypothetical study participants require corrective lenses by the time they are 30 years old. Assume all participants do not need them at a baseline assessment when they are 10 years old. Assume none of them have had serious head injuries or had brain tumors or other major health problems during the 20 years between assessments. Suppose we wanted to know if requiring corrective lenses is associated with having a gene which causes one to have a lifelong love and craving for carrots (assume not having this gene results in the opposite), and that we screened everyone for this carrot gene at baseline (carrot = 1 if they have it, = 0 if not). We also noted their gender (= 1 if female, = 2 if male), and what latitude of the continental US they lived on the longest (24 to 48 degrees north). All values (N=100) were assigned using a random number generator. The data set is eyestudy.dta in Stata 8 format. Here’s a quick description of the variables.

`use https://stats.idre.ucla.edu/stat/stata/faq/eyestudy, clea`

rContains data from https://stats.idre.ucla.edu/stat/stata/faq/eyestudy.dta obs: 100 vars: 5 size: 900 (99.9% of memory free) ------------------------------------------------------------------------------- storage display value variable name type format label variable label ------------------------------------------------------------------------------- id byte %8.0g carrot byte %8.0g gender byte %8.0g latitude byte %8.0g lenses byte %8.0g ------------------------------------------------------------------------------- Sorted by:`describe`

Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- id | 100 50.5 29.01149 1 100 carrot | 100 .51 .5024184 0 1 gender | 100 1.48 .5021167 1 2 latitude | 100 35.97 7.508184 24 48 lenses | 100 .53 .5016136 0 1`summarize`

We have an overall outcome rate of 53%. So if we want to talk about whether the carrot-loving gene, gender, or latitude is associated with the risk of requiring corrective lenses by the age of 30, then relative risk is a more appropriate measure than the odds ratio. Here is a simple crosstab of carrot and lenses, which will allow us to calculate the unadjusted OR and RR by hand.

| lenses carrot | 0 1 | Total -----------+----------------------+---------- 0 | 17 32 | 49 1 | 30 21 | 51 -----------+----------------------+---------- Total | 47 53 | 100`tabulate carrot lenses`

It is interesting that fewer people with the carrot-loving gene needed corrective lenses (especially since
these are fake data!). The OR and RR for those
*without* the carrot gene vs. those *with* it are:

OR = (32/17)/(21/30) = 2.69

RR = (32/49)/(21/51) = 1.59

We could use either command ** logit** or command

**to calculate the OR. Since command**

`glm`

**will be used to calculate the RR, it will also be used to calculate the OR for comparison purposes (and it gives the same results as command**

`glm`

**). Here is the logistic regression with just carrot as the predictor:**

`logit`

Generalized linear models No. of obs = 100 Optimization : ML Residual df = 98 Scale parameter = 1 Deviance = 132.366467 (1/df) Deviance = 1.350678 Pearson = 100 (1/df) Pearson = 1.020408 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = ln(u/(1-u)) [Logit] AIC = 1.363665 Log likelihood = -66.1832335 BIC = -318.9402 ------------------------------------------------------------------------------ | OIM lenses | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0.carrot | .9891975 .4135528 2.39 0.017 .1786489 1.799746 _cons | -.3566749 .2845213 -1.25 0.210 -.9143265 .2009766 ------------------------------------------------------------------------------`glm lenses ib1.carrot, fam(bin) nolog`

Generalized linear models No. of obs = 100 Optimization : ML Residual df = 98 Scale parameter = 1 Deviance = 132.366467 (1/df) Deviance = 1.350678 Pearson = 100 (1/df) Pearson = 1.020408 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = ln(u/(1-u)) [Logit] AIC = 1.363665 Log likelihood = -66.1832335 BIC = -318.9402 ------------------------------------------------------------------------------ | OIM lenses | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0.carrot | 2.689076 1.112075 2.39 0.017 1.195601 6.048112 _cons | .7 .1991649 -1.25 0.210 .4007865 1.222596 ------------------------------------------------------------------------------`glm lenses ib1.carrot, fam(bin) nolog eform`

The ** eform** option gives us the same OR we calculated by hand above for those without the carrot gene
versus those with it. Now this can be contrasted with the two methods of calculating the RR described below.

## Relative risk estimation by log-binomial regression

With a very minor modification of the statements used above for the logistic regression, a log-binomial model can be run to get the RR instead of the OR. All that needs to be changed is the link function between the covariate(s) and outcome. Here it is specified as log instead of logit:

Generalized linear models No. of obs = 100 Optimization : ML Residual df = 98 Scale parameter = 1 Deviance = 132.366467 (1/df) Deviance = 1.350678 Pearson = 100.0000007 (1/df) Pearson = 1.020408 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = ln(u) [Log] AIC = 1.363665 Log likelihood = -66.1832335 BIC = -318.9402 ------------------------------------------------------------------------------ | OIM lenses | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0.carrot | .4612188 .1971117 2.34 0.019 .0748869 .8475507 _cons | -.8873031 .1673655 -5.30 0.000 -1.215333 -.5592728 ------------------------------------------------------------------------------`glm lenses ib1.carrot, fam(bin) link(log) nolog`

Generalized linear models No. of obs = 100 Optimization : ML Residual df = 98 Scale parameter = 1 Deviance = 132.366467 (1/df) Deviance = 1.350678 Pearson = 100.0000007 (1/df) Pearson = 1.020408 Variance function: V(u) = u*(1-u) [Bernoulli] Link function : g(u) = ln(u) [Log] AIC = 1.363665 Log likelihood = -66.1832335 BIC = -318.9402 ------------------------------------------------------------------------------ | OIM lenses | Risk Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0.carrot | 1.586006 .3126203 2.34 0.019 1.077762 2.333923 _cons | .4117647 .0689152 -5.30 0.000 .2966111 .5716246 ------------------------------------------------------------------------------`glm lenses ib1.carrot, fam(bin) link(log) nolog eform`

Now the ** eform** option gives us the estimated RR instead of the OR, and it also matches what was calculated by hand above for the RR. Notice that the standard error (SE) for the beta estimate calculated here is much smaller than that calculated in the logistic regression above (SE = 0.414), but so is the estimate itself (logistic regression beta estimate = 0.989), so the significance level is very similar (logistic regression p = 0.017) in this case. One of the criticisms of using the log-binomial model for the RR is that it produces confidence intervals that are narrower than they should be, and another is that there can be convergence problems ([1],
[2]). This is why the second approach is also presented here.

## Relative risk estimation by Poisson regression with robust error variance

Zou ([2]) suggests using a “modified Poisson” approach to estimate the relative risk and confidence intervals by using robust error variances. Using a Poisson model without robust error variances will result in a confidence interval that is too wide. The robust error variances can be estimated by using the
** robust** option, as Zou cleverly points out. Here is how it is done:

Generalized linear models No. of obs = 100 Optimization : ML Residual df = 98 Scale parameter = 1 Deviance = 64.53613549 (1/df) Deviance = .658532 Pearson = 46.99999999 (1/df) Pearson = .4795918 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] AIC = 1.745361 Log pseudolikelihood = -85.26806774 BIC = -386.7705 ------------------------------------------------------------------------------ | Robust lenses | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0.carrot | .4612188 .1981048 2.33 0.020 .0729406 .849497 _cons | -.8873032 .1682086 -5.28 0.000 -1.216986 -.5576203 ------------------------------------------------------------------------------`glm lenses ib1.carrot, fam(poisson) link(log) nolog vce(robust)`

Generalized linear models No. of obs = 100 Optimization : ML Residual df = 98 Scale parameter = 1 Deviance = 64.53613549 (1/df) Deviance = .658532 Pearson = 46.99999999 (1/df) Pearson = .4795918 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] AIC = 1.745361 Log pseudolikelihood = -85.26806774 BIC = -386.7705 ------------------------------------------------------------------------------ | Robust lenses | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0.carrot | 1.586006 .3141953 2.33 0.020 1.075667 2.33847 _cons | .4117647 .0692624 -5.28 0.000 .2961213 .57257 ------------------------------------------------------------------------------`glm lenses ib1.carrot, fam(poisson) link(log) nolog vce(robust) eform`

Again, the ** eform** option gives us the estimated RR, and it matches exactly what was calculated by the log-binomial method. In this case, the SE for the beta estimate and the p-value are also exactly the same as in the log-binomial model. This may not always be the case, but they should be similar. The SE calculated without the
robust option is 0.281, and the p-value is 0.101, so the robust method is quite different
(see the output below).

Generalized linear models No. of obs = 100 Optimization : ML Residual df = 98 Scale parameter = 1 Deviance = 64.53613549 (1/df) Deviance = .658532 Pearson = 46.99999999 (1/df) Pearson = .4795918 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] AIC = 1.745361 Log likelihood = -85.26806774 BIC = -386.7705 ------------------------------------------------------------------------------ | OIM lenses | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0.carrot | .4612188`glm lenses ib1.carrot, fam(poisson) link(log) nolog`

.28083631.64 0.101 -.0892103 1.011648 _cons | -.8873032 .2182179 -4.07 0.000 -1.315002 -.459604 ------------------------------------------------------------------------------

## Adjusting the relative risk for continuous or categorical covariates

Adjusting the RR for other predictors or potential confounders is simply done by adding them to the model statement as you would in any other procedure. Here gender and latitude will be added to the model:

Generalized linear models No. of obs = 100 Optimization : ML Residual df = 96 Scale parameter = 1 Deviance = 63.7617568 (1/df) Deviance = .664185 Pearson = 46.7434144 (1/df) Pearson = .4869106 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] AIC = 1.777618 Log pseudolikelihood = -84.8808784 BIC = -378.3346 ------------------------------------------------------------------------------ | Robust lenses | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0.carrot | .4832204 .1963616 2.46 0.014 .0983587 .8680821 1.gender | .2052008 .1857414 1.10 0.269 -.1588456 .5692472 latitude | -.0100092 .0128142 -0.78 0.435 -.0351246 .0151061 _cons | -.6521218 .4929193 -1.32 0.186 -1.618226 .3139822 ------------------------------------------------------------------------------`glm lenses ib1.carrot ib2.gender latitude, fam(poisson) link(log) nolog vce(robust)`

Generalized linear models No. of obs = 100 Optimization : ML Residual df = 96 Scale parameter = 1 Deviance = 63.7617568 (1/df) Deviance = .664185 Pearson = 46.7434144 (1/df) Pearson = .4869106 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] AIC = 1.777618 Log pseudolikelihood = -84.8808784 BIC = -378.3346 ------------------------------------------------------------------------------ | Robust lenses | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+----------------------------------------------------------------`glm lenses ib1.carrot ib2.gender latitude, fam(poisson) link(log) nolog vce(robust) eform`

0.carrot | 1.621287 .3183586 2.46 0.014 1.103358 2.382337 1.gender | 1.227772 .228048 1.10 0.269 .8531281 1.766936latitude | .9900407 .0126866 -0.78 0.435 .9654851 1.015221 _cons | .5209393 .256781 -1.32 0.186 .1982501 1.368865 ------------------------------------------------------------------------------

We have also requested the RRs for gender and latitude in the estimate statement. In this case, adjusting for them does not reduce the association between having the carrot-loving gene and risk of needing corrective lenses by age 30.

One should always pay attention to goodness of fit statistics and perform other diagnostic tests.

## References

1. McNutt LA, Wu C, Xue X, Hafner JP. Estimating the Relative Risk in Cohort Studies and Clinical Trials of Common Outcomes. Am J Epidemiol 2003; 157(10):940-3. 2. Zou G. A Modified Poisson Regression Approach to Prospective Studies with Binary Data. Am J Epidemiol 2004; 159(7):702-6. 3. Sander Greenland , Model-based Estimation of Relative Risks and Other Epidemiologic Measures in Studies of Common Outcomes and in Case-Control Studies, American Journal of Epidemiology 2004;160:301-305 4. Cook TD. Up with odds ratios! A case for odds ratios when outcomes are common. Acad Emerg Med 2002; 9:1430-4. 5. Spiegelman, D. und Hertzmark, Easy SAS Calculations for Risk or Prevalence Ratios and Differences, E American Journal of Epidemiology, 2005, 162, 199-205.