ORDER STATA
## Marginal predictions, means, effects, and more

### Highlights

- Integrates out random effects (latent variables) after
- Multilevel models
- SEM (structural equation models)

- Marginal (population-averaged) predictions
- Marginal effects, marginal means, all other
**margins**results - For survival outcomes, plots of survivor, hazard, and cumulative hazard functions

We are about to tell you that **margins** can make meaningful
predictions in the presence of random effects, random coefficients, and
latent variables. We are about to tell you that **margins** and
Stata's **predict** integrate over the unobserved
effects. This is exciting. Here's why.

Making meaningful predictions can be difficult even in the absence of
random effects or random coefficients. For instance, consider a model
as simple as logistic regression. Assume you have a special interest
in variable's *x*'s effect on outcome. You fit a model. Based on
that, you can now say the effect of a 1-unit increase in *x* is
to decrease the log odds by 0.22. Even those of us familiar with logistic regression would probably respond, "Yes, and that means
what?"

"That's easy", you reply, exponentiating -0.22 in your head. "If *x*
increases by 1, the odds fall to 80% of what it otherwise
would be".

To which a reasonable person could reasonably reply, "And what would the odds otherwise be?"

"Well, that depends on the patient's (worker's, student's, etc.) other characteristics".

"That's rather obvious. Perhaps you could tell me the increase in terms of probabilities?"

"Well, that's rather difficult. You see, probabilities are a nonlinear function of odds ratios, so the baseline probability enters into it, and really, it's very similar to the previous problem, with an additional complication".

Irritation shows on the face of our reasonable person. "Surely you can
tell me something interpretable. How about you tell me the number of
lives that would be saved in the data if we increased *x* by one
over what it otherwise would be. Surely you can do that. Perhaps with
a 95% confidence interval?"

You realize that you can do that, but that's not a calculation you are
going to make in your head. Fortunately for you, **margins** can
help; it will make the calculation, with standard errors, and with
confidence intervals. So you agree to make the calculation to return
later.

"We have 2,500 people in our data", you report. "If we increased
*x* by 1, we would expect to save 102 lives, say, 70
to 140".

You just got the reasonable person's attention. Of course, you would have lost it if you had said that the expected value was around 0.5, somewhere between 0.2 and 0.7. That's why we make such calculations.

Now, let's complicate our model. Let's assume that the model does not
include just *x*, it includes an interaction of *x*
with *age*, and that while *x* is estimated to have a
whopping effect,
*x* multiplied by *age* has a negative effect.

We do not need to repeat the dialog, clearly
estimating the effect of *x* on lives saved is going to
be more difficult, and more important. Reporting just the coefficients
or the odds ratios does not even reveal whether increasing *x*
results in lives saved or lives lost.

**margins** can do that calculation, too, and in fact, it is no more
difficult for **margins** than the first calculation. The statement
that you would save 70 to 140 lives is now even more impressive.

Now, let's complicate our model one last time. Let's add random effects.
Let's assume that our outcomes come from patients who have doctors
who work in hospitals and that we have introduced random effects for
those doctors and hospitals. And perhaps we have introduced a random
coefficient as well, either on *x* or on some other variable.

Now, calculating the prediction is truly difficult. We want to predict an average effect, an effect as if patients had been assigned randomly to hospitals and doctors, and we have a nonlinear model, and that means we cannot ignore the variance of the random effects. Instead we will need to dust off our calculus tools and, for each patient, integrate over all unobserved variables.

**margins** can do that, too.

And **margins** can do it not just for logistic regression
and multilevel logistic regression but for

**multilevel probit****multilevel complementary log-log****multilevel ordered logit****multilevel ordered probit****multilevel Poisson****multilevel negative binomial****multilevel interval regression****multilevel tobit****multilevel nonlinear regression****multilevel parametric survival models****structural equation models**

In addition, Stata's **predict** will also integrate over
unobserved variables.

We have fictional hospital data on 2,500 patients treated in 20 hospitals by roughly 600 different doctors. We fit the following model,

. melogit outcome i.x c.age i.x#c.age other || hosp: || doctor:(output omitted)

which is how you specify in Stata that you want a multilevel
logistic regression to be fit containing (indicator variable) *x*,
(continuous variable) age, x*age, and other and that you
want random effects for each hospital and for each doctor within
hospital.

The omitted output reports that

coefficient | Z | |
---|---|---|

x | -17.11 | -10.69 |

age | 0.45 | 5.92 |

x*age | 2.21 | 10.16 |

other | 1.23 | 8.81 |

constant | -4.79 | -6.61 |

The coefficient on *x* is outlandish, but we remind you that that sometimes
happens when you include a variable and its interaction with age.
The sum of effects in this case is reasonable.

The omitted output also reported large estimated variances of the random effects, namely, 8.33 and 11.64. We could tell a story about how we expected those large variances in the case of this treatment, but because the story is fictional, we will not bother.

In fact, in these fictional data, we intentionally made the variances large just so they would have a larger effect on the predicted number of lives saved.

Based on the above results, does treatment *x* save lives or lose them?
We type

. margins r.xContrasts of predictive margins Model VCE: OIM Expression: Marginal predicted mean, predict()

df chi2 P>chi2 | ||

x | 1 52.05 0.0000 | |

Delta-method | ||

Contrast std. err. [95% conf. interval] | ||

x | ||

(1 vs 0) | -.1745077 .0241884 -.2219161 -.1270992 | |

The output reports the difference in probabilities is -0.175; patients die less with x=1 instead of x=0, and the output reports the corresponding 95% confidence interval.

We have 2,500 patients in these data, so multiplying the difference in probabilities and changing the sign results in lives saved. We obtain

436 fewer deaths if all had x=1

with a 95% confidence interval of [318, 555].

The 436 results take into account the estimated coefficients for *x* and
x*age along with the values of age and other in our data, along with
the estimated random effects for doctor and hospital.

The confidence interval takes into account all the above plus the uncertainty because some of the ingredients were estimated rather than known.

For an example of integrating out random effects to obtain marginal
predictions and marginal survivor functions, see [ME] **mestreg postestimation** and visit multilevel survival models.