- Interval-measured outcomes, including
- Interval-censored
- Left-censored
- Right-censored

- Random effects
- Random intercepts
- Random coefficients

- Multilevel: two, three, or more levels
- Support for complex survey data
- Graph marginal means and marginal effects
- Intraclass correlation
- Support for Bayesian estimation

The new **meintreg** command fits models in which
the outcome is interval measured (interval-censored) and the observations
are clustered.

Interval measured means that rather than the outcome (y) being observed
precisely, it is known only that y_{l} ≤ y ≤ y_{u} in some or all observations. Observations can also be left-censored
(y ≤ y_{l}) or right-censored (y ≥ y_{u}). An
interval-measured outcome might be income, recorded in income brackets,
or weekly minutes of exercise, recorded as less than 30 minutes, 31-59
minutes, 60-89 minutes, etc.

Multilevel mixed effects means that the fitted model accounts for clustering, such as when people live near each other or students attend the same school or students are tested repeatedly.

We have fictional data on 8,424 people living in 50 cities. Different cities have different policies. One aspect of this is that some cities are more walker-friendly than others. Our data include a binary variable for being walker-friendly.

In these data, respondents were asked how many minutes per week they
engage in physical activity outside of their jobs and were offered the
following response categories: 0–30, 30–59, 60–89,
90–119, 120–239, and more than 240 minutes. This variable
will be our dependent variable. It is both interval- and right-censored.
The dataset contains variables **exerlo** and **exerup**.
The values recorded in the variables are

Walking per week |

exerlo exerup Meaning |

0 30 0-30 minutes |

30 59 30-59 minutes |

60 89 60-89 minutes |

90 119 90-119 minutes |

120 239 120-239 minutes |

240 . more than 240 minutes |

Our independent variables will be

walk | walker-friendly city |

age | respondent's age in years |

work | hours/week worked |

kids | children under age 12 in household |

Even so, we expect that there are unmeasured characteristics about
cities that matter—like weather, for example. Unmeasured
characteristics would result in people who live in the same city
behaving in overly similar ways, and so we will allow for city-level
random effects. Variable **cid** records city ID number.

To fit a random-intercept model using exercise in the range
**exerlo** and **exerup**, we type

.meintreg exerlo exerup age work kids walk || cid:Fitting fixed-effects model: Iteration 0: log likelihood = -16228.837 Iteration 1: log likelihood = -16211.002 Iteration 2: log likelihood = -16209.922 Iteration 3: log likelihood = -16209.92 Iteration 4: log likelihood = -16209.92 Refining starting values: Grid node 0: log likelihood = -16183.23 Fitting full model: Iteration 0: log likelihood = -16183.23 Iteration 1: log likelihood = -16021.782 Iteration 2: log likelihood = -15902.566 Iteration 3: log likelihood = -15856.315 Iteration 4: log likelihood = -15836.069 Iteration 5: log likelihood = -15830.93 Iteration 6: log likelihood = -15830.08 Iteration 7: log likelihood = -15830.052 Iteration 8: log likelihood = -15830.052 Mixed-effects interval regression Number of obs = 8,424 Uncensored = 0 Left-censored = 0 Right-censored = 54 Interval-cens. = 8,370 Group variable: cid Number of groups = 50 Obs per group: min = 81 avg = 168.5 max = 249 Integration method: mvaghermite Integration pts. = 7 Wald chi2(4) = 125.15 Log likelihood = -15830.052 Prob > chi2 = 0.0000

Coef. Std. Err. z P>|z| [95% Conf. Interval] | ||

age | -.1745526 .0695285 -2.51 0.012 -.3108259 -.0382792 | |

work | -.3635766 .0794423 -4.58 0.000 -.5192807 -.2078726 | |

kids | -10.80659 1.102927 -9.80 0.000 -12.96829 -8.644894 | |

walk | 13.7689 6.514321 2.11 0.035 1.001061 26.53673 | |

_cons | 78.62265 5.01203 15.69 0.000 68.79925 88.44605 | |

cid | ||

var(_cons) | 269.5222 56.84988 178.2592 407.5089 | |

var(e.exerlo) | 2440.81 40.2559 2363.172 2520.999 | |

LR test vs. interval model: chibar2(01) = 759.74 Prob >= chibar2 = 0.0000 |

Just above the coefficient table is a header. It reports the number of censored and uncensored observations, reports the number of observations per city, and provides information about the fitted model.

The coefficient table itself is divided into three or four parts.
The first part reports the fixed part of the model, that is, the effects of
variables **age**, **work**, etc.

The second part reports the variance of the random intercept.
We fit a model with a random intercept (**_cons**) whose
estimated mean is 79 and standard deviation is 16 (a variance of 270).

The third part, **var(e.exerlo)**, reports the variance of the
model's error. Stata labels errors as **e.** followed by the dependent
variable's name. Even so, **e.exerlo** is a bit
misleading. A better name would have been e.exer, with exer being the
imaginary name of the unobserved variable that is bounded by **exerlo**
and **exerup**.

The fourth part—the part below the table—reports a test of whether the intercept is random. Anyone would reject the null hypothesis that it has variance equal to zero.

To obtain standard deviations instead of variances for the random
portion of the model, we can type **estat sd**:

.estat sd

Coef. Std. Err. z P>|z| [95% Conf. Interval] | ||

cid | ||

sd(_cons) | 16.41713 1.731419 13.35137 20.18685 | |

sd(e.exerlo) | 49.40456 .4074108 48.61246 50.20955 | |

We can also obtain the residual intraclass correlation.

.estat iccResidual intraclass correlation

Level | ICC Std. Err. [95% Conf. Interval] | |

cid | .0994425 .0189461 .0679834 .1432221 | |

You can also fit Bayesian multilevel interval regression using the **bayes** prefix.

Learn more about Stata's multilevel mixed-effects models features.

Read more about multilevel interval regression in the *Stata Multilevel Mixed-Effects Reference Manual*.