Generalized Estimating Equations

Generalized Estimating Equations estimate generalized linear models for panel, cluster or repeated measures data when the observations are possibly correlated withing a cluster but uncorrelated across clusters. It supports estimation of the same one-parameter exponential families as Generalized Linear models (GLM).

See Module Reference for commands and arguments.

Examples

The following illustrates a Poisson regression with exchangeable correlation within clusters using data on epilepsy seizures.

import statsmodels.api as sm
import statsmodels.formula.api as smf

data = sm.datasets.get_rdataset('epil', package='MASS').data

fam = sm.families.Poisson()
ind = sm.cov_struct.Exchangeable()
mod = smf.gee("y ~ age + trt + base", "subject", data,
              cov_struct=ind, family=fam)
res = mod.fit()
print(res.summary())

Several notebook examples of the use of GEE can be found on the Wiki: Wiki notebooks for GEE

References

  • KY Liang and S Zeger. “Longitudinal data analysis using generalized linear models”. Biometrika (1986) 73 (1): 13-22.
  • S Zeger and KY Liang. “Longitudinal Data Analysis for Discrete and Continuous Outcomes”. Biometrics Vol. 42, No. 1 (Mar., 1986), pp. 121-130
  • A Rotnitzky and NP Jewell (1990). “Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data”, Biometrika, 77, 485-497.
  • Xu Guo and Wei Pan (2002). “Small sample performance of the score test in GEE”. http://www.sph.umn.edu/faculty1/wp-content/uploads/2012/11/rr2002-013.pdf
  • LA Mancl LA, TA DeRouen (2001). A covariance estimator for GEE with improved small-sample properties. Biometrics. 2001 Mar;57(1):126-34.

Module Reference

Model Class

GEE(endog, exog, groups[, time, family, ...]) Estimation of marginal regression models using Generalized Estimating Equations (GEE).

Results Classes

GEEResults(model, params, cov_params, scale) This class summarizes the fit of a marginal regression model using GEE.
GEEMargins(results, args[, kwargs]) Estimate the marginal effects of a model fit using generalized estimating equations.

Dependence Structures

The dependence structures currently implemented are

CovStruct([cov_nearest_method]) A base class for correlation and covariance structures of grouped data.
Autoregressive([dist_func]) An autoregressive working dependence structure.
Exchangeable() An exchangeable working dependence structure.
GlobalOddsRatio(endog_type) Estimate the global odds ratio for a GEE with ordinal or nominal data.
Independence([cov_nearest_method]) An independence working dependence structure.
Nested([cov_nearest_method]) A nested working dependence structure.

Families

The distribution families are the same as for GLM, currently implemented are

Family(link, variance) The parent class for one-parameter exponential families.
Binomial([link]) Binomial exponential family distribution.
Gamma([link]) Gamma exponential family distribution.
Gaussian([link]) Gaussian exponential family distribution.
InverseGaussian([link]) InverseGaussian exponential family.
NegativeBinomial([link, alpha]) Negative Binomial exponential family.
Poisson([link]) Poisson exponential family.