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Linear Quantile Mixed Effects Models
Dependent data arise in studies with a cluster, multilevel, spatial, and repeated measures sampling designs. Linear quantile mixed effect models can estimate conditional quantile functions with dependent data by including multiple random effects. The approach is based on the maximization of an asymmetric Laplace likelihood.
Papers
Geraci M, Bottai M. Quantile regression for longitudinal data using the asymmetric Laplace distribution. Biostatistics, 2007, 8(1):140-154.

Liu Y, Bottai M. Mixed-Effects Models for Conditional Quantiles with Longitudinal Data. Int J Biostatistics, 2009, Article 28.

Geraci M, Bottai M. Linear quantile mixed models. Stat Comp, 2014, 24:461-479.


Software
Download with the following Stata commands:

net from http://www.imm.ki.se/biostatistics/stata net install xtqreg, replace

The xtreg command is a beta version.

Download the "lqmm" R package.
Presentations
September 27, 2013
Linear quantile mixed-effects models
Stata Users Meeting, Stockholm, Sweden

August 16, 2011
lqmm: estimating quantile regression models for independent and hierarchical data with R
R Users Conference, Coventry, UK


Selected Applications
De Luca F, Boccuzzo G. What do healthcare workers know about sudden infant death syndrome?: the results of the Italian campaign "GenitoriPiu". J Royal Stat Soc, A, 2014, 177:63-82.

Kippler M, Tofail F, Gardner R, Rahman A, Hamadani JD, Bottai M, Vahter M. Maternal cadmium exposure during pregnancy and size at birth: a prospective cohort study. Environ Health Perspectives, 2012, 120(2):284-9



Unit of Biostatistics, Nobels väg 13, Karolinska Institutet, 17177 Stockholm, Sweden