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Logistic Quantile Regression
Logistic quantile regression can analyze variables that take on values within a specified range, like visual analog scales, psychological scales, and percentages. With bounded variables, traditional statistical methods, such as least-squares regression, mixed-effects models, and even classic nonparametric methods such as the Wilcoxon's test, may be inadequate.
Bottai M, Cai B, McKeown RE. Logistic quantile regression for bounded outcomes. Stat Med, 2010, 29(2):309-17.

Orsini N, Bottai M. Logistic quantile regression in Stata. Stata J, 2011, 11(3):327-344.


R package: "lqr"

Download with the following Stata commands:

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

Worked-out example in Stata

Physical activity and lower urinary tract symptoms
June 27, 2013
Logistic quantile regression for bounded outcomes
IX Italian Biometric Society Conference, Brixen, Italy

Selected Applications
Siao JS, Hwang RC, Chu CK. Predicting recovery rates using logistic quantile regression with bounded outcomes. Quantitative Finance, 2016, 16(5):777-792.

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