Bayesian inference for longitudinal data with non-parametric treatment effects

Biostatistics. 2014 Apr;15(2):341-52. doi: 10.1093/biostatistics/kxt049. Epub 2013 Nov 26.


We consider inference for longitudinal data based on mixed-effects models with a non-parametric Bayesian prior on the treatment effect. The proposed non-parametric Bayesian prior is a random partition model with a regression on patient-specific covariates. The main feature and motivation for the proposed model is the use of covariates with a mix of different data formats and possibly high-order interactions in the regression. The regression is not explicitly parameterized. It is implied by the random clustering of subjects. The motivating application is a study of the effect of an anticancer drug on a patient's blood pressure. The study involves blood pressure measurements taken periodically over several 24-h periods for 54 patients. The 24-h periods for each patient include a pretreatment period and several occasions after the start of therapy.

Keywords: Clustering; Mixed-effects model; Non-parametric Bayesian model; Random partition; Repeated measurement data.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem*
  • Blood Pressure / drug effects
  • Blood Pressure Determination
  • Data Interpretation, Statistical*
  • Humans
  • Models, Statistical*
  • Time Factors
  • Treatment Outcome