Design and analysis of intra-subject variability in cross-over experiments

Stat Med. 1996 Aug 15;15(15):1619-34. doi: 10.1002/(SICI)1097-0258(19960815)15:15<1619::AID-SIM326>3.0.CO;2-N.


Recently, interest has grown in the development of inferential techniques to compare treatment variabilities in the setting of a cross-over experiment. In particular, comparison of treatments with respect to intra-subject variability has greater interest than has inter-subject variability. We begin with a presentation of a general approach for statistical inference within a cross-over design. We discuss three different statistical models where model choice depends on the design and assumptions about carry-over effects. Each model incorporates t-variate random subject effects, where t is the number of treatments. We develop maximum likelihood (ML) and restricted maximum likelihood (REML) approaches to derive parameter estimators and we consider a special case in which closed-form expressions for the variance component estimators are available. Finally, we illustrate the methodologies with the analysis of data from three examples.

Publication types

  • Clinical Trial
  • Controlled Clinical Trial

MeSH terms

  • Area Under Curve
  • Bronchodilator Agents / administration & dosage
  • Clinical Trials as Topic / methods*
  • Cross-Over Studies*
  • Ethanolamines / administration & dosage
  • Forced Expiratory Volume / drug effects
  • Formoterol Fumarate
  • Gastroesophageal Reflux / drug therapy
  • Humans
  • Likelihood Functions
  • Male
  • Metoclopramide / therapeutic use
  • Models, Statistical*
  • Randomized Controlled Trials as Topic / methods
  • Reference Values
  • Reproducibility of Results
  • Research Design
  • Therapeutic Equivalency
  • Verapamil / pharmacology


  • Bronchodilator Agents
  • Ethanolamines
  • Verapamil
  • Metoclopramide
  • Formoterol Fumarate