Related Experiment Video
Updated: Jun 6, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Estimating individual treatment effects from multiple-baseline data: a Monte Carlo study of multilevel-modeling
John M Ferron1, Jennie L Farmer, Corina M Owens
1University of South Florida, Tampa, Florida 33620-7750, USA. ferron@usf.edu
Abstract:
While conducting intervention research, researchers and practitioners are often interested in how the intervention functions not only at the group level, but also at the individual level. One way to examine individual treatment effects is through multiple-baseline studies analyzed with multilevel modeling. This analysis allows for the construction of confidence intervals, which are strongly recommended in the reporting guidelines of the American Psychological Association. The purpose of this study was to examine the accuracy of confidence intervals of individual treatment effects obtained from multilevel modeling of multiple-baseline data. Monte Carlo methods were used to examine performance across conditions varying in the number of participants, the number of observations per participant, and the dependency of errors. The accuracy of the confidence intervals depended on the method used, with the greatest accuracy being obtained when multilevel modeling was coupled with the Kenward-Roger method of estimating degrees of freedom.
Related Concept Videos
Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
