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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/675

Title: Choice of units of analysis and modeling strategies in multilevel hierarchical models
Authors: Cortinas Abrahantas, Jose
Molenberghs, Geert
Burzykowski, Tomasz
Shkedy, Ziv
Alonso Abad, Ariel
Renard, Didier
Keywords: Surrogate Markers
Mixed Models
Longitudinal data
Clustered data
Issue Date: 2004
Citation: Computational Statistics and Data Analysis, 47(3). p. 537-563
Abstract: Hierarchical models are common in complex surveys, psychometric applications, as well as agricultural and biomedical applications, to name but a few. The context of interest here is meta-analysis, with emphasis on the use of such an approach in the evaluation of surrogate endpoints in randomized clinical trials. The methodology rests on the ability to replicate the effect of treatment on both the true endpoint, as well as the candidate surrogate endpoint, across a number of trials. However, while a meta-analysis of clinical trials in the same indication seems the natural hierarchical structure, some authors have considered center or country as the unit, eitherbecause no meta-analytic data were available orbecause, even when available, they might not allow for a su9cient level of replication. This leaves us with two important, related questions. First, how sensible is it to replace one level of replication by another one? Second, what are the consequences when a truly three- or higher-level model (e.g., trial, center, patient) is replaced by a coarser two-level structure (either trial and patient or center and patient). The same orsimilarquestions may occurin a numberof different settings, as soon as interest is placed on the validity of a conclusion at a certain level of the hierarchy, such as in sociological or genetic studies. Using the framework of normally distributed endpoints, these questions will be studied, using both analytic calculation as well as Monte Carlo simulation.
URI: http://hdl.handle.net/1942/675
Link to publication: https://pdfs.semanticscholar.org/9bbe/7ca2e5a2d2f2cc70dd67c93a4508abc888c8.pdf
DOI: 10.1016/j.csda.2003.12.003
ISI #: 000224673400008
ISSN: 0167-9473
Category: A1
Type: Journal Contribution
Validation: ecoom, 2005
Appears in Collections: Research publications

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