Estimating Reliability and Generalizability from Hierarchical Biomedical Data

Abstract

It is shown how hierarchical biomedical data, such as coming from longitudinal clinical trials, meta-analyses, or a combination of both, can be used to provide evidence for quantitative strength of reliability, agreement, generalizability, and related measures that derive from association concepts. When responses are of a continuous, Gaussian type, the linear mixed model is shown to be a versatile framework. At the same time, the framework is embedded in the generalized linear mixed models, such that non-Gaussian, e.g., binary, outcomes can be studied as well. Similarities and, above all, important differences are studied. All developments are exemplified using clinical studies in schizophrenia, with focus on the endpoints Clinician's Global Impression (CGI) or Positive and Negative Syndrome Scale (PANSS).

Key Words:

• Generalizability
• Generalized linear mixed model
• Linear mixed model
• Longitudinal data
• Psychiatry
• Rating scale
• Reliability
• Repeated measurements
• Schizophrenia

Mathematics Subject Classification:

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ACKNOWLEDGMENTS

The authors are grateful to Johnson & Johnson Pharmaceutical Research & Development for kind permission to use their data. We gratefully acknowledge support from Belgian IUAP/PAI network “Statistical Techniques and Modeling for Complex Substantive Questions with Complex Data”.