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Published Online: 1 September 2011

Design Considerations for Characterizing Psychiatric Trajectories Across the Lifespan: Application to Effects of APOE-ε4 on Cerebral Cortical Thickness in Alzheimer's Disease

Abstract

Characterization of developmental trajectories across the lifespan is integral to understanding the prodromal course of many neuropsychiatric illnesses and the significant risk factors for disease onset or unfavorable outcomes. Structured multicohort longitudinal designs represent a significant improvement for making developmental inferences while being more practical to implement than single-cohort longitudinal designs.

Abstract

Characterization of developmental trajectories across the lifespan is integral to understanding the prodromal course of many neuropsychiatric illnesses and the significant risk factors for disease onset or unfavorable outcomes. However, the standard experimental designs used in psychiatric research are not ideal for this purpose. The authors review the limitations of the most commonly employed designs in studies that make developmental or lifespan inferences in psychiatry: cross-sectional, single-cohort longitudinal, and unstructured multicohort longitudinal designs. Cross-sectional studies completely confound within- and between-subject sources of variation and hence rely on the presence of parallel trajectories and negligible sampling and age cohort differences for making valid developmental inferences. Delineating trajectories of within-individual change over substantial periods of time requires data covering long age spans that often cannot be covered using single-cohort longitudinal designs. Unstructured multicohort longitudinal designs are a commonly used alternative that can cover a longer age span in a shorter interval than necessary for a single-cohort design. However, the impact of cohort and sampling effects is often minimized or ignored in unstructured multicohort longitudinal designs. The authors propose that structured multicohort longitudinal designs are a particularly viable and underutilized class of designs in psychiatry that represents a significant improvement over cross-sectional designs and unstructured multicohort longitudinal designs for making developmental inferences while being more practical to implement than single-cohort longitudinal designs. As an example of this approach, the authors analyze changes in entorhinal cortex thickness in Alzheimer's disease in relation to APOE-ε4 genotype.

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Published In

Go to American Journal of Psychiatry
Go to American Journal of Psychiatry
American Journal of Psychiatry
Pages: 894 - 903
PubMed: 21724665

History

Received: 24 November 2010
Revision received: 31 March 2011
Revision received: 4 April 2011
Accepted: 4 April 2011
Published online: 1 September 2011
Published in print: September 2011

Authors

Affiliations

Wesley K. Thompson, Ph.D.
From the Department of Psychiatry, University of California San Diego, La Jolla; the Stein Center for Research on Aging, University of California San Diego, La Jolla; the Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford; the VA Sierra-Pacific Mental Illness Education and Clinical Center, Veterans Affairs Health Care System, Palo Alto, Calif.
Joachim Hallmayer, M.D.
From the Department of Psychiatry, University of California San Diego, La Jolla; the Stein Center for Research on Aging, University of California San Diego, La Jolla; the Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford; the VA Sierra-Pacific Mental Illness Education and Clinical Center, Veterans Affairs Health Care System, Palo Alto, Calif.
Ruth O'Hara, Ph.D.
From the Department of Psychiatry, University of California San Diego, La Jolla; the Stein Center for Research on Aging, University of California San Diego, La Jolla; the Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford; the VA Sierra-Pacific Mental Illness Education and Clinical Center, Veterans Affairs Health Care System, Palo Alto, Calif.
the Alzheimer's Disease Neuroimaging Initiative
From the Department of Psychiatry, University of California San Diego, La Jolla; the Stein Center for Research on Aging, University of California San Diego, La Jolla; the Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford; the VA Sierra-Pacific Mental Illness Education and Clinical Center, Veterans Affairs Health Care System, Palo Alto, Calif.

Notes

Address correspondence and reprint requests to Dr. O'Hara ([email protected]).

Funding Information

All authors report no financial relationships with commercial interests.Supported in part by NIH grants MH070886, AG 18784, AG 17824, K25 MH076981-01, and R01 MH067005; and by the Department of Veterans Affairs Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC). Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (NIH Grant U01 AG024904). ADNI is funded by the National Institute on Aging, by the National Institute of Biomedical Imaging and Bioengineering, and through contributions from Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson & Johnson, Eli Lilly, Medpace, Merck, Novartis AG, Pfizer, F. Hoffman-La Roche, Schering-Plough, and Synarc, as well as nonprofit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private-sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 and K01 AG030514 and by the Dana Foundation.

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