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UCLA CTSI Biostatistics Seminar: Accelerated Longitudinal Designs - Nicholas Jackson, PhD, MPH

Type: Event
Start Date: September 19, 2019 12:00 PM
End Date: September 19, 2019 01:00 PM
Location: 1100 Glendon Avenue, Suite 710, 7th Floor Conference Room 725, Los Angeles, CA
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UCLA CTSI Biostatistics Seminar
presented by the UCLA Statistics Core

Talk title: "Accelerated Longitudinal Designs: Methods for studying longitudinal change in outcomes sensitive to generational differences"

Nicholas Jackson, PhD, MPH
Principal Statistician
UCLA Division of General Internal Medicine and
Health Services Research

*** Lunch Provided ***
To join us remotely, simply go to: https://uclahs.zoom.us/j/366612232 
Audio only is available by phone at: (669) 900-6833, meeting ID: 366 612 232
Participating CTSI Institutions: UCLA, Harbor-UCLA, Charles Drew University, and Cedars-Sinai

Abstract: Developmental models have broad applicability across the biomedical and social sciences and have been
used to understand how traits or measures change as we age. While developmental researchers are primarily
interested in maturational change, the traditional research designs used to study this change make assumptions about
the nature of development, often ignoring or confounding the influence of generational change. While ignoring
generational change can be appropriate for outcomes that are developmentally homogeneous across long periods of
time; for knowledge or behavior that may be influenced by cultural changes, alternatives to the traditional methods
are needed. The Accelerated Longitudinal Design (ALD) provides one such alterative through the study of multiple
birth-cohorts, simultaneously, in a longitudinal fashion, with overlap in the age distributions between the cohorts.
These designs allow for the capturing of maturational change with lower costs, less time, and less attrition than
traditional longitudinal designs. The ability to model between-cohort differences provides an additional benefit for
researchers interested in developing age-based trajectories that incorporate generational variability. In this talk, we
review the current state of methodological research on ALDs and present simulations to address the utility of ALDs in
small samples. We utilize Monte Carlo simulation methods to demonstrate how the statistical power and bias in the
ALD is comparable to that of the traditional single-cohort longitudinal design for both linear and nonlinear models and
discuss considerations for when between-cohort differences in development are present. We additionally discuss considerations for the modeling of cohort membership and alternate strategies for cohort inclusion.

This event is supported by NIH National Center for Advancing Translational Science (NCATS) UCLA CTSI Grant Number UL1TR001881. 


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