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Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects.

ABSTRACT: Researchers commonly collect repeated measures on individuals nested within groups such as students within schools, patients within treatment groups, or siblings within families. Often, it is most appropriate to conceptualize such groups as dynamic entities, potentially undergoing stochastic structural and/or functional changes over time. For instance, as a student progresses through school, more senior students matriculate while more junior students enroll, administrators and teachers may turn over, and curricular changes may be introduced. What it means to be a student within that school may thus differ from 1 year to the next. This article demonstrates how to use multilevel linear models to recover time-varying group effects when analyzing repeated measures data on individuals nested within groups that evolve over time. Two examples are provided. The 1st example examines school effects on the science achievement trajectories of students, allowing for changes in school effects over time. The 2nd example concerns dynamic family effects on individual trajectories of externalizing behavior and depression.

SUBMITTER: Bauer DJ

PROVIDER: S-EPMC3638804 | biostudies-other | 2013 Mar

REPOSITORIES: biostudies-other

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Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects.

Bauer Daniel J DJ Gottfredson Nisha C NC Dean Danielle D Zucker Robert A RA

Psychological methods 20121112 1

Researchers commonly collect repeated measures on individuals nested within groups such as students within schools, patients within treatment groups, or siblings within families. Often, it is most appropriate to conceptualize such groups as dynamic entities, potentially undergoing stochastic structural and/or functional changes over time. For instance, as a student progresses through school, more senior students matriculate while more junior students enroll, administrators and teachers may turn ...[more]

PMID: 23148474

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Project description:If animals are independently detected during surveys, many methods exist for estimating animal abundance despite detection probabilities <1. Common estimators include double-observer models, distance sampling models and combined double-observer and distance sampling models (known as mark-recapture-distance-sampling models; MRDS). When animals reside in groups, however, the assumption of independent detection is violated. In this case, the standard approach is to account for imperfect detection of groups, while assuming that individuals within groups are detected perfectly. However, this assumption is often unsupported. We introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under-counted, but not over-counted. The estimator combines an MRDS model with an N-mixture model to account for imperfect detection of individuals. The new MRDS-Nmix model requires the same data as an MRDS model (independent detection histories, an estimate of distance to transect, and an estimate of group size), plus a second estimate of group size provided by the second observer. We extend the model to situations in which detection of individuals within groups declines with distance. We simulated 12 data sets and used Bayesian methods to compare the performance of the new MRDS-Nmix model to an MRDS model. Abundance estimates generated by the MRDS-Nmix model exhibited minimal bias and nominal coverage levels. In contrast, MRDS abundance estimates were biased low and exhibited poor coverage. Many species of conservation interest reside in groups and could benefit from an estimator that better accounts for imperfect detection. Furthermore, the ability to relax the assumption of perfect detection of individuals within detected groups may allow surveyors to re-allocate resources toward detection of new groups instead of extensive surveys of known groups. We believe the proposed estimator is feasible because the only additional field data required are a second estimate of group size.

Project description:BackgroundInclined walking is a challenging task that requires active neuromuscular control to maintain stability. However, the adaptive strategies that preserve stability during inclined walking are not well understood. Investigating the effects of self-paced inclined treadmill walking on gait stability characteristics and the activation patterns of key lower limb muscles can provide insights into these strategies.ObjectiveThe aim of this study was to investigate the effects of self-paced inclined treadmill walking on gait stability characteristics and the activation of key lower limb muscles.MethodsTwenty-eight able-bodied individuals (mean age 25.02, SD 2.06 years) walked on an augmented instrumented treadmill for 3 minutes at 3 inclination angles (-8°, 0°, and 8°) at their preferred walking speed. Changes in gait characteristics (ie, stability, walking speed, spatial-temporal, kinematic, and muscle forces) across inclination angles were assessed using a repeated measures ANOVA and the Friedman test.ResultsThe study revealed that inclined treadmill walking has a significant impact on gait characteristics (P<.001). Changes were observed in spatial-temporal parameters, joint angles, and muscle activations depending on the treadmill inclination. Specifically, stability and walking speed decreased significantly during uphill walking, indicating that it was the most challenging walking condition. Uphill walking also led to a decrease in spatial parameters by at least 13.53% and a 5.26% to 10.96% increase in temporal parameters. Furthermore, joint kinematics and peak activation of several muscles, including the hamstrings (biceps femoris, long head=109.5%, biceps femoris, short head=53.3%, semimembranosus=98.9%, semitendinosus=90.9%), gastrocnemius (medial gastrocnemius=40.6%, lateral gastrocnemius=35.3%), and vastii muscles (vastus intermedius=12.8%, vastus lateralis=16.7%) increased significantly during uphill walking. In contrast, downhill walking resulted in bilateral reductions in spatial-temporal gait parameters, with knee flexion increasing and hip flexion and ankle dorsiflexion decreasing. The peak activation of antagonist muscles, such as the quadriceps, tibialis anterior, and tibialis posterior, significantly increased during downhill walking (rectus femoris=97.7%, vastus lateralis =70.6%, vastus intermedius=68.7%, tibialis anterior=72%, tibialis posterior=107.1%).ConclusionsOur findings demonstrate that able-bodied individuals adopt specific walking patterns during inclined treadmill walking to maintain a comfortable and safe walking performance. The results suggest that inclined treadmill walking has the potential to serve as a functional assessment and rehabilitation tool for gait stability by targeting muscle training. Future research should investigate the effects of inclined treadmill walking on individuals with gait impairments and the potential benefits of targeted muscle training. A better understanding of the adaptive strategies used during inclined walking may lead to the development of more effective rehabilitation interventions for individuals with lower limb injuries.

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Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects.

Publications

Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects.

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