How Does Fidelity of Implementation Matter? Using Multilevel Models to Detect Relationships Between Participant Outcomes and the Delivery and Receipt of Treatment

2012 ◽  
Vol 33 (4) ◽  
pp. 547-565 ◽  
Author(s):  
Keith Zvoch
Keyword(s):  
Program Evaluation ◽  
Multilevel Modeling ◽  
Multilevel Models ◽  
Growth Models ◽  
Fidelity Of Implementation ◽  
Literacy Intervention ◽  
Multidimensional Construct ◽  
Modeling Techniques ◽  
Model Treatment ◽  
Participant Outcomes

Multilevel modeling techniques facilitated examination of relationships between fidelity indicators and outcomes associated with a summer literacy intervention. Three-level growth models were specified to capture the extent to which students experienced instruction and to demonstrate the ways in which dosage–response relationships manifest in program evaluation contexts. The observation that outcome-related deviations from program protocol occurred both at the provider and at the recipient levels suggests that evaluators will often need to conceptualize, measure, and model “treatment fidelity” as a multilevel, multidimensional construct.

Multilevel Modeling and Multilevel Structural Equation Modeling in Lifespan Developmental Analyses

2019 ◽  
Author(s):  
Kimberly L. Fine ◽  
Kevin J. Grimm
Keyword(s):  
Longitudinal Data ◽  
Multilevel Modeling ◽  
Structural Equation ◽  
Multilevel Models ◽  
Growth Models ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Multilevel Structural Equation ◽  
Data Analytic ◽  
Over Time

Multilevel modeling is a data analytic framework that is appropriate when analyzing data that are dependent due to the clustering of observations in higher-level units. Clustered data appear in a variety of disciplines, which makes multilevel modeling a necessary data analytic tool for many researchers. Longitudinal data are a special kind of clustered data as the repeated observations are clustered within individuals. Multilevel models can be applied to longitudinal data to examine how individuals change over time and how individuals differ in their change processes over time. For longitudinal data, linear multilevel models, where the fixed- and random-effects parameters enter the model in a linear fashion, and nonlinear multilevel models, where at least one fixed-and/or random-effect parameter enters the model in a nonlinear fashion are commonly estimated to examine different forms of the individual change process. Multilevel structural equation modeling is an extension of multilevel modeling that allows for multivariate outcomes, and this framework is very useful for modeling multivariate longitudinal data (e.g., multivariate growth models and second-order growth models).

scholarly journals 87. Impact of State of Residence on Adult Vaccination Uptake: A Multilevel Modeling Approach

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S175-S175
Author(s):  
Shannon Hunter ◽  
Diana Garbinsky ◽  
Elizabeth M La ◽  
Sara Poston ◽  
Cosmina Hogea
Keyword(s):  
Multilevel Modeling ◽  
Vaccination Coverage ◽  
Multilevel Models ◽  
State Level ◽  
Childhood Vaccination ◽  
Material Support ◽  
Adult Vaccination ◽  
Direct Compensation ◽  
Cluster Variation ◽  
Coverage Rates

Abstract Background Previous studies on adult vaccination coverage found inter-state variability that persists after adjusting for individual demographic factors. Assessing the impact of state-level factors may help improve uptake strategies. This study aimed to: • Update previous estimates of state-level, model-adjusted coverage rates for influenza; pneumococcal; tetanus, diphtheria, and acellular pertussis (Tdap); and herpes zoster (HZ) vaccines (individually and in compliance with all age-appropriate recommended vaccinations) • Evaluate effects of individual and state-level factors on adult vaccination coverage using a multilevel modeling framework. Methods Behavioral Risk Factor Surveillance System (BRFSS) survey data (2015–2017) were retrospectively analyzed. Multivariable logistic regression models estimated state vaccination coverage and compliance using predicted marginal proportions. BRFSS data were then combined with external state-level data to estimate multilevel models evaluating effects of state-level factors on coverage. Weighted odds ratios and measures of cluster variation were estimated. Results Adult vaccination coverage and compliance varied by state, even after adjusting for individual characteristics, with coverage ranging as follows: • Influenza (2017): 35.1–48.1% • Pneumococcal (2017): 68.2–80.8% • Tdap (2016): 21.9–46.5% • HZ (2017): 30.5–50.9% Few state-level variables were retained in final multilevel models, and measures of cluster variation suggested substantial residual variation unexplained by individual and state-level variables. Key state-level variables positively associated with vaccination included health insurance coverage rates (influenza/HZ), pharmacists’ vaccination authority (HZ), presence of childhood vaccination exemptions (pneumococcal/Tdap), and adult immunization information system participation (Tdap/HZ). Conclusion Adult vaccination coverage and compliance continue to show substantial variation by state even after adjusting for individual and state-level characteristics associated with vaccination. Further research is needed to assess additional state or local factors impacting vaccination disparities. Funding GlaxoSmithKline Biologicals SA (study identifier: HO-18-19794) Disclosures Shannon Hunter, MS, GSK (Other Financial or Material Support, Ms. Hunter is an employee of RTI Health Solutions, who received consultancy fees from GSK for conduct of the study. Ms. Hunter received no direct compensation from the Sponsor.) Diana Garbinsky, MS, GSK (Other Financial or Material Support, The study was conducted by RTI Health Solutions, which received consultancy fees from GSK. I am a salaried employee at RTI Health Solutions and received no direct compensation from GSK for the conduct of this study..) Elizabeth M. La, PhD, RTI Health Solutions (Employee) Sara Poston, PharmD, The GlaxoSmithKline group of companies (Employee, Shareholder) Cosmina Hogea, PhD, GlaxoSmithKline (Employee, Shareholder)

scholarly journals A Tale of Two Emotions: The Diverging Salience and Health Consequences of Calmness and Excitement in Old Age

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 626-627
Author(s):  
Jeremy Hamm ◽  
Carsten Wrosch ◽  
Meaghan Barlow ◽  
Ute Kunzmann
Keyword(s):  
Older Adults ◽  
Old Age ◽  
Perceived Control ◽  
Positive Emotions ◽  
Multilevel Models ◽  
Growth Models ◽  
Well Being ◽  
Physical Symptoms ◽  
Health Consequences ◽  
Younger Adults

Abstract Using two studies, we examined the late life prevalence and health consequences of discrete positive emotions posited to motivate rest and recovery (calmness) or pursuit of novelty and stimulation (excitement). Study 1 assessed the salience of these discrete emotions in older adults (n=73, Mage=73) relative to younger adults (n=73, Mage=23) over a one-week period. Multilevel models showed that older (vs. younger) adults reported higher calmness and lower excitement. Study 2 examined the longitudinal health consequences of calmness and excitement in old age (n=336, Mage=75), as moderated by perceived control. Multilevel growth models showed that calmness, but not excitement, buffered against 10-year declines in psychological well-being (perceived stress, depressive symptoms) and physical health (physical symptoms, chronic conditions) for older adults with low perceived control. Results suggest that positive emotions with disparate motivational functions become more (calmness) or less (excitement) salient and have diverging implications for health in old age.

scholarly journals Application of Multilevel Models to Morphometric Data. Part 2. Correlations

2003 ◽  
Vol 25 (4) ◽  
pp. 187-191 ◽  
Author(s):  
O. Tsybrovskyy ◽  
A. Berghold
Keyword(s):  
Multilevel Modeling ◽  
Multilevel Models ◽  
Correlation Coefficients ◽  
Morphometric Data ◽  
Cell Level ◽  
Units Of Analysis ◽  
Major Interest ◽  
Level Statistics ◽  
Indispensable Tool ◽  
Multivariate Multilevel Models

Multilevel organization of morphometric data (cells are “nested” within patients) requires special methods for studying correlations between karyometric features. The most distinct feature of these methods is that separate correlation (covariance) matrices are produced for every level in the hierarchy. In karyometric research, the cell‐level (i.e., within‐tumor) correlations seem to be of major interest. Beside their biological importance, these correlation coefficients (CC) are compulsory when dimensionality reduction is required. Using MLwiN, a dedicated program for multilevel modeling, we show how to use multivariate multilevel models (MMM) to obtain and interpret CC in each of the levels. A comparison with two usual, “single‐level” statistics shows that MMM represent the only way to obtain correct cell‐level correlation coefficients. The summary statistics method (take average values across each patient) produces patient‐level CC only, and the “pooling” method (merge all cells together and ignore patients as units of analysis) yields incorrect CC at all. We conclude that multilevel modeling is an indispensable tool for studying correlations between morphometric variables.

scholarly journals Application of Multilevel Models to Morphometric Data. Part 1. Linear Models and Hypothesis Testing

2003 ◽  
Vol 25 (4) ◽  
pp. 173-185 ◽  
Author(s):  
O. Tsybrovskyy ◽  
A. Berghold
Keyword(s):  
Multilevel Modeling ◽  
Multilevel Models ◽  
Linear Models ◽  
Nuclear Morphology ◽  
Hierarchical Data ◽  
Morphometric Data ◽  
Statistical Software ◽  
Level Statistics ◽  
Thyroid Adenomas ◽  
Theoretical Issues

Morphometric data usually have a hierarchical structure (i.e., cells are nested within patients), which should be taken into consideration in the analysis. In the recent years, special methods of handling hierarchical data, called multilevel models (MM), as well as corresponding software have received considerable development. However, there has been no application of these methods to morphometric data yet. In this paper we report our first experience of analyzing karyometric data by means of MLwiN – a dedicated program for multilevel modeling. Our data were obtained from 34 follicular adenomas and 44 follicular carcinomas of the thyroid. We show examples of fitting and interpreting MM of different complexity, and draw a number of interesting conclusions about the differences in nuclear morphology between follicular thyroid adenomas and carcinomas. We also demonstrate substantial advantages of multilevel models over conventional, single‐level statistics, which have been adopted previously to analyze karyometric data. In addition, some theoretical issues related to MM as well as major statistical software for MM are briefly reviewed.

The clinical course and interrelations of dementia related symptoms

2017 ◽  
Vol 30 (6) ◽  
pp. 859-866 ◽  
Author(s):  
Miriam L. Haaksma ◽  
Jeannie-Marie S. Leoutsakos ◽  
Jonne A. E. Bremer ◽  
Pauline Aalten ◽  
Inez H. G. B. Ramakers ◽  
...  
Keyword(s):  
Time Course ◽  
Multilevel Models ◽  
Growth Models ◽  
Neuropsychiatric Symptoms ◽  
Cognitive Status ◽  
Daily Functioning ◽  
Future Research ◽  
Dementia Severity ◽  
Highly Correlated ◽  
Over Time

ABSTRACTBackground:Dementia is a neurodegenerative syndrome that interferes with multiple aspects of life, including cognition, daily functioning, and behavior. Despite the large heterogeneity in symptom development, these three domains are seldom studied simultaneously. This study investigates how trajectories of these domains are interrelated within individuals over time, and how they in turn are related to dementia severity and quality of life (QoL).Methods:We used data from a longitudinal clinical cohort study, including 331 dementia patients. Cognitive status was measured using the Mini-Mental State Examination, daily functioning was measured with the disability assessment for dementia and neuropsychiatric symptoms (NPS) were scored using the neuropsychiatric inventory. We investigated the relationships in the time course of the various dementia domains using random effects multilevel models and parallel-process growth models.Results:Changes in cognition and daily functioning were highly correlated over time (r = 0.85, p < 0.01), as were changes in NPS and functioning (r = −0.60, p < 0.01), while changes in cognition and NPS were not (r = −0.20, p = 0.06). All three domains were strongly associated with dementia severity over time (p < 0.01). Decreased functioning and increased NPS were both associated with decreased QoL (β = 2.97, p < 0.01 and β = −2.41, p < 0.01, respectively), while cognition was not (β = 0.01, p = 0.93).Conclusion:This study demonstrates the heterogeneity of dementia progression between individuals and between different dementia domains within individuals. To improve our understanding of dementia progression, future research should embrace a broader perspective encompassing multiple outcome measures along with the patient's profile, including neurological factors as well as physical, social, and psychiatric health.

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