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.

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.

Developing Multilevel Models for Research

2015 ◽  
pp. 463-489 ◽  
Author(s):  
John Turner ◽  
Kristin Firmery Petrunin ◽  
Jeff Allen
Keyword(s):  
Multilevel Modeling ◽  
Multilevel Models ◽  
Research Process ◽  
Statistical Software ◽  
Single Level ◽  
The Past ◽  
Research Techniques ◽  
Research Findings ◽  
Level Analysis ◽  
Level Of Analysis

In the past, a large number of research efforts concentrated on single-level analysis; however, researchers who only conduct this level of analysis are finding it harder to justify due to the advancements in statistical software and research techniques. The validation of research findings comes partially from others replicating existing studies as well as building onto theories. Through replication and validation, the research process becomes cyclical in nature, and each iteration builds upon the next. Each succession of tests sets new boundaries, further verification, or falsification. For a model to be correctly specified, the level of analysis needs to be in congruence with the level of measurement. This chapter provides an overview of multilevel modeling for researchers and provides guides for the development and investigation of these models.

scholarly journals Multilevel Modeling With Stat-JR SAAs: A Software Review

2018 ◽  
Vol 44 (1) ◽  
pp. 103-121
Author(s):  
Minjung Kim ◽  
Hsien-Yuan Hsu
Keyword(s):  
High School ◽  
Education Research ◽  
Statistical Analysis ◽  
Multilevel Modeling ◽  
Hierarchical Structure ◽  
Multilevel Models ◽  
School Setting ◽  
Data Set ◽  
Statistical Software ◽  
Software Packages

Given the natural hierarchical structure in school-setting data, multilevel modeling (MLM) has been widely employed in education research using a number of different statistical software packages. The purpose of this article is to review a recent feature of Stat-JR, the statistical analysis assistants (SAAs) embedded in Stat-JR (Version 1.0.5), with regard to their use for MLM. In this article, we review the features of Stat-JR’s SAAs and illustrate how to implement SAAs, using one of the Stat-JR interfaces to analyze multilevel models for the 1982 High School and Beyond data set. Results from Stat-JR SAA are compared with the results using HLM7.01 software. We also discuss recommendations and implications for future users of SAAs.

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)

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 Models

2009 ◽  
Author(s):  
Bradford S. Jones
Keyword(s):  
Random Effects ◽  
Data Structures ◽  
Multilevel Model ◽  
Multilevel Models ◽  
Great Promise ◽  
Hierarchical Data ◽  
Hierarchical Data Structures ◽  
Fixed And Random Effects

This article addresses multilevel models in which units are nested within one another. The focus is primarily two-level models. It also describes cross-unit heterogeneity. Moreover, it assesses the fixed and random effects from the multilevel model. It generally tries to convey the scope of multilevel models but in a very compact way. Multilevel models provide great promise for exploiting information in hierarchical data structures. There are a range of alternatives for such data and it bears repeating that sometimes, simpler-to-apply correctives are best.

Exploring the Effects of Vehicular and Operational Characteristics on Bus Roadworthiness: An Application of the Multilevel Modeling Approach

2019 ◽  
Vol 2673 (4) ◽  
pp. 811-822 ◽  
Author(s):  
Jianrong Qiu ◽  
David B. Logan ◽  
Jennifer Oxley ◽  
Christopher Lowe
Keyword(s):  
Data Structure ◽  
Multilevel Modeling ◽  
Multilevel Analysis ◽  
Hierarchical Structure ◽  
Research Evidence ◽  
Hierarchical Data ◽  
Modeling Approach ◽  
Operational Characteristics ◽  
The Hierarchical Structure ◽  
Inspection Data

This paper examines the effects of vehicular and operational characteristics on bus roadworthiness. The analysis was based on annual bus inspection data in Victoria, Australia, between 2014 and 2017, consisting of 17,630 inspections of 6,447 vehicles run by 252 operators. A multilevel modeling approach was employed to account for the hierarchical data structure where inspections are nested within vehicles and vehicles within operators. The results offered insights into the effects on bus roadworthiness of characteristics attributable to inspections, vehicles, and operators. The probability of failing an inspection was found to be positively associated with vehicle age and odometer reading. Vehicle make played an important role in roadworthiness outcome, with the performance of different makes varying significantly. Small operators carried the highest risk of failure and large operators the lowest, irrespective of the location of operation. The multilevel analysis revealed that 28.9% of the variation in inspection outcomes occurred across operators and 5.2% across vehicles, which verified the presence of the hierarchical structure. The findings from this study provide safety regulators with solid research evidence to formulate policies aimed at enhancing bus roadworthiness.

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