Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multilevel modeling in psychosomatic medicine research.

ABSTRACT: The primary purpose of this study is to provide an overview of multilevel modeling for Psychosomatic Medicine readers and contributors. The article begins with a general introduction to multilevel modeling. Multilevel regression modeling at two levels is emphasized because of its prevalence in psychosomatic medicine research. Simulated data sets based on some core ideas from the Familias Unidas effectiveness study are used to illustrate key concepts including communication of model specification, parameter interpretation, sample size and power, and missing data. Input and key output files from Mplus and SAS are provided. A cluster randomized trial with repeated measures (i.e., three-level regression model) is then briefly presented with simulated data based on some core ideas from a cognitive-behavioral stress management intervention in prostate cancer.

SUBMITTER: Myers ND

PROVIDER: S-EPMC3498540 | biostudies-literature | 2012 Nov-Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Multilevel modeling in psychosomatic medicine research.

Myers Nicholas D ND Brincks Ahnalee M AM Ames Allison J AJ Prado Guillermo J GJ Penedo Frank J FJ Benedict Catherine C

Psychosomatic medicine 20121029 9

The primary purpose of this study is to provide an overview of multilevel modeling for Psychosomatic Medicine readers and contributors. The article begins with a general introduction to multilevel modeling. Multilevel regression modeling at two levels is emphasized because of its prevalence in psychosomatic medicine research. Simulated data sets based on some core ideas from the Familias Unidas effectiveness study are used to illustrate key concepts including communication of model specification ...[more]

PMID: 23107843

Similar Datasets

PSYChosomatic Medicine in ONcologIc and Cardiac Disease (PSYCHONIC) Study-A Retrospective and Prospective Observational Research Protocol.

Project description:Psychosocial factors play an important role in non-communicable diseases (NCDs). This observational study is primarily aimed at assessing the relationship of psychological characteristics of patients with the outcomes of different NCDs, and to assess short-term psychotherapy (STP) efficacy in the real world. Methods: One hundred and forty patients with recent acute myocardial infarction, Takotsubo syndrome, or non-metastatic breast cancer and a control group of 140 age and sex-matched healthy subjects, will be enrolled. All subjects will be administered psychometric tests, quality of life tests, a specific body perception questionnaire, a dream questionnaire, and a projective test, the Six Drawing test at baseline and follow-up. All subjects with medical conditions will be asked to freely choose between an ontopsychological STP along with standard medical therapy and, whenever indicated, rehabilitation therapy or medical therapy plus rehabilitation alone. The study endpoints will be to evaluate: the relationship of the psychological characteristics of enrolled subjects with the outcomes of different NCDs, predictors of the choice of psychotherapy, and the efficacy of ontopsychological intervention on psychological and medical outcomes. Conclusion: This study will generate data on distinctive psychological characteristics of patients suffering from different CDs and their relationship with medical outcomes, as well as explore the efficacy of ontopsychological STP in these patients in the real world. (Number of registration: NCT03437642).

| S-EPMC8709231 | biostudies-literature

Multilevel Modeling of Cognitive Diagnostic Assessment: The Multilevel DINA Example.

Project description:Many multilevel linear and item response theory models have been developed to account for multilevel data structures. However, most existing cognitive diagnostic models (CDMs) are unilevel in nature and become inapplicable when data have a multilevel structure. In this study, using the log-linear CDM as the item-level model, multilevel CDMs were developed based on the latent continuous variable approach and the multivariate Bernoulli distribution approach. In a series of simulations, the newly developed multilevel deterministic input, noisy, and gate (DINA) model was used as an example to evaluate the parameter recovery and consequences of ignoring the multilevel structures. The results indicated that all parameters in the new multilevel DINA were recovered fairly well by using the freeware Just Another Gibbs Sampler (JAGS) and that ignoring multilevel structures by fitting the standard unilevel DINA model resulted in poor estimates for the student-level covariates and underestimated standard errors, as well as led to poor recovery for the latent attribute profiles for individuals. An empirical example using the 2003 Trends in International Mathematics and Science Study eighth-grade mathematical test was provided.

| S-EPMC6297912 | biostudies-literature

Trends in ambulatory self-report: the role of momentary experience in psychosomatic medicine.

Project description:In this article, we review the differences between momentary, retrospective, and trait self-report techniques and discuss the unique role that ambulatory reports of momentary experience play in psychosomatic medicine. After a brief historical review of self-report techniques, we discuss the latest perspective that links ambulatory self-reports to a qualitatively different conscious self-the "experiencing self"--which is functionally and neuroanatomically different from the "remembering" and "believing" selves measured through retrospective and trait questionnaires. The experiencing self functions to navigate current environments and is relatively more tied to the salience network and corporeal information from the body that regulates autonomic processes. As evidence, we review research showing that experiences measured through ambulatory assessment have stronger associations with cardiovascular reactivity, cortisol response, immune system function, and threat/reward biomarkers compared with memories or beliefs. By contrast, memories and beliefs play important roles in decision making and long-term planning, but they are less tied to bodily processes and more tied to default/long-term memory networks, which minimizes their sensitivity for certain research questions. We conclude with specific recommendations for using self-report questionnaires in psychosomatic medicine and suggest that intensive ambulatory assessment of experiences may provide greater sensitivity for connecting psychological with biologic processes.

| S-EPMC3372543 | biostudies-literature

The Theoretical Construction of a Classification of Clinical Somatic Symptoms in Psychosomatic Medicine Theory.

Project description:ObjectiveThis article adopts the perspective of psychosomatic medicine to present and test a theoretical model of the classification of clinical somatic symptoms. The theoretical model consists of four dimensions: emotional somatic symptoms, biological somatic symptoms, imaginative somatic symptoms, and cognitive somatic symptoms.MethodA clinical somatic symptom classification scale was developed according to the theoretical model. A total of 542 participants completed the clinical somatic symptoms classification scale. The data were analyzed using exploratory and confirmatory factor analyses.ResultsThe results confirmed the theoretical model. The analyses found that the proposed theoretical structure of the scale was good, as indicated by factor loadings and fit indices, and that the scale had good reliability and construct validity.ConclusionsBased on the interpretation of the clinical symptoms of psychosomatic medicine, the treatment of chronic non-infectious diseases includes at least three dimensions: the first is the etiological treatment, the second is the pathophysiological and pathopsychological dimension, and the third is symptomatic treatment. The unified psychosomatic point of view and diverse clinical thinking modes are aimed at identifying different classes of somatic symptoms and important prerequisites for the treatment of these symptoms. We registered the study with the Chinese Clinical Trial Registry and it was approved by the West China Hospital, Sichuan University ethics committee.Trial registrationThe registration number is ChiCTR-OCS-14004632 (time: 2014-05-12).

| S-EPMC4985123 | biostudies-literature

Pharmacogenomics on the Somalia population using the Axiom Precision Medicine Research Array

Project description:* To evaluate genome-wide drug variants in the Somali population * To be able to detect pharmacogenetic variants of importance in the Somali populaion * To infer HLA alleles and blood group variants in Somalia

2020-03-19 | E-MTAB-8331 | biostudies-arrayexpress

Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study.

Project description:BACKGROUND:Artificial intelligence (AI)-based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field. OBJECTIVE:This study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018. METHODS:We obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape. RESULTS:The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources. CONCLUSIONS:The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.

| S-EPMC6858616 | biostudies-literature

Multilevel pharmacokinetics-driven modeling of metabolomics data.

Project description:IntroductionMultilevel modeling is a quantitative statistical method to investigate variability and relationships between variables of interest, taking into account population structure and dependencies. It can be used for prediction, data reduction and causal inference from experiments and observational studies allowing for more efficient elucidation of knowledge.ObjectivesIn this study we introduced the concept of multilevel pharmacokinetics (PK)-driven modelling for large-sample, unbalanced and unadjusted metabolomics data comprising nucleoside and creatinine concentration measurements in urine of healthy and cancer patients.MethodsA Bayesian multilevel model was proposed to describe the nucleoside and creatinine concentration ratio considering age, sex and health status as covariates. The predictive performance of the proposed model was summarized via area under the ROC, sensitivity and specificity using external validation.ResultsCancer was associated with an increase in methylthioadenosine/creatinine excretion rate by a factor of 1.42 (1.09-2.03) which constituted the highest increase among all nucleosides. Age influenced nucleosides/creatinine excretion rates for all nucleosides in the same direction which was likely caused by a decrease in creatinine clearance with age. There was a small evidence of sex-related differences for methylthioadenosine. The individual a posteriori prediction of patient classification as area under the ROC with 5th and 95th percentile was 0.57(0.5-0.67) with sensitivity and specificity of 0.59(0.42-0.76) and 0.57(0.45-0.7), respectively suggesting limited usefulness of 13 nucleosides/creatinine urine concentration measurements in predicting disease in this population.ConclusionBayesian multilevel pharmacokinetics-driven modeling in metabolomics may be useful in understanding the data and may constitute a new tool for searching towards potential candidates of disease indicators.

| S-EPMC5306155 | biostudies-literature

Designing cultural multilevel selection research for sustainability science.

Project description:Humans stand out among animals in that we cooperate in large groups to exploit natural resources, and accumulate resource exploitation techniques across generations via cultural learning. This uniquely human form of adaptability is in large part to blame for the global sustainability crisis. This paper builds on cultural evolutionary theory to conceptualize and study environmental resource use and overexploitation. Human social learning and cooperation, particularly regarding social dilemmas, result in both sustainability crises and solutions. Examples include the collapse of global fisheries, and multilateral agreements to halt ozone depletion. We propose an explicitly evolutionary approach to study how crises and solutions may emerge, persist, or disappear. We first present a brief primer on cultural evolution to define group-level cultural adaptations for resource use. This includes criteria for identifying where group-level cultural adaptations may exist, and if a cultural evolutionary approach can be implemented in studying a given system. We then outline a step-by-step process for designing a study of group-level cultural adaptation, including the major methodological considerations that researchers should address in study design, such as tradeoffs between validity and control, issues of time scale, and the value of both qualitative and quantitative data and analysis. We discuss how to evaluate multiple types of evidence synthetically, including historical accounts, new and existing data sets, case studies, and simulations. The electronic supplement provides a tutorial and simple computer code in the R environment to lead users from theory to data to an illustration of an empirical test for group-level adaptations in sustainability research.

| S-EPMC6086275 | biostudies-other

Multilevel quantile function modeling with application to birth outcomes.

Project description:Infants born preterm or small for gestational age have elevated rates of morbidity and mortality. Using birth certificate records in Texas from 2002 to 2004 and Environmental Protection Agency air pollution estimates, we relate the quantile functions of birth weight and gestational age to ozone exposure and multiple predictors, including parental age, race, and education level. We introduce a semi-parametric Bayesian quantile approach that models the full quantile function rather than just a few quantile levels. Our multilevel quantile function model establishes relationships between birth weight and the predictors separately for each week of gestational age and between gestational age and the predictors separately across Texas Public Health Regions. We permit these relationships to vary nonlinearly across gestational age, spatial domain and quantile level and we unite them in a hierarchical model via a basis expansion on the regression coefficients that preserves interpretability. Very low birth weight is a primary concern, so we leverage extreme value theory to supplement our model in the tail of the distribution. Gestational ages are recorded in completed weeks of gestation (integer-valued), so we present methodology for modeling quantile functions of discrete response data. In a simulation study we show that pooling information across gestational age and quantile level substantially reduces MSE of predictor effects. We find that ozone is negatively associated with the lower tail of gestational age in south Texas and across the distribution of birth weight for high gestational ages. Our methods are available in the R package BSquare.

| S-EPMC6601633 | biostudies-other

The Genomic Medicine Integrative Research Framework: A Conceptual Framework for Conducting Genomic Medicine Research.

Project description:Conceptual frameworks are useful in research because they can highlight priority research domains, inform decisions about interventions, identify outcomes and factors to measure, and display how factors might relate to each other to generate and test hypotheses. Discovery, translational, and implementation research are all critical to the overall mission of genomic medicine and prevention, but they have yet to be organized into a unified conceptual framework. To fill this gap, our diverse team collaborated to develop the Genomic Medicine Integrative Research (GMIR) Framework, a simple but comprehensive tool to aid the genomics community in developing research questions, strategies, and measures and in integrating genomic medicine and prevention into clinical practice. Here we present the GMIR Framework and its development, along with examples of its use for research development, demonstrating how we applied it to select and harmonize measures for use across diverse genomic medicine implementation projects. Researchers can utilize the GMIR Framework for their own research, collaborative investigations, and clinical implementation efforts; clinicians can use it to establish and evaluate programs; and all stakeholders can use it to help allocate resources and make sure that the full complexity of etiology is included in research and program design, development, and evaluation.

| S-EPMC6556906 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data