ArticleOphthalmic epidemiology2014
Structural equation modeling: a framework for ocular and other medical sciences research.
Article in Ophthalmic epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 42 citations in OpenAlex.
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- Interplay Among Reward Processing, Schizotypal Traits, and Psychosocial Stress in a Large Chinese Young Adult Sample: A Cross-Sectional Network Analysis.PsyCh journal · 2026Article
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- Statistical Methods in Aging Research: Improving Current Practices and Embracing Emerging Approaches.Annual review of statistics and its application · 2026Article
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- Dietary and lifestyle factors associated with cardiovascular disease risk: a structural equation modeling approach.BMC cardiovascular disorders · 2025Article
- Relationship between periodontal health-related knowledge, belief, and behaviors: a structural equation modeling approach.BMC oral health · 2025Article
- Sleep disturbance as a mediator of the relationship between perceived stress and demoralization in hemodialysis patients: a structural equation modeling analysis.Frontiers in psychiatry · 2025Article
- Is structural equation modeling applicable in a population health determinants assessment? An experience from the European Union.PloS one · 2025Article
- Exploring the influences of personal attitudes on the intention of continuing online grocery shopping after the COVID-19 pandemic.Travel behaviour & society · 2023Article
- Structural equation modelling of work related musculoskeletal disorders among dumper operators.Scientific reports · 2023Article
- Evaluation of the Risk Factors for Type 2 Diabetes Using the Generalized Structure Equation Modeling in Iranian Adults based on Shahedieh Cohort Study.Journal of diabetes and metabolic disorders · 2022Review
- Relative impact of red blood cell transfusion and anaemia on 5-year mortality in cardiac surgery.Interactive cardiovascular and thoracic surgery · 2021Article
- Social and physical environmental factors in daily stepping activity in those with chronic stroke.Topics in stroke rehabilitation · 2021Article
- Methods to Account for Uncertainty in Latent Class Assignments When Using Latent Classes as Predictors in Regression Models, with Application to Acculturation Strategy Measures.Epidemiology (Cambridge, Mass.) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Structural equation modeling (SEM) is a modeling framework that encompasses many types of statistical models and can accommodate a variety of estimation and testing methods. SEM has been used primarily in social sciences but is increasingly used in epidemiology, public health, and the medical sciences. SEM provides many advantages for the analysis of survey and clinical data, including the ability to model latent constructs that may not be directly observable. Another major feature is simultaneous estimation of parameters in systems of equations that may include mediated relationships, correlated dependent variables, and in some instances feedback relationships. SEM allows for the specification of theoretically holistic models because multiple and varied relationships may be estimated together in the same model. SEM has recently expanded by adding generalized linear modeling capabilities that include the simultaneous estimation of parameters of different functional form for outcomes with different distributions in the same model. Therefore, mortality modeling and other relevant health outcomes may be evaluated. Random effects estimation using latent variables has been advanced in the SEM literature and software. In addition, SEM software has increased estimation options. Therefore, modern SEM is quite general and includes model types frequently used by health researchers, including generalized linear modeling, mixed effects linear modeling, and population average modeling. This article does not present any new information. It is meant as an introduction to SEM and its uses in ocular and other health research.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.