ReviewSSM - population health2020
Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices.
Review in SSM - population health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Ten Healthcare Delivery Trends and Their Measurement and Methodological Implications for Cancer Health Services Research.Health services research · 2026Article
- Variation in mental health-related sickness absence duration: The role of occupational health professionals.PloS one · 2026Article
- Where you live and where you receive care: Using cross-classified multilevel modeling to examine hospital and neighborhood variation in in-hospital mortality and mortality disparities.Annals of epidemiology · 2025Article
- Intersectional analysis for science and technology.Nature · 2025Review
- Best of Both Worlds: Bridging One Model for All and Group-Specific Model Approaches using Ensemble-based Subpopulation Modeling.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Article
- Implementation Research at NHLBI: Methodological and Design Challenges and Lessons Learned from the DECIPHeR Initiative.Ethnicity & disease · 2023Article
- Construction of multilevel statistical models in health research: Foundations and generalitiesBiomedica : revista del Instituto Nacional de Salud · 2023Article
- Adolescent alcohol use: use of social network analysis and cross-classified multilevel modeling to examine peer group, school, and neighborhood-level influences.The American journal of drug and alcohol abuse · 2023Article
- Adolescent individual, school, and neighborhood influences on young adult diabetes risk.Health & place · 2023Article
- Disparities in healthcare utilization by insurance status among patients with symptomatic peripheral artery disease.BMC health services research · 2023Article
- Restoring the missing person to personalized medicine and precision psychiatry.Frontiers in neuroscience · 2023Article
- Article
- Adolescent individual, school, and neighborhood influences on young adult hypertension risk.PloS one · 2022Article
- Four strategic areas to advance equitable implementation of evidence-based practices in cancer care.Translational behavioral medicine · 2021Article
- Measurement in Intensive Longitudinal Data.Structural equation modeling : a multidisciplinary journal · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Recognizing that health outcomes are influenced by and occur within multiple social and physical contexts, researchers have used multilevel modeling techniques for decades to analyze hierarchical or nested data. Cross-Classified Multilevel Models (CCMM) are a statistical technique proposed in the 1990s that extend standard multilevel modeling and enable the simultaneous analysis of non-nested multilevel data. Though use of CCMM in empirical health studies has become increasingly popular, there has not yet been a review summarizing how CCMM are used in the health literature. To address this gap, we performed a scoping review of empirical health studies using CCMM to: (a) evaluate the extent to which this statistical approach has been adopted; (b) assess the rationale and procedures for using CCMM; and (c) provide concrete recommendations for the future use of CCMM. We identified 118 CCMM papers published in English-language literature between 1994 and 2018. Our results reveal a steady growth in empirical health studies using CCMM to address a wide variety of health outcomes in clustered non-hierarchical data. Health researchers use CCMM primarily for five reasons: (1) to statistically account for non-independence in clustered data structures; out of substantive interest in the variance explained by (2) concurrent contexts, (3) contexts over time, and (4) age-period-cohort effects; and (5) to apply CCMM alongside other techniques within a joint model. We conclude by proposing a set of recommendations for use of CCMM with the aim of improved clarity and standardization of reporting in future research using this statistical approach.
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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.