Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices
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 19...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-12-01
|
| Series: | SSM: Population Health |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352827320302986 |
| _version_ | 1818647270067798016 |
|---|---|
| author | Kathryn M. Barker Erin C. Dunn Tracy K. Richmond Sarah Ahmed Matthew Hawrilenko Clare R. Evans |
| author_facet | Kathryn M. Barker Erin C. Dunn Tracy K. Richmond Sarah Ahmed Matthew Hawrilenko Clare R. Evans |
| author_sort | Kathryn M. Barker |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T00:59:52Z |
| format | Article |
| id | doaj.art-4cd0668ce01e4a60be97ccda4419b525 |
| institution | Directory Open Access Journal |
| issn | 2352-8273 |
| language | English |
| last_indexed | 2024-12-17T00:59:52Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SSM: Population Health |
| spelling | doaj.art-4cd0668ce01e4a60be97ccda4419b5252022-12-21T22:09:30ZengElsevierSSM: Population Health2352-82732020-12-0112100661Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practicesKathryn M. Barker0Erin C. Dunn1Tracy K. Richmond2Sarah Ahmed3Matthew Hawrilenko4Clare R. Evans5Department of Medicine, University of California San Diego, La Jolla, CA, USA; Corresponding author. Department of Medicine, University of California, San Diego, 9500 Gilman Drive #0507, La Jolla, CA, 92093-0507, USA.Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USADepartment of Medicine, Division of Adolescent Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USADepartment of Sociology, University of Oregon, Eugene, OR, USADepartment of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USADepartment of Sociology, University of Oregon, Eugene, OR, USARecognizing 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.http://www.sciencedirect.com/science/article/pii/S2352827320302986Cross-classified multilevel modelingArea-level effectsContextual effectsAge period cohort effects |
| spellingShingle | Kathryn M. Barker Erin C. Dunn Tracy K. Richmond Sarah Ahmed Matthew Hawrilenko Clare R. Evans Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices SSM: Population Health Cross-classified multilevel modeling Area-level effects Contextual effects Age period cohort effects |
| title | Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices |
| title_full | Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices |
| title_fullStr | Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices |
| title_full_unstemmed | Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices |
| title_short | Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices |
| title_sort | cross classified multilevel models ccmm in health research a systematic review of published empirical studies and recommendations for best practices |
| topic | Cross-classified multilevel modeling Area-level effects Contextual effects Age period cohort effects |
| url | http://www.sciencedirect.com/science/article/pii/S2352827320302986 |
| work_keys_str_mv | AT kathrynmbarker crossclassifiedmultilevelmodelsccmminhealthresearchasystematicreviewofpublishedempiricalstudiesandrecommendationsforbestpractices AT erincdunn crossclassifiedmultilevelmodelsccmminhealthresearchasystematicreviewofpublishedempiricalstudiesandrecommendationsforbestpractices AT tracykrichmond crossclassifiedmultilevelmodelsccmminhealthresearchasystematicreviewofpublishedempiricalstudiesandrecommendationsforbestpractices AT sarahahmed crossclassifiedmultilevelmodelsccmminhealthresearchasystematicreviewofpublishedempiricalstudiesandrecommendationsforbestpractices AT matthewhawrilenko crossclassifiedmultilevelmodelsccmminhealthresearchasystematicreviewofpublishedempiricalstudiesandrecommendationsforbestpractices AT clarerevans crossclassifiedmultilevelmodelsccmminhealthresearchasystematicreviewofpublishedempiricalstudiesandrecommendationsforbestpractices |