Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices

Kathryn M Barker; Erin C Dunn; Tracy K Richmond; Sarah Ahmed; Matthew Hawrilenko; Clare R Evans

doi:10.1016/j.ssmph.2020.100661

Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices

SSM Popul Health. 2020 Aug 29:12:100661. doi: 10.1016/j.ssmph.2020.100661. eCollection 2020 Dec.

Authors

Kathryn M Barker¹, Erin C Dunn^{2

3

4}, Tracy K Richmond^{5

6}, Sarah Ahmed⁷, Matthew Hawrilenko⁸, Clare R Evans⁷

Affiliations

¹ Department of Medicine, University of California San Diego, La Jolla, CA, USA.
² Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA.
³ Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
⁴ Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁵ Department of Medicine, Division of Adolescent Medicine, Boston Children's Hospital, Boston, MA, USA.
⁶ Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
⁷ Department of Sociology, University of Oregon, Eugene, OR, USA.
⁸ Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA.

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.

Keywords: Age period cohort effects; Area-level effects; Contextual effects; Cross-classified multilevel modeling.

Publication types

Review

Abstract

Publication types

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