{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T09:38:07Z","timestamp":1793093887186,"version":"4.5.0"},"reference-count":59,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Dent Res"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:p>Multilevel modeling (MLM) is increasingly applied in oral health research to reflect the hierarchical structure of dental data, in which teeth are nested within individuals and individuals are embedded within social and environmental contexts. By partitioning both within- and between-individual variability, MLM improves estimate precision, reduces bias, and strengthens causal interpretation, thereby enhancing the evidence base for preventive strategies, clinical practice, and public health policy. This review synthesizes recent methodological developments and ongoing challenges in the application of MLM to oral health research, with particular emphasis on causal inference. Key issues include reciprocal relationships between individual- and contextual-level factors, establishing temporality, collinearity among contextual variables, population mobility, and the selection of appropriate geographic or institutional levels. We also highlight methodological advances, such as integrating propensity score weighting within multilevel frameworks, which offer promising approaches to improving causal assessment in observational studies. Accumulating evidence demonstrates the importance of contextual determinants, such as school and neighborhood environments, in shaping oral health outcomes across the life course. However, causal inference regarding these distal exposures remains underdeveloped. Persistent threats to validity include measurement error in contextual indicators, unmeasured multilevel confounding, and limited consideration of cross-level interactions. Using a school-based example, we illustrate how food insecurity mediates the association between household income and active dental caries, highlighting the value of analytical approaches that jointly address individual- and contextual-level determinants. Overall, MLM provides a robust framework for clarifying causal pathways, improving analytical rigor, and guiding interventions that target both behavioral and structural drivers of oral health inequalities.<\/jats:p>","DOI":"10.1177\/00220345261452997","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T08:19:09Z","timestamp":1783585149000},"page":"1248-1257","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Multilevel Modeling for Causal Inference in Oral Health Research"],"prefix":"10.1177","volume":"105","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7864-1401","authenticated-orcid":false,"given":"H.","family":"Li","sequence":"first","affiliation":[{"name":"National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore"},{"name":"Oral Health ACP, Health Services and Systems Research Programme (HSSR), Duke-NUS Medical School, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7899-1460","authenticated-orcid":false,"given":"M.C.M.","family":"Wong","sequence":"additional","affiliation":[{"name":"Applied Oral Health and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2468-6655","authenticated-orcid":false,"given":"R.K.","family":"Celeste","sequence":"additional","affiliation":[{"name":"Department of Preventive and Social Dentistry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil"},{"name":"Aging Research Center, Karolinska Institutet, Solna, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8329-2808","authenticated-orcid":false,"given":"M.A.","family":"Peres","sequence":"additional","affiliation":[{"name":"National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore"},{"name":"Oral Health ACP, Health Services and Systems Research Programme (HSSR), Duke-NUS Medical School, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"crossref","unstructured":"Albandar JM Goldstein H. 1992. Multi-level statistical models in studies of periodontal diseases. J Periodontol. 63(8):690\u2013695. https:\/\/doi.org\/10.1902\/jop.1992.63.8.690","DOI":"10.1902\/jop.1992.63.8.690"},{"key":"e_1_3_3_3_1","doi-asserted-by":"crossref","unstructured":"Antunes JL Peres MA de Campos Mello TR Waldman EA. 2006. Multilevel assessment of determinants of dental caries experience in Brazil. Community Dent Oral Epidemiol. 34(2):146\u2013152. https:\/\/doi.org\/10.1111\/j.1600-0528.2006.00274.x","DOI":"10.1111\/j.1600-0528.2006.00274.x"},{"key":"e_1_3_3_4_1","doi-asserted-by":"crossref","unstructured":"Arpino B Mealli F. 2011. The specification of the propensity score in multilevel observational studies. Comput Stat Data Anal. 55(4):1770\u20131780. https:\/\/doi.org\/10.1016\/j.csda.2010.11.008","DOI":"10.1016\/j.csda.2010.11.008"},{"key":"e_1_3_3_5_1","doi-asserted-by":"crossref","unstructured":"Austin PC. 2011. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 46(3):399\u2013424. https:\/\/doi.org\/10.1080\/00273171.2011.568786","DOI":"10.1080\/00273171.2011.568786"},{"key":"e_1_3_3_6_1","doi-asserted-by":"crossref","unstructured":"Austin PC. 2020. Advances in propensity score analysis. Stat Methods Med Res. 29(3):641\u2013643. https:\/\/doi.org\/10.1177\/0962280219899248","DOI":"10.1177\/0962280219899248"},{"key":"e_1_3_3_7_1","doi-asserted-by":"crossref","unstructured":"Bates D M\u00e4chler M Bolker B Walker S. 2015. Fitting linear mixed-effects models using lme4. J Stat Softw. 67(1):1\u201348. https:\/\/doi.org\/10.18637\/jss.v067.i01","DOI":"10.18637\/jss.v067.i01"},{"key":"e_1_3_3_8_1","first-page":"16","volume-title":"Hierarchical linear models for social and behavioral research: applications and data analysis methods","author":"Bryk AS","year":"1992","unstructured":"Bryk AS, Raudenbush SW. 1992. Hierarchical linear models for social and behavioral research: applications and data analysis methods. Sage. p 16\u201361."},{"key":"e_1_3_3_9_1","doi-asserted-by":"crossref","unstructured":"Burgette JM Preisser JS Rozier RG. 2016. Propensity score weighting: an application to an Early Head Start Dental Study. J Public Health Dent. 76(1):17\u201329. https:\/\/doi.org\/10.1111\/jphd.12106","DOI":"10.1111\/jphd.12106"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v080.i01"},{"key":"e_1_3_3_11_1","doi-asserted-by":"crossref","unstructured":"Burnham KP Anderson DR. 2004. Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res. 33(2):261\u2013304. https:\/\/doi.org\/10.1177\/0049124104268644","DOI":"10.1177\/0049124104268644"},{"key":"e_1_3_3_12_1","doi-asserted-by":"crossref","unstructured":"Cafri G Wang W Chan PH Austin PC. 2019. A review and empirical comparison of causal inference methods for clustered observational data with application to the evaluation of the effectiveness of medical devices. Stat Methods Med Res. 28(10\u201311):3142\u20133162. https:\/\/doi.org\/10.1177\/0962280218799540","DOI":"10.1177\/0962280218799540"},{"key":"e_1_3_3_13_1","doi-asserted-by":"crossref","unstructured":"Celeste RK Eyj\u00f3lfsd\u00f3ttir HS Lennartsson C Fritzell J. 2020. Socioeconomic life course models and oral health: a longitudinal analysis. J Dent Res. 99(3):257\u2013263. https:\/\/doi.org\/10.1177\/0022034520901709","DOI":"10.1177\/0022034520901709"},{"key":"e_1_3_3_14_1","doi-asserted-by":"crossref","unstructured":"Chang TH Stuart EA. 2022. Propensity score methods for observational studies with clustered data: a review. Stat Med. 41(18):3612\u20133626. https:\/\/doi.org\/10.1002\/sim.9437","DOI":"10.1002\/sim.9437"},{"key":"e_1_3_3_15_1","volume-title":"MLwiN version 3.14","author":"Charlton C","year":"2025","unstructured":"Charlton C, Rasbash J, Browne WJ, Healy M, Cameron B. 2025. MLwiN version 3.14. Centre for Multilevel Modelling, University of Bristol."},{"key":"e_1_3_3_16_1","first-page":"231","volume-title":"Periodontitis: symptoms, prevention and treatment options","author":"Cheung K","year":"2019","unstructured":"Cheung K, Wong MCM, Lam KF, Corbet EF, Leung WK. 2019. Multilevel modelling for the analysis of longitudinal periodontal data. In: Leung WK, Goh V, editors. Periodontitis: symptoms, prevention and treatment options. Nova Science. p 231\u2013278."},{"key":"e_1_3_3_17_1","doi-asserted-by":"crossref","unstructured":"Cho SJ De Boeck P Naveiras M Ervin H. 2022. Level-specific residuals and diagnostic measures plots and tests for random effects selection in multilevel and mixed models. Behav Res Methods. 54(5):2178\u20132220. https:\/\/doi.org\/10.3758\/s13428-021-01709-z","DOI":"10.3758\/s13428-021-01709-z"},{"key":"e_1_3_3_18_1","doi-asserted-by":"crossref","unstructured":"Diez-Roux AV. 1998. Bringing context back into epidemiology: variables and fallacies in multilevel analysis. Am J Public Health. 88(2):216\u2013222. https:\/\/doi.org\/10.2105\/ajph.88.2.216","DOI":"10.2105\/AJPH.88.2.216"},{"key":"e_1_3_3_19_1","doi-asserted-by":"crossref","unstructured":"Diez-Roux AV. 2000. Multilevel analysis in public health research. Annu Rev Public Health. 21:171\u2013192. https:\/\/doi.org\/10.1146\/annurev.publhealth.21.1.171","DOI":"10.1146\/annurev.publhealth.21.1.171"},{"key":"e_1_3_3_20_1","doi-asserted-by":"crossref","unstructured":"Diez Roux AV. 2002. A glossary for multilevel analysis. J Epidemiol Community Health. 56(8):588\u2013594. https:\/\/doi.org\/10.1136\/jech.56.8.588","DOI":"10.1136\/jech.56.8.588"},{"key":"e_1_3_3_21_1","doi-asserted-by":"crossref","unstructured":"Diez Roux AV. 2004. Estimating neighborhood health effects: the challenges of causal inference in a complex world. Soc Sci Med. 58(10):1953\u20131960. https:\/\/doi.org\/10.1016\/s0277-9536(03)00414-3","DOI":"10.1016\/S0277-9536(03)00414-3"},{"key":"e_1_3_3_22_1","doi-asserted-by":"crossref","unstructured":"Enders CK Mistler SA Keller BT. 2016. Multilevel multiple imputation: a review and evaluation of joint modeling and chained equations imputation. Psychol Methods. 21(2):222\u2013240. https:\/\/doi.org\/10.1037\/met0000063","DOI":"10.1037\/met0000063"},{"key":"e_1_3_3_23_1","doi-asserted-by":"crossref","unstructured":"Enders CK Tofighi D. 2007. Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychol Methods. 12(2):121\u2013138. https:\/\/doi.org\/10.1037\/1082-989x.12.2.121","DOI":"10.1037\/1082-989X.12.2.121"},{"key":"e_1_3_3_24_1","doi-asserted-by":"crossref","unstructured":"Fuentes A L\u00fcdtke O Robitzsch A. 2022. Causal inference with multilevel data: a comparison of different propensity score weighting approaches. Multivariate Behav Res. 57(6):916\u2013939. https:\/\/doi.org\/10.1080\/00273171.2021.1925521","DOI":"10.1080\/00273171.2021.1925521"},{"key":"e_1_3_3_25_1","doi-asserted-by":"crossref","unstructured":"Goldstein H. 1986. Multilevel mixed linear-model analysis using iterative generalized least-squares. Biometrika 73(1):43\u201356. https:\/\/doi.org\/10.1093\/biomet\/73.1.43","DOI":"10.1093\/biomet\/73.1.43"},{"key":"e_1_3_3_26_1","first-page":"19","author":"Goldstein H","year":"1995","unstructured":"Goldstein H. 1995. Multilevel statistical model. Edward Arnold. p 19\u201380.","journal-title":"Multilevel statistical model. Edward Arnold"},{"key":"e_1_3_3_27_1","doi-asserted-by":"crossref","unstructured":"Goldstein H Browne W Rasbash J. 2002. Partitioning variation in multilevel models. Underst Stat. 1(4):223\u2013231. https:\/\/doi.org\/10.1207\/S15328031US0104_02","DOI":"10.1207\/S15328031US0104_02"},{"key":"e_1_3_3_28_1","doi-asserted-by":"crossref","unstructured":"Grund S L\u00fcdtke O Robitzsch A. 2016. Multiple imputation of multilevel missing data: an introduction to the R package pan. Sage Open. 6(4). https:\/\/doi.org\/10.1177\/2158244016668220","DOI":"10.1177\/2158244016668220"},{"key":"e_1_3_3_29_1","volume-title":"Modern epidemiology","author":"Haneuse S","year":"2021","unstructured":"Haneuse S. 2021. Regression analysis part II: model fitting and assessment. In: Lash TL, VanderWeele TJ, Haneuse S, Rothman KJ, editors. Modern epidemiology. 4th ed. Philadelphia (PA): Wolters Kluwer. p. 467\u2013496.","edition":"4"},{"key":"e_1_3_3_30_1","volume-title":"Longitudinal data analysis","author":"Hedeker D","year":"2006","unstructured":"Hedeker D, Gibbons R. 2006. Longitudinal data analysis. Wiley-Interscience."},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.4324\/9781315650982"},{"key":"e_1_3_3_32_1","doi-asserted-by":"crossref","unstructured":"Lee Y Nguyen TQ Stuart EA. 2021. Partially pooled propensity score models for average treatment effect estimation with multilevel data. J R Stat Soc Series A 184(4):1578\u20131598. https:\/\/doi.org\/10.1111\/rssa.12741","DOI":"10.1111\/rssa.12741"},{"key":"e_1_3_3_33_1","doi-asserted-by":"crossref","unstructured":"Li F et al. 2021. Trend on dental caries status and its risk indicators in children aged 12 years in China: a multilevel analysis based on the repeated national cross-sectional surveys in 2005 and 2015. BMC Public Health. 21(1):2285. https:\/\/doi.org\/10.1186\/s12889-021-12262-x","DOI":"10.1186\/s12889-021-12262-x"},{"key":"e_1_3_3_34_1","doi-asserted-by":"crossref","unstructured":"Listl S Galloway J Mossey PA Marcenes W. 2015. Global economic impact of dental diseases. J Dent Res. 94(10):1355\u20131361. https:\/\/doi.org\/10.1177\/0022034515602879","DOI":"10.1177\/0022034515602879"},{"key":"e_1_3_3_35_1","doi-asserted-by":"crossref","unstructured":"Loy A Hofmann H. 2014. HLMdiag: a suite of diagnostics for hierarchical linear models in R. J Stat Softw. 56(5):1\u201328. https:\/\/doi.org\/10.18637\/jss.v056.i05","DOI":"10.18637\/jss.v056.i05"},{"key":"e_1_3_3_36_1","doi-asserted-by":"crossref","unstructured":"Maas CJM Hox JJ. 2005. Sufficient sample sizes for multilevel modeling. Methodology. 1(3):86\u201392. https:\/\/doi.org\/10.1027\/1614-2241.1.3.86","DOI":"10.1027\/1614-2241.1.3.86"},{"key":"e_1_3_3_37_1","doi-asserted-by":"crossref","unstructured":"Mabangkhru S Duangthip D Chu CH Phonghanyudh A Jirarattanasopha V. 2020. A randomized clinical trial to arrest dentin caries in young children using silver diamine fluoride. J Dent. 99:103375. https:\/\/doi.org\/10.1016\/j.jdent.2020.103375","DOI":"10.1016\/j.jdent.2020.103375"},{"key":"e_1_3_3_38_1","doi-asserted-by":"crossref","unstructured":"Morgenstern H. 1982. Uses of ecologic analysis in epidemiologic research. Am J Public Health. 72(12):1336\u20131344. https:\/\/doi.org\/10.2105\/ajph.72.12.1336","DOI":"10.2105\/AJPH.72.12.1336"},{"key":"e_1_3_3_39_1","doi-asserted-by":"crossref","unstructured":"Morgenstern H. 1995. Ecologic studies in epidemiology: concepts principles and methods. Annu Rev Public Health. 16:61\u201381. https:\/\/doi.org\/10.1146\/annurev.pu.16.050195.000425","DOI":"10.1146\/annurev.pu.16.050195.000425"},{"key":"e_1_3_3_40_1","doi-asserted-by":"crossref","unstructured":"Muth C Oravecz Z Gabry J. 2018. User-friendly bayesian regression modeling: a tutorial with rstanarm and shinystan. Quant Meth Psychol. 14(2):99\u2013119. https:\/\/doi.org\/10.20982\/tqmp.14.2.p099","DOI":"10.20982\/tqmp.14.2.p099"},{"key":"e_1_3_3_41_1","doi-asserted-by":"crossref","unstructured":"Newton JT Bower EJ. 2005. The social determinants of oral health: new approaches to conceptualizing and researching complex causal networks. Community Dent Oral Epidemiol. 33(1):25\u201334. https:\/\/doi.org\/10.1111\/j.1600-0528.2004.00190.x","DOI":"10.1111\/j.1600-0528.2004.00190.x"},{"key":"e_1_3_3_42_1","doi-asserted-by":"crossref","unstructured":"Peres MA et al. 2019. Oral diseases: a global public health challenge. Lancet. 394(10194):249\u2013260. https:\/\/doi.org\/10.1016\/s0140-6736(19)31146-8","DOI":"10.1016\/S0140-6736(19)31146-8"},{"key":"e_1_3_3_43_1","doi-asserted-by":"crossref","unstructured":"Piovesan ETA Bernabe E. 2025. Municipal oral healthcare coverage and dental caries among Brazilian children. Caries Res. [published online ahead of print]. https:\/\/doi.org\/10.1159\/000548560","DOI":"10.1159\/000548560"},{"key":"e_1_3_3_44_1","doi-asserted-by":"crossref","unstructured":"Preacher KJ. 2011. Multilevel sem strategies for evaluating mediation in three-level data. Multivariate Behav Res. 46(4):691\u2013731. https:\/\/doi.org\/10.1080\/00273171.2011.589280","DOI":"10.1080\/00273171.2011.589280"},{"key":"e_1_3_3_45_1","doi-asserted-by":"crossref","unstructured":"Preacher KJ Zyphur MJ Zhang Z. 2010. A general multilevel SEM framework for assessing multilevel mediation. Psychol Methods. 15(3):209\u2013233. https:\/\/doi.org\/10.1037\/a0020141","DOI":"10.1037\/a0020141"},{"key":"e_1_3_3_46_1","first-page":"16","volume-title":"Hierarchical linear models: applications and data analysis methods","author":"Raudenbush SW","year":"2002","unstructured":"Raudenbush SW, Bryk AS. 2002. Hierarchical linear models: applications and data analysis methods. Sage. p 16\u2013100."},{"key":"e_1_3_3_47_1","doi-asserted-by":"crossref","unstructured":"Reichenheim ME et al. 2016. A model-based approach to identify classes and respective cutoffs of the brazilian household food insecurity measurement scale. J Nutr. 146(7):1356\u20131364. https:\/\/doi.org\/10.3945\/jn.116.231845","DOI":"10.3945\/jn.116.231845"},{"key":"e_1_3_3_48_1","doi-asserted-by":"crossref","unstructured":"Rosenbaum PR Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika. 70(1):41\u201355. https:\/\/doi.org\/10.1093\/biomet\/70.1.41","DOI":"10.1093\/biomet\/70.1.41"},{"key":"e_1_3_3_49_1","doi-asserted-by":"crossref","unstructured":"Sabbah W Tsakos G Chandola T Sheiham A Watt RG. 2007. Social gradients in oral and general health. J Dent Res. 86(10):992\u2013996. https:\/\/doi.org\/10.1177\/154405910708601014","DOI":"10.1177\/154405910708601014"},{"key":"e_1_3_3_50_1","doi-asserted-by":"crossref","unstructured":"Savitz DA Wellenius GA. 2023. Can cross-sectional studies contribute to causal inference? It depends. Am J Epidemiol. 192(4):514\u2013516. https:\/\/doi.org\/10.1093\/aje\/kwac037","DOI":"10.1093\/aje\/kwac037"},{"key":"e_1_3_3_51_1","doi-asserted-by":"crossref","unstructured":"Segall-Corr\u00eaa AM Mar\u00edn-Le\u00f3n L Melgar-Qui\u00f1onez H P\u00e9rez-Escamilla R. 2014. Refinement of the Brazilian household food insecurity measurement scale: recommendation for a 14-item EBIA. Rev Nutr. 27(2):241\u2013251. https:\/\/doi.org\/10.1590\/1415-52732014000200010","DOI":"10.1590\/1415-52732014000200010"},{"key":"e_1_3_3_52_1","doi-asserted-by":"crossref","unstructured":"Shetty V et al. 2016. Methamphetamine users have increased dental disease: a propensity score analysis. J Dent Res. 95(7):814\u2013821. https:\/\/doi.org\/10.1177\/0022034516640478","DOI":"10.1177\/0022034516640478"},{"key":"e_1_3_3_53_1","volume-title":"Multilevel analysis: an introduction to basic and advanced multilevel modeling","author":"Snijders T","year":"2012","unstructured":"Snijders T, Bosker R. 2012. Multilevel analysis: an introduction to basic and advanced multilevel modeling. 2nd ed. Sage.","edition":"2"},{"key":"e_1_3_3_54_1","unstructured":"Su Y Cortina J. 2009. What do we gain? Combining propensity score methods and multilevel modeling. Paper presented at: APSA 2009 Annual Meeting; Toronto. https:\/\/ssrn.com\/abstract=1450058"},{"key":"e_1_3_3_55_1","doi-asserted-by":"crossref","unstructured":"Tadakamadla SK Rathore V Mitchell AE Johnson N Morawska A. 2022. Protocol of a cluster randomised controlled trial evaluating the effectiveness of an online parenting intervention for promoting oral health of 2-6 years old Australian children. BMJ Open. 12(10):e056269. https:\/\/doi.org\/10.1136\/bmjopen-2021-056269","DOI":"10.1136\/bmjopen-2021-056269"},{"key":"e_1_3_3_56_1","doi-asserted-by":"crossref","unstructured":"Thoemmes FJ West SG. 2011. The use of propensity scores for nonrandomized designs with clustered data. Multivariate Behav Res. 46(3):514\u2013543. https:\/\/doi.org\/10.1080\/00273171.2011.569395","DOI":"10.1080\/00273171.2011.569395"},{"key":"e_1_3_3_57_1","first-page":"1","volume-title":"Linear mixed models for longitudinal data","author":"Verbeke G","year":"2000","unstructured":"Verbeke G, Molenberghs G. 2000. Linear mixed models for longitudinal data. Springer. p 1\u201392."},{"key":"e_1_3_3_58_1","doi-asserted-by":"crossref","unstructured":"Wan CP et al. 2009. Effects of smoking on healing response to non-surgical periodontal therapy: a multilevel modelling analysis. J Clin Periodontol. 36(3):229\u2013239. https:\/\/doi.org\/10.1111\/j.1600-051x.2008.01371.x","DOI":"10.1111\/j.1600-051X.2008.01371.x"},{"key":"e_1_3_3_59_1","doi-asserted-by":"crossref","unstructured":"Wierichs RJ et al. 2025. Practice-based study on CAD\/CAM inlays onlays and crowns: longevity and risks. J Dent Res. 104(11):1218\u20131227. https:\/\/doi.org\/10.1177\/00220345251332882","DOI":"10.1177\/00220345251332882"},{"key":"e_1_3_3_60_1","doi-asserted-by":"crossref","unstructured":"Zhang Z Zyphur MJ Preacher KJ. 2009. Testing multilevel mediation using hierarchical linear models: problems and solutions. Organizational Research Methods. 12(4):695\u2013719. https:\/\/doi.org\/10.1177\/1094428108327450","DOI":"10.1177\/1094428108327450"}],"container-title":["Journal of Dental Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00220345261452997","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/00220345261452997","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00220345261452997","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T09:24:15Z","timestamp":1793093055000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/00220345261452997"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,9]]},"references-count":59,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10.1177\/00220345261452997"],"URL":"https:\/\/doi.org\/10.1177\/00220345261452997","relation":{},"ISSN":["0022-0345","1544-0591"],"issn-type":[{"value":"0022-0345","type":"print"},{"value":"1544-0591","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,9]]}}}