Hostname: page-component-745bb68f8f-g4j75 Total loading time: 0 Render date: 2025-01-07T18:31:23.321Z Has data issue: false hasContentIssue false
  • English
  • Français

Nonlinear Predictive Models for Multiple Mediation Analysis: With an Application to Explore Ethnic Disparities in Anxiety and Depression Among Cancer Survivors

Published online by Cambridge University Press:  01 January 2025

Qingzhao Yu Open the ORCID record for Qingzhao Yu [Opens in a new window] ,
Kaelen L. Medeiros ,
Xiaocheng Wu  and
Roxanne E. Jensen
Qingzhao Yu*
Affiliation:
Louisiana State University Health Sciences Center
Kaelen L. Medeiros
Affiliation:
American College of Surgeon
Xiaocheng Wu
Affiliation:
Louisiana Tumor Registry
Roxanne E. Jensen
Affiliation:
Lombardi Comprehensive Cancer Center
*
Correspondence should be made to Qingzhao Yu, Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, 3rd Floor, 2020 Gravier Street, New Orleans, LA 70112, USA. Email: qyu@lsuhsc.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Mediation analysis allows the examination of effects of a third variable (mediator/confounder) in the causal pathway between an exposure and an outcome. The general multiple mediation analysis method (MMA), proposed by Yu et al., improves traditional methods (e.g., estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. Previous studies find that compared with non-Hispanic cancer survivors, Hispanic survivors are more likely to endure anxiety and depression after cancer diagnoses. In this paper, we applied MMA on MY-Health study to identify mediators/confounders and quantify the indirect effect of each identified mediator/confounder in explaining ethnic disparities in anxiety and depression among cancer survivors who enrolled in the study. We considered a number of socio-demographic variables, tumor characteristics, and treatment factors as potential mediators/confounders and found that most of the ethnic differences in anxiety or depression between Hispanic and non-Hispanic white cancer survivors were explained by younger diagnosis age, lower education level, lower proportions of employment, less likely of being born in the USA, less insurance, and less social support among Hispanic patients.

Keywords

ethnic disparityhealth-related quality of lifemediation/confounding analysisMY-Health studynonlinear models
Type
Original Paper
Information
Psychometrika , Volume 83 , Issue 4 , December 2018 , pp. 991 - 1006
Copyright
Copyright © 2018 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-018-9612-2) contains supplementary material, which is available to authorized users.

References

Albert, J. M.(2008).Mediation analysis via potential outcomes models.Statistics in Medicine,27(8),12821304.CrossRefGoogle ScholarPubMed
Alwin, D. F.,Hauser, R. M.(1975).The decomposition of effects in path analysis.American Sociological Review,40(1),37CrossRefGoogle Scholar
Avin, C., Shpitser, I., & Pearl, J. (2005). Identifiability of path-specific effects. In van Zaanen, M., Oates, T., Paliouras, G., & de la Higuera, C.(Eds.), Proceedings of the international joint conference on artificial intelligence IJCAI-05. Edinburgh: Morgan-Kaufmann Publishers.Google Scholar
Baron, R., &Kenny, D.(1986).The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.Journal of Personality and Social Psychology,51(6),11731182.CrossRefGoogle ScholarPubMed
Breiman, L.,Friedman, J.,Olshen, R. A.,Stone, C.(1984).Classification and regression trees,Pacific GroveWadsworth.Google Scholar
Culver, J. L.,Arena, P. L.,Antoni, M. H., &Carver, C. S.(2002).Coping and distress among women under treatment for early stage breast cancer: Comparing African Americans, Hispanics and non-Hispanic Whites.Psycho-Oncology,11(6),495504.CrossRefGoogle ScholarPubMed
Fan, Y. (2012). Multiple mediation analysis with general predictive models, Dissertation. New Orleans, LA: Louisiana State University Health Sciences Center..Google Scholar
Friedman, J.(2001).Greedy function approximation: A gradient boosting machine.The Annals of Statistics,29,11891232.CrossRefGoogle Scholar
Gallicchio, L.,Calhoun, C., &Helzlsouer, K. J.(2013).Association between race and physical functioning limitations among breast cancer survivors.Supportive Care in Cancer,22(4),10811088.CrossRefGoogle ScholarPubMed
Gu, C.(2013).Smoothing spline ANOVA models,New York:Springer.CrossRefGoogle Scholar
Have, TRT.,Joffe, M. M.,Lynch, K. G.,Brown, G. K.,Maisto, S. A., &Beck, A. T.(2007).Causal mediation analyses with rank preserving models.Biometrics,63(3),926934.CrossRefGoogle ScholarPubMed
Jensen, R. E.,Moinpour, C. M.,Keegan, THM.,Cress, R. D.,Wu, X.-C.,Paddock, L.(2017).The measuring your health study: Leveraging community-based cancer registry recruitment to establish a large, diverse cohort of cancer survivors for analyses of measurement equivalence and validity of the patient reported outcomes measurement information system (promis) short form items.Psychological Test and Assessment Modeling,58(1),99117.Google Scholar
Jensen, R. E.,Moinpour, C. M.,Potosky, A. L.,Lobo, T.,Hahn, E. A.,&Hays, R. D.et.al (2016).Responsiveness of 8 patient-reported outcomes measurement information system (PROMIS) measures in a large, community-based cancer study cohort.Cancer,123(2),327335.CrossRefGoogle Scholar
Jensen, R. E.,Potosky, A. L.,Reeve, B. B.,Hahn, E.,Cella, D., &Fries, J.et.al (2015).Validation of the PROMIS physical function measures in a diverse US population-based cohort of cancer patients.Quality of Life Research,24(10),23332344.CrossRefGoogle Scholar
Judd, C. M., &Kenny, D. A.(1981).Process analysis: Estimating mediation in treatment evaluations.Evaluation Review,5(5),602619.CrossRefGoogle Scholar
MacKinnon, D. P.(2000).Equivalence of the mediation, confounding and suppression effect.Prevention Science,1(4),173181.CrossRefGoogle ScholarPubMed
Mackinnon, D. P.(2008).Introduction to statistical mediation analysis,New York:Lawrence Erlbaum Associates.Google Scholar
Mackinnon, D. P., &Dwyer, J. H.(1993).Estimating mediated effects in prevention studies.Evaluation Review,17(2),144158.CrossRefGoogle Scholar
NIH. Calibration studies testing. http://www.nihpromis.org/science/calibrationtesting. Accessed 09 Feb 2017.Google Scholar
NIH. Promis domain frameworks. http://www.nihpromis.org/measures/domainframework. Accessed 09 Feb 2017.Google Scholar
NIH. Promis methodology. http://www.nihpromis.org/science/methodology. Accessed 09 Feb 2017.Google Scholar
Patrick, D., &Erickson, P.(1993).Health policy, quality of life: Health care evaluation and resource allocation,New York, NY:Oxford University Press..Google Scholar
Pearl, J. (2001). Direct and indirect effects. In Proceedings of the seventeenth conference on uncertainty and artificial intelligence. Morgan Kaufmann.Google Scholar
Reeve, B. B.,Potosky, A. L.,Smith, A. W.,Han, P. K.,Hays, R. D.,Davis, W. W.et.al (2009).Impact of cancer on health-related quality of life of older americans.JNCI Journal of the National Cancer Institute,101(12),860868.CrossRefGoogle ScholarPubMed
Reeve, B. B.,Stover, A. M.,Jensen, R. E.,Chen, R. C.,Taylor, K. L., &Clauser, S. B.et.al (2012).Impact of diagnosis and treatment of clinically localized prostate cancer on health-related quality of life for older Americans.Cancer,118(22),56795687.CrossRefGoogle ScholarPubMed
Reeve, B. B., &Terest, J. A.(2016).Overview to the two-part series: Measurement equivalence of the patient reported outcomes measurement information system (promis) short forms.Psychological Test and Assessment Modeling,58(1),3135.Google Scholar
Reeve, B. B.,Thissen, D.,DeWalt, D. A.,Huang, I-C,Liu, Y., &Magnus, B.et.al (2015).Linkage between the PROMIS® pediatric and adult emotional distress measures.Quality of Life Research,25(4),823833.CrossRefGoogle ScholarPubMed
Robins, J. M., &Greenland, S.(1992).Identifiability and exchangeability for direct and indirect effects.Epidemiology,3(2),143155.CrossRefGoogle ScholarPubMed
Rodenberg, C. A.(2006).A review of: Assessing quality of life in clinical trials: Theory and methods, second edition, by P. Fayers and R. Hays (eds.).Journal of Biopharmaceutical Statistics,16(5),761763.CrossRefGoogle Scholar
Teresi, J. A.,Ocepek-Welikson, K.,Kleinman, M.,Ramirez, M., &Kim, G.(2016).Measurement equivalence of the patient reported outcomes measurement information system (promis) anxiety short forms in ethnically diverse groups.Psychological Test and Assessment Modeling,58(1),183219.Google ScholarPubMed
Teresi, J. A.,Ocepek-Welikson, K.,Kleinman, M.,Ramirez, M., &Kim, G.(2016).Psychometric properties and performance of the patient reported outcomes measurement information system (promis) depression short forms in ethnically diverse groups.Psychological Test and Assessment Modeling,58(1),141181.Google ScholarPubMed
VanderWeele, T. J.(2009).Marginal structural models for the estimation of direct and indirect effects.Epidemiology,20(1),1826.CrossRefGoogle ScholarPubMed
VanderWeele, T. J.,Robinson, W. R.(2014).On the causal interpretation of race in regressions adjusting for confounding and mediating variables.Epidemiology,25(4),473484.CrossRefGoogle ScholarPubMed
Vanderweele, T. J., &Vansteelandt, S.(2009).Conceptual issues concerning mediation, interventions and composition.Statistics and Its Interface,2(4),457468.CrossRefGoogle Scholar
VanderWeele, T.,Vansteelandt, S., &Daniel, R. M.(2014).Effect decomposition in the presence of an exposure-induced mediator-outcome confounder.Epidemiology,25(2),300306.CrossRefGoogle ScholarPubMed
Vansteelandt, S., &Daniel, R. M.(2017).Interventional effects for mediation analysis with multiple mediators.Epidemiology,28(2),258265.CrossRefGoogle ScholarPubMed
Yost, K.,Eton, D.,Garcia, S., &Cella, D.(2011).Minimally important differences were estimated for six promis cancer scales in advanced-stage cancer patients.Journal of Clinical Epidemiology,64(5),507516.CrossRefGoogle ScholarPubMed
Yu, Q.,Fan, Y., &Wu, X.(2014).General multiple mediation analysis with an application to explore racial disparities in breast cancer survival.Journal of Biometrics & Biostatistics,5(189),189Google Scholar
Yu, Q., &Li, B.(2017).mma: An r package for multiple mediation analysis.Journal of Open Research Software,5 11CrossRefGoogle Scholar
Yu, Q.,Li, B., &Scribner, R. A.(2009).Hierarchical additive modeling of nonlinear association with spatial correlations—An application to relate alcohol outlet density and neighborhood assault rates.Statistics in Medicine,28(14),18961912.CrossRefGoogle ScholarPubMed
Yu, Q.,Scribner, R., Leonardi, C.,Zhang, L.,Park, C., &Chen, L.et.al (2017).Exploring racial disparity in obesity: A mediation analysis considering geo-coded environmental factors.Spatial and Spatio-temporal Epidemiology,21,1323.CrossRefGoogle ScholarPubMed
Supplementary material: File

Yu et al. supplementary material

Yu et al. supplementary material 1
Download Yu et al. supplementary material(File)
File 175.9 KB
Supplementary material: File

Yu et al. supplementary material

Yu et al. supplementary material 2
Download Yu et al. supplementary material(File)
File 78.3 KB