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Zhang H, Hong X, Zheng Y, Hou L, Zheng C, Wang X, Liu L. High-dimensional quantile mediation analysis with application to a birth cohort study of mother-newborn pairs. Bioinformatics 2024; 40:btae055. [PMID: 38290773 PMCID: PMC10873903 DOI: 10.1093/bioinformatics/btae055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 02/16/2024] [Accepted: 02/16/2024] [Indexed: 02/01/2024] Open
Abstract
MOTIVATION There has been substantial recent interest in developing methodology for high-dimensional mediation analysis. Yet, the majority of mediation statistical methods lean heavily on mean regression, which limits their ability to fully capture the complex mediating effects across the outcome distribution. To bridge this gap, we propose a novel approach for selecting and testing mediators throughout the full range of the outcome distribution spectrum. RESULTS The proposed high-dimensional quantile mediation model provides a comprehensive insight into how potential mediators impact outcomes via their mediation pathways. This method's efficacy is demonstrated through extensive simulations. The study presents a real-world data application examining the mediating effects of DNA methylation on the relationship between maternal smoking and offspring birthweight. AVAILABILITY AND IMPLEMENTATION Our method offers a publicly available and user-friendly function qHIMA(), which can be accessed through the R package HIMA at https://CRAN.R-project.org/package=HIMA.
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Affiliation(s)
- Haixiang Zhang
- Center for Applied Mathematics, Tianjin University, Tianjin 300072, China
| | - Xiumei Hong
- Department of Population, Family and Reproductive Health, Center On the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States
| | - Yinan Zheng
- Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, United States
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, United States
| | - Cheng Zheng
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Xiaobin Wang
- Department of Population, Family and Reproductive Health, Center On the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO 63110, United States
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Nonequivalence of two least-absolute-deviation estimators for mediation effects. TEST-SPAIN 2022. [DOI: 10.1007/s11749-022-00837-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Devick KL, Valeri L, Chen J, Jara A, Bind MA, Coull BA. The role of body mass index at diagnosis of colorectal cancer on Black-White disparities in survival: a density regression mediation approach. Biostatistics 2022; 23:449-466. [PMID: 32968805 PMCID: PMC9016785 DOI: 10.1093/biostatistics/kxaa034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Revised: 08/07/2020] [Accepted: 08/11/2020] [Indexed: 11/14/2022] Open
Abstract
The study of racial/ethnic inequalities in health is important to reduce the uneven burden of disease. In the case of colorectal cancer (CRC), disparities in survival among non-Hispanic Whites and Blacks are well documented, and mechanisms leading to these disparities need to be studied formally. It has also been established that body mass index (BMI) is a risk factor for developing CRC, and recent literature shows BMI at diagnosis of CRC is associated with survival. Since BMI varies by racial/ethnic group, a question that arises is whether differences in BMI are partially responsible for observed racial/ethnic disparities in survival for CRC patients. This article presents new methodology to quantify the impact of the hypothetical intervention that matches the BMI distribution in the Black population to a potentially complex distributional form observed in the White population on racial/ethnic disparities in survival. Our density mediation approach can be utilized to estimate natural direct and indirect effects in the general causal mediation setting under stronger assumptions. We perform a simulation study that shows our proposed Bayesian density regression approach performs as well as or better than current methodology allowing for a shift in the mean of the distribution only, and that standard practice of categorizing BMI leads to large biases when BMI is a mediator variable. When applied to motivating data from the Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium, our approach suggests the proposed intervention is potentially beneficial for elderly and low-income Black patients, yet harmful for young or high-income Black populations.
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Affiliation(s)
- Katrina L Devick
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 13400 E. Shea Blvd., Scottsdale, AZ 85259, USA
| | - Linda Valeri
- Department of Biostatistics, Columbia Mailman School of Public Health, 722 W. 168th Street, Room 612, New York, NY 10032, USA
| | - Jarvis Chen
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Landmark Center, Room 403-N, West Wing, Boston, MA 02215, USA
| | - Alejandro Jara
- Department of Statistics, Facultad de Matemáticas, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile and Millennium Nucleus Center for the Discovery of Structures in Complex Data, Faculty of Mathematics UC, Campus San Joaquin, Vicuña Mackenna 4860, Macul, Chile
| | - Marie-Abèle Bind
- Department of Statistics, Harvard University, One Oxford Street, Suite 400, Cambridge, MA, USA
| | - Brent A Coull
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building 2, 4th Floor, Boston, MA 02115, USA
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Zeng P, Shao Z, Zhou X. Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges. Comput Struct Biotechnol J 2021; 19:3209-3224. [PMID: 34141140 PMCID: PMC8187160 DOI: 10.1016/j.csbj.2021.05.042] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/21/2021] [Accepted: 05/21/2021] [Indexed: 12/12/2022] Open
Abstract
Mediation analysis investigates the intermediate mechanism through which an exposure exerts its influence on the outcome of interest. Mediation analysis is becoming increasingly popular in high-throughput genomics studies where a common goal is to identify molecular-level traits, such as gene expression or methylation, which actively mediate the genetic or environmental effects on the outcome. Mediation analysis in genomics studies is particularly challenging, however, thanks to the large number of potential mediators measured in these studies as well as the composite null nature of the mediation effect hypothesis. Indeed, while the standard univariate and multivariate mediation methods have been well-established for analyzing one or multiple mediators, they are not well-suited for genomics studies with a large number of mediators and often yield conservative p-values and limited power. Consequently, over the past few years many new high-dimensional mediation methods have been developed for analyzing the large number of potential mediators collected in high-throughput genomics studies. In this work, we present a thorough review of these important recent methodological advances in high-dimensional mediation analysis. Specifically, we describe in detail more than ten high-dimensional mediation methods, focusing on their motivations, basic modeling ideas, specific modeling assumptions, practical successes, methodological limitations, as well as future directions. We hope our review will serve as a useful guidance for statisticians and computational biologists who develop methods of high-dimensional mediation analysis as well as for analysts who apply mediation methods to high-throughput genomics studies.
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Affiliation(s)
- Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
- Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Zhonghe Shao
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor 48109, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor 48109, MI, USA
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5
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Advancing Substantive Knowledge by Asking New Questions, Best Done in the Light of Answers to Older Questions. Epidemiology 2019; 30:633-636. [DOI: 10.1097/ede.0000000000001037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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6
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Han LKM, Verhoeven JE, Tyrka AR, Penninx BWJH, Wolkowitz OM, Månsson KNT, Lindqvist D, Boks MP, Révész D, Mellon SH, Picard M. Accelerating research on biological aging and mental health: Current challenges and future directions. Psychoneuroendocrinology 2019; 106:293-311. [PMID: 31154264 PMCID: PMC6589133 DOI: 10.1016/j.psyneuen.2019.04.004] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/22/2019] [Accepted: 04/02/2019] [Indexed: 12/13/2022]
Abstract
Aging is associated with complex biological changes that can be accelerated, slowed, or even temporarily reversed by biological and non-biological factors. This article focuses on the link between biological aging, psychological stressors, and mental illness. Rather than comprehensively reviewing this rapidly expanding field, we highlight challenges in this area of research and propose potential strategies to accelerate progress in this field. This effort requires the interaction of scientists across disciplines - including biology, psychiatry, psychology, and epidemiology; and across levels of analysis that emphasize different outcome measures - functional capacity, physiological, cellular, and molecular. Dialogues across disciplines and levels of analysis naturally lead to new opportunities for discovery but also to stimulating challenges. Some important challenges consist of 1) establishing the best objective and predictive biological age indicators or combinations of indicators, 2) identifying the basis for inter-individual differences in the rate of biological aging, and 3) examining to what extent interventions can delay, halt or temporarily reverse aging trajectories. Discovering how psychological states influence biological aging, and vice versa, has the potential to create novel and exciting opportunities for healthcare and possibly yield insights into the fundamental mechanisms that drive human aging.
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Affiliation(s)
- Laura K M Han
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Public Health Research Institute, Oldenaller 1, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Josine E Verhoeven
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Public Health Research Institute, Oldenaller 1, the Netherlands
| | - Audrey R Tyrka
- Butler Hospital and the Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Brenda W J H Penninx
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Public Health Research Institute, Oldenaller 1, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Owen M Wolkowitz
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, School of Medicine, San Francisco, CA, USA
| | - Kristoffer N T Månsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Psychology, Stockholm University, Stockholm, Sweden; Department of Psychology, Uppsala University, Uppsala, Sweden
| | - Daniel Lindqvist
- Faculty of Medicine, Department of Clinical Sciences, Psychiatry, Lund University, Lund, Sweden; Department of Psychiatry, University of California San Francisco (UCSF) School of Medicine, San Francisco, CA, USA; Psychiatric Clinic, Lund, Division of Psychiatry, Lund, Sweden
| | - Marco P Boks
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, the Netherlands
| | - Dóra Révész
- Center of Research on Psychology in Somatic diseases (CoRPS), Department of Medical and Clinical Psychology, Tilburg University, Tilburg, the Netherlands
| | - Synthia H Mellon
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, School of Medicine, San Francisco, CA, USA
| | - Martin Picard
- Department of Psychiatry, Division of Behavioral Medicine, Columbia University Medical Center, New York, NY, USA; Department of Neurology, H. Houston Merritt Center, Columbia Translational Neuroscience Initiative, Columbia University Medical Center, New York, NY, USA; Columbia Aging Center, Columbia University, New York, NY, USA.
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Abstract
The field of environmental health has been dominated by modeling associations, especially by regressing an observed outcome on a linear or nonlinear function of observed covariates. Readers interested in advances in policies for improving environmental health are, however, expecting to be informed about health effects resulting from, or more explicitly caused by, environmental exposures. The quantification of health impacts resulting from the removal of environmental exposures involves causal statements. Therefore, when possible, causal inference frameworks should be considered for analyzing the effects of environmental exposures on health outcomes.
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Affiliation(s)
- Marie-Abèle Bind
- Department of Statistics, Faculty of Arts and Sciences, Harvard University, Cambridge, Massachusetts 02138, USA;
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Liu L, Zheng C, Kang J. Exploring causality mechanism in the joint analysis of longitudinal and survival data. Stat Med 2018; 37:3733-3744. [PMID: 29882359 DOI: 10.1002/sim.7838] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 04/28/2018] [Accepted: 05/08/2018] [Indexed: 11/07/2022]
Abstract
In many biomedical studies, disease progress is monitored by a biomarker over time, eg, repeated measures of CD4 in AIDS and hemoglobin in end-stage renal disease patients. The endpoint of interest, eg, death or diagnosis of a specific disease, is correlated with the longitudinal biomarker. In this paper, we examine and compare different models of longitudinal and survival data to investigate causal mechanisms, specifically, those related to the role of random effects. We illustrate the methods by data from two clinical trials: an AIDS study and a liver cirrhosis study.
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Affiliation(s)
- Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Cheng Zheng
- Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Joseph Kang
- Centers for Disease Control and Prevention, Atlanta, GA, USA
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