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Rudolph KE, Williams NT, Diaz I. Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders. Biostatistics 2024:kxae012. [PMID: 38576206 DOI: 10.1093/biostatistics/kxae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/18/2024] [Accepted: 03/17/2024] [Indexed: 04/06/2024] Open
Abstract
Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including (i) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the mediator-outcome relationship), that may also be (ii) multivariate, and (iii) the existence of multivariate mediators. All three challenges are present in the mediation analysis we consider here, where our goal is to estimate the indirect effects of receiving a Section 8 housing voucher as a young child on the risk of developing a psychiatric mood disorder in adolescence that operate through mediators related to neighborhood poverty, the school environment, and instability of the neighborhood and school environments, considered together and separately. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator-outcome relationship, but currently, no readily implementable nonparametric estimator for IDE/IIE exists that allows for both multivariate mediators and multivariate post-exposure intermediate confounders. The absence of such an IDE/IIE estimator that can easily accommodate both multivariate mediators and post-exposure confounders represents a significant limitation for real-world analyses, because when considering each mediator subgroup separately, the remaining mediator subgroups (or a subset of them) become post-exposure intermediate confounders. We address this gap by extending a recently developed nonparametric estimator for the IDE/IIE to allow for easy incorporation of multivariate mediators and multivariate post-exposure confounders simultaneously. We apply the proposed estimation approach to our analysis, including walking through a strategy to account for other, possibly co-occurring intermediate variables when considering each mediator subgroup separately.
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Affiliation(s)
- Kara E Rudolph
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St, NY, NY 10032, United States
| | - Nicholas T Williams
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W 168th St, NY, NY 10032, United States
| | - Ivan Diaz
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, 180 Madison Ave, NY, NY 10016, United States
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Diemer EW. The importance of translating genetic partitioning into causal language. Int J Epidemiol 2024; 53:dyae036. [PMID: 38441195 DOI: 10.1093/ije/dyae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/20/2024] [Indexed: 03/07/2024] Open
Affiliation(s)
- Elizabeth W Diemer
- CAUSALab, Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
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Díaz I, Hoffman KL, Hejazi NS. Causal survival analysis under competing risks using longitudinal modified treatment policies. LIFETIME DATA ANALYSIS 2024; 30:213-236. [PMID: 37620504 DOI: 10.1007/s10985-023-09606-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 07/17/2023] [Indexed: 08/26/2023]
Abstract
Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, ordinal, or continuous treatments measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to a competing event that precludes observation of the event of interest. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as [Formula: see text]-consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event.
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Affiliation(s)
- Iván Díaz
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, 10016, USA.
| | - Katherine L Hoffman
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Nima S Hejazi
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA
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Rudolph KE, Williams N, Díaz I. Efficient and flexible estimation of natural direct and indirect effects under intermediate confounding and monotonicity constraints. Biometrics 2023; 79:3126-3139. [PMID: 36905172 PMCID: PMC11037503 DOI: 10.1111/biom.13850] [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: 05/12/2022] [Accepted: 02/16/2023] [Indexed: 03/12/2023]
Abstract
Natural direct and indirect effects are mediational estimands that decompose the average treatment effect and describe how outcomes would be affected by contrasting levels of a treatment through changes induced in mediator values (in the case of the indirect effect) or not through induced changes in the mediator values (in the case of the direct effect). Natural direct and indirect effects are not generally point-identified in the presence of a treatment-induced confounder; however, they may be identified if one is willing to assume monotonicity between the treatment and the treatment-induced confounder. We argue that this assumption may be reasonable in the relatively common encouragement-design trial setting, where the intervention is randomized treatment assignment and the treatment-induced confounder is whether or not treatment was actually taken/adhered to. We develop efficiency theory for the natural direct and indirect effects under this monotonicity assumption, and use it to propose a nonparametric, multiply robust estimator. We demonstrate the finite sample properties of this estimator using a simulation study, and apply it to data from the Moving to Opportunity Study to estimate the natural direct and indirect effects of being randomly assigned to receive a Section 8 housing voucher-the most common form of federal housing assistance-on risk developing any mood or externalizing disorder among adolescent boys, possibly operating through various school and community characteristics.
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Affiliation(s)
- Kara E Rudolph
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Nicholas Williams
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Iván Díaz
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA
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Pamplin II JR, Rudolph KE, Keyes KM, Susser ES, Bates LM. Investigating a Paradox: Toward a Better Understanding of the Relationships Between Racial Group Membership, Stress, and Major Depressive Disorder. Am J Epidemiol 2023; 192:1845-1853. [PMID: 37230957 PMCID: PMC11043785 DOI: 10.1093/aje/kwad128] [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: 08/04/2022] [Revised: 04/05/2023] [Accepted: 05/19/2023] [Indexed: 05/27/2023] Open
Abstract
Epidemiologic studies in the United States routinely report a lower or equal prevalence of major depressive disorder (MDD) for Black people relative to White people. Within racial groups, individuals with greater life stressor exposure experience greater prevalence of MDD; however, between racial groups this pattern does not hold. Informed by theoretical and empirical literature seeking to explain this "Black-White depression paradox," we outline 2 proposed models for the relationships between racial group membership, life stressor exposure, and MDD: an effect modification model and an inconsistent mediator model. Either model could explain the paradoxical within- and between-racial group patterns of life stressor exposure and MDD. We empirically estimated associations under each of the proposed models using data from 26,960 self-identified Black and White participants in the National Epidemiologic Survey on Alcohol and Related Conditions III (United States, 2012-2013). Under the effect modification model, we estimated relative risk effect modification using parametric regression with a cross-product term, and under the inconsistent mediation model, we estimated interventional direct and indirect effects using targeted minimum loss-based estimation. We found evidence of inconsistent mediation (i.e., direct and indirect effects operating in opposite directions), suggesting a need for greater consideration of explanations for racial patterns in MDD that operate independent of life stressor exposure. This article is part of a Special Collection on Mental Health.
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Affiliation(s)
- John R Pamplin II
- Correspondence to Dr. John Pamplin, Department of Epidemiology, Columbia University Mailman School of Public Health, 722 W. 168th Street #520, New York, NY 10032 (e-mail: )
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Hejazi NS, Rudolph KE, Van Der Laan MJ, Díaz I. Nonparametric causal mediation analysis for stochastic interventional (in)direct effects. Biostatistics 2023; 24:686-707. [PMID: 35102366 PMCID: PMC10345989 DOI: 10.1093/biostatistics/kxac002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 01/07/2022] [Accepted: 01/07/2022] [Indexed: 07/20/2023] Open
Abstract
Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary exposures and static interventions and (ii) direct and indirect effect decompositions have been pursued that are only identifiable in the absence of intermediate confounders affected by exposure. We present a theoretical study of an (in)direct effect decomposition of the population intervention effect, defined by stochastic interventions jointly applied to the exposure and mediators. In contrast to existing proposals, our causal effects can be evaluated regardless of whether an exposure is categorical or continuous and remain well-defined even in the presence of intermediate confounders affected by exposure. Our (in)direct effects are identifiable without a restrictive assumption on cross-world counterfactual independencies, allowing for substantive conclusions drawn from them to be validated in randomized controlled trials. Beyond the novel effects introduced, we provide a careful study of nonparametric efficiency theory relevant for the construction of flexible, multiply robust estimators of our (in)direct effects, while avoiding undue restrictions induced by assuming parametric models of nuisance parameter functionals. To complement our nonparametric estimation strategy, we introduce inferential techniques for constructing confidence intervals and hypothesis tests, and discuss open-source software, the $\texttt{medshift}$$\texttt{R}$ package, implementing the proposed methodology. Application of our (in)direct effects and their nonparametric estimators is illustrated using data from a comparative effectiveness trial examining the direct and indirect effects of pharmacological therapeutics on relapse to opioid use disorder.
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Affiliation(s)
| | - Kara E Rudolph
- Department of Epidemiology, Mailman School of Public Health, Columbia University, 722 W. 168th Street, New York, NY 10032, USA
| | - Mark J Van Der Laan
- Division of Biostatistics, School of Public Health, and Department of Statistics, University of California, Berkeley, 2121 Berkeley Way, Berkeley, CA 94720, USA
| | - Iván Díaz
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, 402 E. 67th Street, New York, NY 10065, USA
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Byeon S, Lee W. An Introduction to Causal Mediation Analysis With a Comparison of 2 R Packages. J Prev Med Public Health 2023; 56:303-311. [PMID: 37551068 PMCID: PMC10415648 DOI: 10.3961/jpmph.23.189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/22/2023] [Indexed: 08/09/2023] Open
Abstract
Traditional mediation analysis, which relies on linear regression models, has faced criticism due to its limited suitability for cases involving different types of variables and complex covariates, such as interactions. This can result in unclear definitions of direct and indirect effects. As an alternative, causal mediation analysis using the counterfactual framework has been introduced to provide clearer definitions of direct and indirect effects while allowing for more flexible modeling methods. However, the conceptual understanding of this approach based on the counterfactual framework remains challenging for applied researchers. To address this issue, the present article was written to highlight and illustrate the definitions of causal estimands, including controlled direct effect, natural direct effect, and natural indirect effect, based on the key concept of nested counterfactuals. Furthermore, we recommend using 2 R packages, 'medflex' and 'mediation', to perform causal mediation analysis and provide public health examples. The article also offers caveats and guidelines for accurate interpretation of the results.
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Affiliation(s)
- Sangmin Byeon
- Institute of Health & Environment, Seoul National University, Seoul,
Korea
| | - Woojoo Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul,
Korea
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Yan Y, Ren M, de Leon A. Measurement error correction in mediation analysis under the additive hazards model. COMMUN STAT-SIMUL C 2023. [DOI: 10.1080/03610918.2023.2170412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Ying Yan
- School of Mathematics, Sun Yat-sen University, Guangzhou, China
| | - Mingchen Ren
- Department of Mathematics and Statistics, University of Calgary, Calgary, Canada
| | - Alexander de Leon
- Department of Mathematics and Statistics, University of Calgary, Calgary, Canada
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Rudolph KE, Díaz I. When the Ends do not Justify the Means: Learning Who is Predicted to Have Harmful Indirect Effects. JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A, (STATISTICS IN SOCIETY) 2022; 185:S573-S589. [PMID: 37397280 PMCID: PMC10312488 DOI: 10.1111/rssa.12951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
There is a growing literature on finding rules by which to assign treatment based on an individual's characteristics such that a desired outcome under the intervention is maximized. A related goal entails identifying a subpopulation of individuals predicted to have a harmful indirect effect (the effect of treatment on an outcome through mediators), perhaps even in the presence of a predicted beneficial total treatment effect. In some cases, the implications of a likely harmful indirect effect may outweigh an anticipated beneficial total treatment effect, and would motivate further discussion of whether to treat identified individuals. We build on the mediation and optimal treatment rule literatures to propose a method of identifying a subgroup for which the treatment effect through the mediator is expected to be harmful. Our approach is nonparametric, incorporates post-treatment confounders of the mediator-outcome relationship, and does not make restrictions on the distribution of baseline covariates, mediating variables, or outcomes. We apply the proposed approach to identify a subgroup of boys in the MTO housing voucher experiment who are predicted to have a harmful indirect effect of housing voucher receipt on subsequent psychiatric disorder incidence through aspects of their school and neighborhood environments.
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Affiliation(s)
- Kara E Rudolph
- Department of Epidemiology, Mailman School of Public Health, Columbia University
| | - Iván Díaz
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine
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Understanding Etiologic Pathways Through Multiple Sequential Mediators: An Application in Perinatal Epidemiology. Epidemiology 2022; 33:854-863. [PMID: 35816125 DOI: 10.1097/ede.0000000000001518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND Causal mediation analysis facilitates decomposing the total effect into a direct effect and an indirect effect that operates through an intermediate variable. Recent developments in causal mediation analysis have clarified the process of evaluating how-and to what extent-different pathways via multiple causally ordered mediators link the exposure to the outcome. METHODS Through an application of natural effect models for multiple mediators, we show how placental abruption might affect perinatal mortality using small for gestational age (SGA) birth and preterm delivery as two sequential mediators. We describe methods to disentangle the total effect into the proportions mediated via each of the sequential mediators, when evaluating natural direct and natural indirect effects. RESULTS Under the assumption that SGA births causally precedes preterm delivery, an analysis of 16.7 million singleton pregnancies is consistent with the hypothesis that abruption exerts powerful effects on perinatal mortality (adjusted risk ratio = 11.9; 95% confidence interval = 11.6, 12.1). The proportions of the estimated total effect mediated through SGA birth and preterm delivery were 2% and 58%, respectively. The proportion unmediated via either SGA or preterm delivery was 41%. CONCLUSIONS Through an application of causal mediation analysis with sequential mediators, we uncovered new insights into the pathways along which abruption impacts perinatal mortality.
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Xia F, Chan KCG. Identification, Semiparametric Efficiency, and Quadruply Robust Estimation in Mediation Analysis with Treatment-Induced Confounding. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1990765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Fan Xia
- Department of Epidemiology, University of Washington
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12
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Rudolph KE, Díaz I. Efficiently transporting causal direct and indirect effects to new populations under intermediate confounding and with multiple mediators. Biostatistics 2021; 23:789-806. [PMID: 33528006 DOI: 10.1093/biostatistics/kxaa057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 11/16/2020] [Accepted: 11/28/2020] [Indexed: 11/12/2022] Open
Abstract
The same intervention can produce different effects in different sites. Existing transport mediation estimators can estimate the extent to which such differences can be explained by differences in compositional factors and the mechanisms by which mediating or intermediate variables are produced; however, they are limited to consider a single, binary mediator. We propose novel nonparametric estimators of transported interventional (in)direct effects that consider multiple, high-dimensional mediators and a single, binary intermediate variable. They are multiply robust, efficient, asymptotically normal, and can incorporate data-adaptive estimation of nuisance parameters. They can be applied to understand differences in treatment effects across sites and/or to predict treatment effects in a target site based on outcome data in source sites.
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Affiliation(s)
- Kara E Rudolph
- Department of Epidemiology, Mailman School of Public Health, Columbia University; and Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Iván Díaz
- Department of Epidemiology, Mailman School of Public Health, Columbia University; and Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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Loh WW, Moerkerke B, Loeys T, Vansteelandt S. Nonlinear mediation analysis with high-dimensional mediators whose causal structure is unknown. Biometrics 2020; 78:46-59. [PMID: 33215694 DOI: 10.1111/biom.13402] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 10/28/2020] [Accepted: 11/11/2020] [Indexed: 11/28/2022]
Abstract
With multiple possible mediators on the causal pathway from a treatment to an outcome, we consider the problem of decomposing the effects along multiple possible causal path(s) through each distinct mediator. Under a path-specific effects framework, such fine-grained decompositions necessitate stringent assumptions, such as correctly specifying the causal structure among the mediators, and no unobserved confounding among the mediators. In contrast, interventional direct and indirect effects for multiple mediators can be identified under much weaker conditions, while providing scientifically relevant causal interpretations. Nonetheless, current estimation approaches require (correctly) specifying a model for the joint mediator distribution, which can be difficult when there is a high-dimensional set of possibly continuous and noncontinuous mediators. In this article, we avoid the need to model this distribution, by developing a definition of interventional effects previously suggested for longitudinal mediation. We propose a novel estimation strategy that uses nonparametric estimates of the (counterfactual) mediator distributions. Noncontinuous outcomes can be accommodated using nonlinear outcome models. Estimation proceeds via Monte Carlo integration. The procedure is illustrated using publicly available genomic data to assess the causal effect of a microRNA expression on the 3-month mortality of brain cancer patients that is potentially mediated by expression values of multiple genes.
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Affiliation(s)
- Wen Wei Loh
- Department of Data Analysis, Ghent University, Gent, Belgium
| | | | - Tom Loeys
- Department of Data Analysis, Ghent University, Gent, Belgium
| | - Stijn Vansteelandt
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.,Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
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