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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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2
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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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Hejazi NS, Boileau P, van der Laan MJ, Hubbard AE. A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology. Stat Methods Med Res 2023; 32:539-554. [PMID: 36573044 PMCID: PMC11078029 DOI: 10.1177/09622802221146313] [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] [Indexed: 12/28/2022]
Abstract
The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational and high-dimensional biology. As the dimensionality of datasets continues to grow, so too does the complexity of identifying biomarkers linked to exposure patterns. The statistical analysis of such data often relies upon parametric modeling assumptions motivated by convenience, inviting opportunities for model misspecification. While estimation frameworks incorporating flexible, data adaptive regression strategies can mitigate this, their standard variance estimators are often unstable in high-dimensional settings, resulting in inflated Type-I error even after standard multiple testing corrections. We adapt a shrinkage approach compatible with parametric modeling strategies to semiparametric variance estimators of a family of efficient, asymptotically linear estimators of causal effects, defined by counterfactual exposure contrasts. Augmenting the inferential stability of these estimators in high-dimensional settings yields a data adaptive approach for robustly uncovering stable causal associations, even when sample sizes are limited. Our generalized variance estimator is evaluated against appropriate alternatives in numerical experiments, and an open source R/Bioconductor package, biotmle, is introduced. The proposal is demonstrated in an analysis of high-dimensional DNA methylation data from an observational study on the epigenetic effects of tobacco smoking.
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Affiliation(s)
- Nima S Hejazi
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Philippe Boileau
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Mark J van der Laan
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, CA, USA
- Department of Statistics, University of California, Berkeley, CA, USA
| | - Alan E Hubbard
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, CA, USA
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Hejazi NS, van der Laan MJ, Janes HE, Gilbert PB, Benkeser DC. Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials. Biometrics 2021; 77:1241-1253. [PMID: 32949147 PMCID: PMC8016405 DOI: 10.1111/biom.13375] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/02/2020] [Accepted: 09/01/2020] [Indexed: 12/17/2022]
Abstract
The advent and subsequent widespread availability of preventive vaccines has altered the course of public health over the past century. Despite this success, effective vaccines to prevent many high-burden diseases, including human immunodeficiency virus (HIV), have been slow to develop. Vaccine development can be aided by the identification of immune response markers that serve as effective surrogates for clinically significant infection or disease endpoints. However, measuring immune response marker activity is often costly, which has motivated the usage of two-phase sampling for immune response evaluation in clinical trials of preventive vaccines. In such trials, the measurement of immunological markers is performed on a subset of trial participants, where enrollment in this second phase is potentially contingent on the observed study outcome and other participant-level information. We propose nonparametric methodology for efficiently estimating a counterfactual parameter that quantifies the impact of a given immune response marker on the subsequent probability of infection. Along the way, we fill in theoretical gaps pertaining to the asymptotic behavior of nonparametric efficient estimators in the context of two-phase sampling, including a multiple robustness property enjoyed by our estimators. Techniques for constructing confidence intervals and hypothesis tests are presented, and an open source software implementation of the methodology, the txshift R package, is introduced. We illustrate the proposed techniques using data from a recent preventive HIV vaccine efficacy trial.
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Affiliation(s)
- Nima S Hejazi
- Graduate Group in Biostatistics, University of California, Berkeley, California
- Center for Computational Biology, University of California, Berkeley, California
| | - Mark J van der Laan
- Division of Epidemiology & Biostatistics, School of Public Health, University of California, Berkeley, California
- Department of Statistics, University of California, Berkeley, California
| | - Holly E Janes
- Vaccine & Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - Peter B Gilbert
- Vaccine & Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - David C Benkeser
- Department of Biostatistics & Computational Biology, Rollins School of Public Health, Emory University, Atlanta, Georgia
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Fang EX, Wang Z, Wang L. Fairness-Oriented Learning for Optimal Individualized Treatment Rules. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.2008402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Ethan X. Fang
- Department of Statistics, Pennsylvania State University, University Park, PA 16802
| | - Zhaoran Wang
- Department of Industrial al Engineering and Management Science, Northwestern University, Evanston, IL 60208
| | - Lan Wang
- Department of Management Science, Miami Herbert Business School, University of Miami, Coral Gables, FL 33146
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Wang J, Ning J, Shete S. Mediation model with a categorical exposure and a censored mediator with application to a genetic study. PLoS One 2021; 16:e0257628. [PMID: 34637449 PMCID: PMC8509986 DOI: 10.1371/journal.pone.0257628] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 09/06/2021] [Indexed: 12/12/2022] Open
Abstract
Mediation analysis is a statistical method for evaluating the direct and indirect effects of an exposure on an outcome in the presence of a mediator. Mediation models have been widely used to determine direct and indirect contributions of genetic variants in clinical phenotypes. In genetic studies, the additive genetic model is the most commonly used model because it can detect effects from either recessive or dominant models (or any model in between). However, the existing approaches for mediation model cannot be directly applied when the genetic model is additive (e.g. the most commonly used model for SNPs) or categorical (e.g. polymorphic loci), and thus modification to measures of indirect and direct effects is warranted. In this study, we proposed overall measures of indirect, direct, and total effects for a mediation model with a categorical exposure and a censored mediator, which accounts for the frequency of different values of the categorical exposure. The proposed approach provides the overall contribution of the categorical exposure to the outcome variable. We assessed the empirical performance of the proposed overall measures via simulation studies and applied the measures to evaluate the mediating effect of a women’s age at menopause on the association between genetic variants and type 2 diabetes.
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Affiliation(s)
- Jian Wang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Jing Ning
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Sanjay Shete
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- * E-mail:
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Díaz I, Williams N, Hoffman KL, Schenck EJ. Nonparametric Causal Effects Based on Longitudinal Modified Treatment Policies. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1955691] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Iván Díaz
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York
| | - Nicholas Williams
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York
| | - Katherine L. Hoffman
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York
| | - Edward J. Schenck
- Division of Pulmonary & Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York
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Moreno-Betancur M, Moran P, Becker D, Patton GC, Carlin JB. Mediation effects that emulate a target randomised trial: Simulation-based evaluation of ill-defined interventions on multiple mediators. Stat Methods Med Res 2021; 30:1395-1412. [PMID: 33749386 PMCID: PMC8371283 DOI: 10.1177/0962280221998409] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Many epidemiological questions concern potential interventions to alter the pathways presumed to mediate an association. For example, we consider a study that investigates the benefit of interventions in young adulthood for ameliorating the poorer mid-life psychosocial outcomes of adolescent self-harmers relative to their healthy peers. Two methodological challenges arise. First, mediation methods have hitherto mostly focused on the elusive task of discovering pathways, rather than on the evaluation of mediator interventions. Second, the complexity of such questions is invariably such that there are no well-defined mediator interventions (i.e. actual treatments, programs, etc.) for which data exist on the relevant populations, outcomes and time-spans of interest. Instead, researchers must rely on exposure (non-intervention) data, that is, on mediator measures such as depression symptoms for which the actual interventions that one might implement to alter them are not well defined. We propose a novel framework that addresses these challenges by defining mediation effects that map to a target trial of hypothetical interventions targeting multiple mediators for which we simulate the effects. Specifically, we specify a target trial addressing three policy-relevant questions, regarding the impacts of hypothetical interventions that would shift the mediators' distributions (separately under various interdependence assumptions, jointly or sequentially) to user-specified distributions that can be emulated with the observed data. We then define novel interventional effects that map to this trial, simulating shifts by setting mediators to random draws from those distributions. We show that estimation using a g-computation method is possible under an expanded set of causal assumptions relative to inference with well-defined interventions, which reflects the lower level of evidence that is expected with ill-defined interventions. Application to the self-harm example in the Victorian Adolescent Health Cohort Study illustrates the value of our proposal for informing the design and evaluation of actual interventions in the future.
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Affiliation(s)
- Margarita Moreno-Betancur
- Department of Paediatrics, University of Melbourne, Melbourne, Australia.,Murdoch Children's Research Institute, Melbourne, Australia
| | - Paul Moran
- Centre for Academic Mental Health, School of Social & Community Medicine, University of Bristol, Bristol, UK
| | - Denise Becker
- Murdoch Children's Research Institute, Melbourne, Australia
| | - George C Patton
- Department of Paediatrics, University of Melbourne, Melbourne, Australia.,Murdoch Children's Research Institute, Melbourne, Australia
| | - John B Carlin
- Department of Paediatrics, University of Melbourne, Melbourne, Australia.,Murdoch Children's Research Institute, Melbourne, Australia
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