Tyler J Vanderweele

University of North Carolina at Chapel Hill, North Carolina, United States

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Publications (155)679.23 Total impact

  • Linda Valeri, Xihong Lin, Tyler J VanderWeele
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    ABSTRACT: Mediation analysis is a popular approach to examine the extent to which the effect of an exposure on an outcome is through an intermediate variable (mediator) and the extent to which the effect is direct. When the mediator is mis-measured, the validity of mediation analysis can be severely undermined. In this paper, we first study the bias of classical, non-differential measurement error on a continuous mediator in the estimation of direct and indirect causal effects in generalized linear models when the outcome is either continuous or discrete and exposure-mediator interaction may be present. Our theoretical results as well as a numerical study demonstrate that in the presence of non-linearities, the bias of naive estimators for direct and indirect effects that ignore measurement error can take unintuitive directions. We then develop methods to correct for measurement error. Three correction approaches using method of moments, regression calibration, and SIMEX are compared. We apply the proposed method to the Massachusetts General Hospital lung cancer study to evaluate the effect of genetic variants mediated through smoking on lung cancer risk. Copyright © 2014 John Wiley & Sons, Ltd.
    Statistics in Medicine 09/2014; · 2.04 Impact Factor
  • Tyler J VanderWeele, Eric J Tchetgen Tchetgen
    Epidemiology (Cambridge, Mass.) 09/2014; 25(5):727-8. · 5.51 Impact Factor
  • Tyler J VanderWeele, Eric J Tchetgen Tchetgen
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    ABSTRACT: A framework is presented that allows an investigator to estimate the portion of the effect of one exposure that is attributable to an interaction with a second exposure. We show that when the 2 exposures are statistically independent in distribution, the total effect of one exposure can be decomposed into a conditional effect of that exposure when the second is absent and also a component due to interaction. The decomposition applies on difference or ratio scales. We discuss how the components can be estimated using standard regression models, and how these components can be used to evaluate the proportion of the total effect of the primary exposure attributable to the interaction with the second exposure. In the setting in which one of the exposures affects the other, so that the 2 are no longer statistically independent in distribution, alternative decompositions are discussed. The various decompositions are illustrated with an example in genetic epidemiology. If it is not possible to intervene on the primary exposure of interest, the methods described in this article can help investigators to identify other variables that, if intervened upon, would eliminate the largest proportion of the effect of the primary exposure.
    Epidemiology (Cambridge, Mass.) 07/2014; · 5.51 Impact Factor
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    ABSTRACT: Spiritual care (SC) is important to the care of seriously ill patients. Few studies have examined types of SC provided and their perceived impact. This study surveyed patients with advanced cancer (N = 75, response rate [RR] = 73%) and oncology nurses and physicians (N = 339, RR = 63%). Frequency and perceived impact of 8 SC types were assessed. Spiritual care is infrequently provided, with encouraging or affirming beliefs the most common type (20%). Spiritual history taking and chaplaincy referrals comprised 10% and 16%, respectively. Most patients viewed each SC type positively, and SC training predicted provision of many SC types. In conclusion, SC is infrequent, and core elements of SC-spiritual history taking and chaplaincy referrals-represent a minority of SC. Spiritual care training predicts provision of SC, indicting its importance to advancing SC in the clinical setting.
    The American journal of hospice & palliative care 07/2014;
  • Tyler J VanderWeele
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    ABSTRACT: The overall effect of an exposure on an outcome, in the presence of a mediator with which the exposure may interact, can be decomposed into 4 components: (1) the effect of the exposure in the absence of the mediator, (2) the interactive effect when the mediator is left to what it would be in the absence of exposure, (3) a mediated interaction, and (4) a pure mediated effect. These 4 components, respectively, correspond to the portion of the effect that is due to neither mediation nor interaction, to just interaction (but not mediation), to both mediation and interaction, and to just mediation (but not interaction). This 4-way decomposition unites methods that attribute effects to interactions and methods that assess mediation. Certain combinations of these 4 components correspond to measures for mediation, whereas other combinations correspond to measures of interaction previously proposed in the literature. Prior decompositions in the literature are in essence special cases of this 4-way decomposition. The 4-way decomposition can be carried out using standard statistical models, and software is provided to estimate each of the 4 components. The 4-way decomposition provides maximum insight into how much of an effect is mediated, how much is due to interaction, how much is due to both mediation and interaction together, and how much is due to neither.
    Epidemiology (Cambridge, Mass.) 07/2014; · 5.51 Impact Factor
  • Tyler J VanderWeele, Whitney R Robinson
    Epidemiology (Cambridge, Mass.) 07/2014; 25(4):491-493. · 5.51 Impact Factor
  • Tyler J VanderWeele, Whitney R Robinson
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    ABSTRACT: We consider several possible interpretations of the "effect of race" when regressions are run with race as an exposure variable, controlling also for various confounding and mediating variables. When adjustment is made for socioeconomic status early in a person's life, we discuss under what contexts the regression coefficients for race can be interpreted as corresponding to the extent to which a racial inequality would remain if various socioeconomic distributions early in life across racial groups could be equalized. When adjustment is also made for adult socioeconomic status, we note how the overall racial inequality can be decomposed into the portion that would be eliminated by equalizing adult socioeconomic status across racial groups and the portion of the inequality that would remain even if adult socioeconomic status across racial groups were equalized. We also discuss a stronger interpretation of the effect of race (stronger in terms of assumptions) involving the joint effects of race-associated physical phenotype (eg, skin color), parental physical phenotype, genetic background, and cultural context when such variables are thought to be hypothetically manipulable and if adequate control for confounding were possible. We discuss some of the challenges with such an interpretation. Further discussion is given as to how the use of selected populations in examining racial disparities can additionally complicate the interpretation of the effects.
    Epidemiology (Cambridge, Mass.) 07/2014; 25(4):473-484. · 5.51 Impact Factor
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    ABSTRACT: We give critical attention to the assumptions underlying Mendelian randomization analysis and their biological plausibility. Several scenarios violating the Mendelian randomization assumptions are described, including settings with inadequate phenotype definition, the setting of time-varying exposures, the presence of gene-environment interaction, the existence of measurement error, the possibility of reverse causation, and the presence of linkage disequilibrium. Data analysis examples are given, illustrating that the inappropriate use of instrumental variable techniques when the Mendelian randomization assumptions are violated can lead to biases of enormous magnitude. To help address some of the strong assumptions being made, three possible approaches are suggested. First, the original proposal of Katan (Lancet. 1986;1:00-00) for Mendelian randomization was not to use instrumental variable techniques to obtain estimates but merely to examine genotype-outcome associations to test for the presence of an effect of the exposure on the outcome. We show that this more modest goal and approach can circumvent many, though not all, the potential biases described. Second, we discuss the use of sensitivity analysis in evaluating the consequences of violations in the assumptions and in attempting to correct for those violations. Third, we suggest that a focus on negative, rather than positive, Mendelian randomization results may turn out to be more reliable.
    Epidemiology (Cambridge, Mass.) 03/2014; · 5.51 Impact Factor
  • Linda Valeri, Tyler J Vanderweele
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    ABSTRACT: Mediation analysis serves to quantify the effect of an exposure on an outcome mediated by a certain intermediate and to quantify the extent to which the effect is direct. When the mediator is misclassified, the validity of mediation analysis can be severely undermined. The contribution of the present work is to study the effects of non-differential misclassification of a binary mediator in the estimation of direct and indirect causal effects when the outcome is either continuous or binary and exposure-mediator interaction can be present, and to allow the correction of misclassification. A hybrid of likelihood-based and predictive value weighting method for misclassification correction coupled with sensitivity analysis is proposed and a second approach using the expectation-maximization algorithm is developed. The correction strategy requires knowledge of a plausible range of sensitivity and specificity parameters. The approaches are applied to a perinatal epidemiological study of the determinants of pre-term birth.
    Biostatistics 03/2014; · 2.43 Impact Factor
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    Elizabeth L. Ogburn, Tyler J. VanderWeele
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    ABSTRACT: Consider the causal effect that one individual's treatment may have on another individual's outcome when the outcome is contagious, with specific application to the effect of vaccination on an infectious disease outcome. The effect of one individual's vaccination on another's outcome can be decomposed into two different causal effects, called the "infectiousness" and "contagion" effects. We present identifying assumptions and estimation or testing procedures for infectiousness and contagion effects in two different settings: (1) using data sampled from independent groups of observations, and (2) using data collected from a single interdependent social network. The methods that we propose for social network data require fitting generalized linear models (GLMs). GLMs and other statistical models that require independence across subjects have been used widely to estimate causal effects in social network data, but, because the subjects in networks are presumably not independent, the use of such models is generally invalid, resulting in inference that is expected to be anticonservative. We introduce a way to ensure that GLM residuals are uncorrelated across subjects despite the fact that outcomes are non-independent. This simultaneously demonstrates the possibility of using GLMs and related statistical models for network data and highlights their limitations.
    03/2014;
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    Elizabeth L. Ogburn, Tyler J. VanderWeele
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    ABSTRACT: The term "interference" has been used to describe any setting in which one subject's exposure may affect another subject's outcome. We use causal diagrams to distinguish among three causal mechanisms that give rise to interference. The first causal mechanism by which interference can operate is a direct causal effect of one individual's treatment on another individual's outcome; we call this direct interference. Interference by contagion is present when one individual's outcome may affect the outcomes of other individuals with whom he comes into contact. Then giving treatment to the first individual could have an indirect effect on others through the treated individual's outcome. The third pathway by which interference may operate is allocational interference. Treatment in this case allocates individuals to groups; through interactions within a group, individuals' characteristics may affect one another. In many settings more than one type of interference will be present simultaneously. The causal effects of interest differ according to which types of interference are present, as do the conditions under which causal effects are identifiable. Using causal diagrams for interference, we describe these differences, give criteria for the identification of important causal effects, and discuss applications to infectious diseases.
    03/2014;
  • Tyler J Vanderweele, Stijn Vansteelandt, James M Robins
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    ABSTRACT: Methods from causal mediation analysis have generalized the traditional approach to direct and indirect effects in the epidemiologic and social science literature by allowing for interaction and nonlinearities. However, the methods from the causal inference literature have themselves been subject to a major limitation, in that the so-called natural direct and indirect effects that are used are not identified from data whenever there is a mediator-outcome confounder that is also affected by the exposure. In this article, we describe three alternative approaches to effect decomposition that give quantities that can be interpreted as direct and indirect effects and that can be identified from data even in the presence of an exposure-induced mediator-outcome confounder. We describe a simple weighting-based estimation method for each of these three approaches, illustrated with data from perinatal epidemiology. The methods described here can shed insight into pathways and questions of mediation even when an exposure-induced mediator-outcome confounder is present.
    Epidemiology (Cambridge, Mass.) 03/2014; 25(2):300-6. · 5.51 Impact Factor
  • Eric J Tchetgen Tchetgen, Tyler J Vanderweele
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    ABSTRACT: Natural direct and indirect effects formalize traditional notions of mediation analysis into a rigorous causal framework and have recently received considerable attention in epidemiology and in social sciences. Sufficient conditions for the identification of natural direct effects were formulated by Judea Pearl under a nonparametric structural equations model, which assumes certain independencies between potential outcomes. A common situation in epidemiology is that a confounder of the mediator-outcome relationship is itself affected by the exposure, in which case natural direct effects fail to be nonparametrically identified without additional assumptions, even under Pearl's nonparametric structural equations model. In this article, we show that when a single binary confounder of the mediator is affected by the exposure, the natural direct effect is nonparametrically identified under the model, assuming monotonicity about the effect of the exposure on the confounder. A similar result is shown to hold for a vector of binary confounders of the mediator under a certain independence assumption about the confounders. Finally, we show that natural direct effects are more generally identified if there is no additive mean interaction between the mediator and the confounders of the mediator affected by exposure. When correct, this latter assumption is particularly appealing because it does not require monotonicity of effects of the exposure. In addition, it places no restriction on the nature of the confounders of the mediator, which can be continuous or polytomous.
    Epidemiology (Cambridge, Mass.) 03/2014; 25(2):282-91. · 5.51 Impact Factor
  • Etsuji Suzuki, David Evans, Basile Chaix, Tyler J Vanderweele
    Epidemiology (Cambridge, Mass.) 03/2014; 25(2):309-10. · 5.51 Impact Factor
  • Yen-Tsung Huang, Tyler J Vanderweele, Xihong Lin
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    ABSTRACT: Genetic association studies have been a popular approach for assessing the association between common Single Nucleotide Polymorphisms (SNPs) and complex diseases. However, other genomic data involved in the mechanism from SNPs to disease, e.g., gene expressions, are usually neglected in these association studies. In this paper, we propose to exploit gene expression information to more powerfully test the association between SNPs and diseases by jointly modeling the relations among SNPs, gene expressions and diseases. We propose a variance component test for the total effect of SNPs and a gene expression on disease risk. We cast the test within the causal mediation analysis framework with the gene expression as a potential mediator. For eQTL SNPs, the use of gene expression information can enhance power to test for the total effect of a SNP-set, which are the combined direct and indirect effects of the SNPs mediated through the gene expression, on disease risk. We show that the test statistic under the null hypothesis follows a mixture of χ (2) distributions, which can be evaluated analytically or empirically using the resampling-based perturbation method. We construct tests for each of three disease models that is determined by SNPs only, SNPs and gene expression, or includes also their interactions. As the true disease model is unknown in practice, we further propose an omnibus test to accommodate different underlying disease models. We evaluate the finite sample performance of the proposed methods using simulation studies, and show that our proposed test performs well and the omnibus test can almost reach the optimal power where the disease model is known and correctly specified. We apply our method to re-analyze the overall effect of the SNP-set and expression of the ORMDL3 gene on the risk of asthma.
    The Annals of Applied Statistics 03/2014; 8(1):352-376. · 2.24 Impact Factor
  • Tyler J Vanderweele, Stijn Vansteelandt
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    ABSTRACT: In the accompanying commentary, Rose and van der Laan (Am J Epidemiol. 0000;000(00):000-000) criticize the relative excess risk due to interaction (RERI) measure, the use of additive interaction, and the weighting approach we developed to assess RERI with case-control data. In this commentary, we note some of the advantages of using additive measures of interaction, such as RERI, in making decisions about targeting interventions toward certain subgroups and in assessing mechanistic interaction. We discuss the relationship between Rose and van der Laan's estimator for case-control data and the one we had previously proposed. We also develop a new doubly robust estimator for determining the RERI with case-control data when the prevalence or incidence of the outcome is known.
    American journal of epidemiology 01/2014; · 5.59 Impact Factor
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    ABSTRACT: Spiritual care (SC) from medical practitioners is infrequent at the end of life (EOL) despite national standards. The study aimed to describe nurses' and physicians' desire to provide SC to terminally ill patients and assess 11 potential SC barriers. This was a survey-based, multisite study conducted from October 2008 through January 2009. All eligible oncology nurses and physicians at four Boston academic centers were approached for study participation; 339 nurses and physicians participated (response rate=63%). Most nurses and physicians desire to provide SC within the setting of terminal illness (74% vs. 60%, respectively; P=0.002); however, 40% of nurses/physicians provide SC less often than they desire. The most highly endorsed barriers were "lack of private space" for nurses and "lack of time" for physicians, but neither was associated with actual SC provision. Barriers that predicted less frequent SC for all medical professionals included inadequate training (nurses: odds ratio [OR]=0.28, 95% confidence interval [CI]=0.12-0.73, P=0.01; physicians: OR=0.49, 95% CI=0.25-0.95, P=0.04), "not my professional role" (nurses: OR=0.21, 95% CI=0.07-0.61, P=0.004; physicians: OR=0.35, 95% CI=0.17-0.72, P=0.004), and "power inequity with patient" (nurses: OR=0.33, 95% CI=0.12-0.87, P=0.03; physicians: OR=0.41, 95% CI=0.21-0.78, P=0.007). A minority of nurses and physicians (21% and 49%, P=0.003, respectively) did not desire SC training. Those less likely to desire SC training reported lower self-ratings of spirituality (nurses: OR=5.00, 95% CI=1.82-12.50, P=0.002; physicians: OR=3.33, 95% CI=1.82-5.88, P<0.001) and male gender (physicians: OR=3.03, 95% CI=1.67-5.56, P<0.001). SC training is suggested to be critical to the provision of SC in accordance with national care quality standards.
    Journal of pain and symptom management 01/2014; · 2.42 Impact Factor
  • Linda Valeri, Tyler J Vanderweele
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    ABSTRACT: Reports an error in "Mediation analysis allowing for exposure-mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros" by Linda Valeri and Tyler J. VanderWeele (Psychological Methods, 2013[Jun], Vol 18[2], 137-150). The technical appendix was missing from the original supplemental materials. The appendix has been added to the supplemental materials. (The following abstract of the original article appeared in record 2013-03476-001.) Mediation analysis is a useful and widely employed approach to studies in the field of psychology and in the social and biomedical sciences. The contributions of this article are several-fold. First we seek to bring the developments in mediation analysis for nonlinear models within the counterfactual framework to the psychology audience in an accessible format and compare the sorts of inferences about mediation that are possible in the presence of exposure-mediator interaction when using a counterfactual versus the standard statistical approach. Second, the work by VanderWeele and Vansteelandt (2009, 2010) is extended here to allow for dichotomous mediators and count outcomes. Third, we provide SAS and SPSS macros to implement all of these mediation analysis techniques automatically, and we compare the types of inferences about mediation that are allowed by a variety of software macros. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
    Psychological Methods 12/2013; 18(4):474. · 4.45 Impact Factor
  • Source
    Brian Sauer, Tyler J Vanderweele
  • Tyler J Vanderweele
    European Journal of Epidemiology 09/2013; · 5.12 Impact Factor

Publication Stats

2k Citations
679.23 Total Impact Points

Institutions

  • 2014
    • University of North Carolina at Chapel Hill
      North Carolina, United States
  • 2010–2014
    • Massachusetts Department of Public Health
      Boston, Massachusetts, United States
  • 2006–2014
    • Harvard University
      • • Department of Epidemiology
      • • Department of Biostatistics
      Cambridge, Massachusetts, United States
    • Harvard Medical School
      • Department of Medicine
      Boston, Massachusetts, United States
  • 2013
    • University of Utah
      • Division of Epidemiology
      Salt Lake City, UT, United States
  • 2006–2013
    • University of Chicago
      • • Department of Psychology
      • • Department of Health Studies
      Chicago, IL, United States
  • 2012
    • University Medical Center Utrecht
      • Julius Center for Health Sciences and Primary Care
      Utrecht, Provincie Utrecht, Netherlands
    • McGill University
      • Department of Epidemiology, Biostatistics and Occupational Health
      Montréal, Quebec, Canada
  • 2011–2012
    • Dana-Farber Cancer Institute
      • Center for Psycho-Oncology and Palliative Care Research
      Boston, Massachusetts, United States
    • Eunice Kennedy Shriver National Institute of Child Health and Human Development
      Maryland, United States
    • Robert Wood Johnson University Hospital
      New Brunswick, New Jersey, United States
    • Wuhan University
      • School of Public Health
      Wuhan, Hubei, China
    • University of California, Berkeley
      • School of Public Health
      Berkeley, CA, United States
    • Princeton University
      Princeton, New Jersey, United States
    • Kinki University
      Ōsaka, Ōsaka, Japan
    • Columbia University
      • Department of Epidemiology
      New York City, NY, United States
  • 2008–2012
    • Ghent University
      • Department of Applied Mathematics and Computer Science
      Gent, VLG, Belgium
    • McLean Hospital
      • Biological Psychiatry Laboratory
      Cambridge, MA, United States
  • 2009–2010
    • Northwestern University
      • Department of Preventive Medicine
      Evanston, IL, United States
    • University of Illinois at Chicago
      Chicago, Illinois, United States