Tyler J VanderWeele

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

Are you Tyler J VanderWeele?

Claim your profile

Publications (176)750.5 Total impact

  • Tyler J VanderWeele, Eric J Tchetgen Tchetgen
    Epidemiology (Cambridge, Mass.) 02/2015; · 6.18 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The purpose of this study is to determine how oncology nurses and physicians view their role in providing spiritual care (SC), factors influencing this perception, and how this belief affects SC provision. This is a survey-based, multisite study conducted from October 2008 to January 2009. All oncology physicians and nurses caring for advanced cancer patients at four Boston, MA cancer centers were invited to participate; 339 participated (response rate = 63 %). Nurses were more likely than physicians to report that it is the role of medical practitioners to provide SC, including for doctors (69 vs. 49 %, p < 0.001), nurses (73 vs. 49 %, p < 0.001), and social workers (81 vs. 63 %, p = 0.001). Among nurses, older age was the only variable that was predictive of this belief [adjusted odds ratio (AOR) 1.08; 1.01-1.16, p = 0.02]. For nurses, role perception was not related to actual SC provision to patients. In contrast, physicians' role perceptions were influenced by their intrinsic religiosity (AOR, 1.44; 95 % CI, 1.09-1.89; p = 0.01) and spirituality (AOR, 6.41; 95 % CI, 2.31-17.73, p < 0.001). Furthermore, physicians who perceive themselves as having a role in SC provision reported greater SC provision to their last advanced cancer patients seen in clinic, 69 % compared to 31 %, p < 0.001. Nurses are more likely than physicians to perceive medical practitioners as having a role in SC provision. Physicians' perceptions of their role in SC provision are influenced by their religious/spiritual characteristics and are predictive of actual SC provision to patients. Spiritual care training that includes improved understanding of clinicians' appropriate role in SC provision to severely ill patients may lead to increased SC provision.
    Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer. 01/2015;
  • Environmental Health Perspectives 01/2015; · 7.03 Impact Factor
  • Etsuji Suzuki, Tyler J VanderWeele
    [Show abstract] [Hide abstract]
    ABSTRACT: Under Bateson's original conception, the term "epistasis" is used to describe the situation in which the effect of a genetic factor at one locus is masked by a variant at another locus. Epistasis in the sense of masking has been termed "compositional epistasis." In general, statistical tests for interaction are of limited use in detecting compositional epistasis. Using recently developed epidemiological methods, however, it has been shown that there are relations between empirical data patterns and compositional epistasis. These relations can sometimes be exploited to empirically test for certain forms of compositional epistasis, by using alternative nonstandard tests for interaction.Using the counterfactual framework, we show conditions that can be empirically tested to determine whether there are individuals whose phenotype response patterns manifest epistasis in the sense of masking. Only under some very strong assumptions would tests for standard statistical interactions correspond to compositional epistasis. Even without such strong assumptions, however, one can still test whether there are individuals of phenotype response type representing compositional epistasis. The empirical conditions are quite strong, but the conclusions which tests of these conditions allow may be of interest in a wide range of studies. This chapter highlights that epidemiologic perspectives can be used to shed light on underlying mechanisms at the genetic, molecular, and cellular levels.
    Methods in molecular biology (Clifton, N.J.) 01/2015; 1253:197-216. · 1.29 Impact Factor
  • Tyler J VanderWeele, Whitney R Robinson
    Epidemiology (Cambridge, Mass.) 11/2014; 25(6):937-938. · 6.18 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Causal inference with interference is a rapidly growing area. The literature has begun to relax the "no-interference" assumption that the treatment received by one individual does not affect the outcomes of other individuals. In this paper we briefly review the literature on causal inference in the presence of interference when treatments have been randomized. We then consider settings in which causal effects in the presence of interference are not identified, either because randomization alone does not suffice for identification, or because treatment is not randomized and there may be unmeasured confounders of the treatment-outcome relationship. We develop sensitivity analysis techniques for these settings. We describe several sensitivity analysis techniques for the infectiousness effect which, in a vaccine trial, captures the effect of the vaccine of one person on protecting a second person from infection even if the first is infected. We also develop two sensitivity analysis techniques for causal effects in the presence of unmeasured confounding which generalize analogous techniques when interference is absent. These two techniques for unmeasured confounding are compared and contrasted.
    Statistical science : a review journal of the Institute of Mathematical Statistics. 11/2014; 29(4):687-706.
  • Tyler J VanderWeele
    International Journal of Epidemiology 10/2014; 43(5):1368-73. · 9.20 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Additive interactions can have public health and etiological implications but are infrequently reported. We assessed departures from additivity on the absolute risk scale between 9 established breast cancer risk factors and 23 susceptibility single-nucleotide polymorphisms (SNPs) identified from genome-wide association studies among 10,146 non-Hispanic white breast cancer cases and 12,760 controls within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium. We estimated the relative excess risk due to interaction and its 95% confidence interval for each pairwise combination of SNPs and nongenetic risk factors using age- and cohort-adjusted logistic regression models. After correction for multiple comparisons, we identified a statistically significant relative excess risk due to interaction (uncorrected P = 4.51 × 10(-5)) between a SNP in the DNA repair protein RAD51 homolog 2 gene (RAD51L1; rs10483813) and body mass index (weight (kg)/height (m)(2)). We also compared additive and multiplicative polygenic risk prediction models using per-allele odds ratio estimates from previous studies for breast-cancer susceptibility SNPs and observed that the multiplicative model had a substantially better goodness of fit than the additive model.
    American journal of epidemiology. 09/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Antipsychotic drugs are used to treat dementia-related symptoms in older adults, and observational studies show higher risks of death and stroke associated with the use of first-generation antipsychotic drugs (FGAs) compared with second-generation antipsychotic drugs (SGAs). However, the extent to which stroke explains the differential mortality risk between FGA use and SGA use in older adults is unclear. We followed those who initiated use of antipsychotic drugs (9,777 FGA users and 21,164 SGA users) aged 65 years or older, and who were enrolled in Medicare and either the New Jersey or Pennsylvania pharmacy assistance program during 1994 to 2005, over 180 days for the outcomes of stroke and death. We estimated direct and indirect effects by comparing 180-day mortality risks associated with the use of FGAs versus SGAs as mediated by stroke on the risk ratio scale, as well as the proportion mediated on the risk difference scale. FGA use was associated with marginally higher risks of stroke (risk ratio =1.24, 95% confidence interval (CI): 1.01, 1.53) and death (risk ratio = 1.15, 95% CI: 1.08, 1.22) compared with SGA use, but stroke explained little (2.7%) of the observed difference in mortality risk. The indirect effect was null (risk ratio = 1.004, 95% CI: 1.000, 1.008), and the direct effect was equal to the total effect of antipsychotic drug type (FGA vs. SGA) on mortality risk (risk ratio = 1.15, 95% CI: 1.08, 1.22). These results suggest that the difference in mortality risk between users of FGAs and SGAs may develop mostly through pathways that do not involve stroke.
    American Journal of Epidemiology 09/2014; · 4.98 Impact Factor
  • Linda Valeri, Xihong Lin, Tyler J VanderWeele
    [Show abstract] [Hide abstract]
    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. · 6.18 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Observational studies have reported higher mortality among older adults treated with first-generation antipsychotics (FGAs) versus second-generation antipsychotics (SGAs). A few studies examined risk for medical events, including stroke, ventricular arrhythmia, venous thromboembolism, myocardial infarction, pneumonia, and hip fracture.
    PLoS ONE 08/2014; 9(8):e105376. · 3.53 Impact Factor
  • Tyler J VanderWeele, Eric J Tchetgen Tchetgen
    [Show abstract] [Hide abstract]
    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; · 6.18 Impact Factor
  • [Show abstract] [Hide abstract]
    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
    [Show abstract] [Hide abstract]
    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; · 6.18 Impact Factor
  • Tyler J VanderWeele, Whitney R Robinson
    Epidemiology (Cambridge, Mass.) 07/2014; 25(4):491-493. · 6.18 Impact Factor
  • Tyler J VanderWeele, Whitney R Robinson
    [Show abstract] [Hide abstract]
    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. · 6.18 Impact Factor
  • [Show abstract] [Hide abstract]
    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; · 6.18 Impact Factor
  • Linda Valeri, Tyler J Vanderweele
    [Show abstract] [Hide abstract]
    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; · 2.24 Impact Factor
  • Source
    Elizabeth L. Ogburn, Tyler J. VanderWeele
    [Show abstract] [Hide abstract]
    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.

Publication Stats

2k Citations
750.50 Total Impact Points


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