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  1. Article ; Online: More Efficient and Effective Clinical Decision-Making

    Elizabeth L. Ogburn

    Harvard Data Science Review (2021)

    2021  

    Keywords Electronic computers. Computer science ; QA75.5-76.95
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher The MIT Press
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Finding influential subjects in a network using a causal framework.

    Lee, Youjin / Buchanan, Ashley L / Ogburn, Elizabeth L / Friedman, Samuel R / Halloran, M Elizabeth / Katenka, Natallia V / Wu, Jing / Nikolopoulos, Georgios K

    Biometrics

    2023  Volume 79, Issue 4, Page(s) 3715–3727

    Abstract: Researchers across a wide array of disciplines are interested in finding the most influential subjects in a network. In a network setting, intervention effects and health outcomes can spill over from one node to another through network ties, and ... ...

    Abstract Researchers across a wide array of disciplines are interested in finding the most influential subjects in a network. In a network setting, intervention effects and health outcomes can spill over from one node to another through network ties, and influential subjects are expected to have a greater impact than others. For this reason, network research in public health has attempted to maximize health and behavioral changes by intervening on a subset of influential subjects. Although influence is often defined only implicitly in most of the literature, the operative notion of influence is inherently causal in many cases: influential subjects are those we should intervene on to achieve the greatest overall effect across the entire network. In this work, we define a causal notion of influence using potential outcomes. We review existing influence measures, such as node centrality, that largely rely on the particular features of the network structure and/or on certain diffusion models that predict the pattern of information or diseases spreads through network ties. We provide simulation studies to demonstrate when popular centrality measures can agree with our causal measure of influence. As an illustrative example, we apply several popular centrality measures to the HIV risk network in the Transmission Reduction Intervention Project and demonstrate the assumptions under which each centrality can represent the causal influence of each participant in the study.
    MeSH term(s) Humans ; Computer Simulation
    Language English
    Publishing date 2023-02-28
    Publishing country England
    Document type Review ; Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 213543-7
    ISSN 1541-0420 ; 0099-4987 ; 0006-341X
    ISSN (online) 1541-0420
    ISSN 0099-4987 ; 0006-341X
    DOI 10.1111/biom.13841
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  3. Book ; Online: Augmented balancing weights as linear regression

    Bruns-Smith, David / Dukes, Oliver / Feller, Avi / Ogburn, Elizabeth L.

    2023  

    Abstract: We provide a novel characterization of augmented balancing weights, also known as Automatic Debiased Machine Learning (AutoDML). These estimators combine outcome modeling with balancing weights, which estimate inverse propensity score weights directly. ... ...

    Abstract We provide a novel characterization of augmented balancing weights, also known as Automatic Debiased Machine Learning (AutoDML). These estimators combine outcome modeling with balancing weights, which estimate inverse propensity score weights directly. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients that combine the original outcome model coefficients and OLS; in many settings, the augmented estimator collapses to OLS alone. We then extend these results to specific choices of outcome and weighting models. We first show that the combined estimator that uses (kernel) ridge regression for both outcome and weighting models is equivalent to a single, undersmoothed (kernel) ridge regression; this also holds when considering asymptotic rates. When the weighting model is instead lasso regression, we give closed-form expressions for special cases and demonstrate a ``double selection'' property. Finally, we generalize these results to linear estimands via the Riesz representer. Our framework ``opens the black box'' on these increasingly popular estimators and provides important insights into estimation choices for augmented balancing weights.
    Keywords Statistics - Methodology ; Computer Science - Machine Learning ; Economics - Econometrics ; Statistics - Machine Learning
    Subject code 310 ; 519
    Publishing date 2023-04-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Network Dependence Can Lead to Spurious Associations and Invalid Inference

    Lee, Youjin / Ogburn, Elizabeth L.

    Journal of the American Statistical Association. 2021 July 3, v. 116, no. 535 p.1060-1074

    2021  

    Abstract: Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a small number of communities, schools, hospitals, etc. A paradigmatic example ... ...

    Abstract Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a small number of communities, schools, hospitals, etc. A paradigmatic example of this is the Framingham Heart Study (FHS). Many of the limitations of such samples are well-known, but the issue of statistical dependence due to social network ties has not previously been addressed. We show that, along with anticonservative variance estimation, this can result in spurious associations due to network dependence. Using a statistical test that we adapted from one developed for spatial autocorrelation, we test for network dependence in several of the thousands of influential papers that have been published using FHS data. Results suggest that some of the many decades of research on coronary heart disease, other health outcomes, and peer influence using FHS data may suffer from spurious associations, error-prone point estimates, and anticonservative inference due to unacknowledged network dependence. These issues are not unique to the FHS; as researchers in psychology, medicine, and beyond grapple with replication failures, this unacknowledged source of invalid statistical inference should be part of the conversation. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
    Keywords autocorrelation ; coronary disease ; medicine ; psychology ; social networks ; statistical inference ; variance ; Confounding ; Replication ; Statistical dependence
    Language English
    Dates of publication 2021-0703
    Size p. 1060-1074.
    Publishing place Taylor & Francis
    Document type Article ; Online
    ZDB-ID 2064981-2
    ISSN 1537-274X
    ISSN 1537-274X
    DOI 10.1080/01621459.2020.1782219
    Database NAL-Catalogue (AGRICOLA)

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  5. Article: Commentary on "Mediation analysis without sequential ignorability: Using baseline covariates interacted with random assignment as instrumental variables" by Dylan Small.

    Ogburn, Elizabeth L

    Journal of statistical research

    2014  Volume 46, Issue 2, Page(s) 105–111

    Abstract: I applaud Dr. Small for advancing causal mediation analysis and thank the editors for the opportunity to comment on this valuable article. Small's project was to relax and test the assumptions on which a previously proposed model relies; in the second ... ...

    Abstract I applaud Dr. Small for advancing causal mediation analysis and thank the editors for the opportunity to comment on this valuable article. Small's project was to relax and test the assumptions on which a previously proposed model relies; in the second half of this discussion I will assess those assumptions and others on which the model hinges. But first I will review the various schools of mediation analysis and situate the estimand considered by Small within the somewhat esoteric domain of mediation estimands.
    Language English
    Publishing date 2014-12-01
    Publishing country Bangladesh
    Document type Journal Article
    ISSN 0256-422X
    ISSN 0256-422X
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  6. Article: Causal inference, social networks and chain graphs.

    Ogburn, Elizabeth L / Shpitser, Ilya / Lee, Youjin

    Journal of the Royal Statistical Society. Series A, (Statistics in Society)

    2020  Volume 183, Issue 4, Page(s) 1659–1676

    Abstract: Traditionally, statistical inference and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social ... ...

    Abstract Traditionally, statistical inference and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social networks, where individuals may interact with one another such that treatments may spill over from the treated individual to their social contacts and outcomes may be contagious. Existing models proposed for causal inference using observational data from networks of interacting individuals have two major shortcomings. First, they often require a level of granularity in the data that is infeasible in practice to collect in most settings and, second, the models are high dimensional and often too big to fit to the available data. We illustrate and justify a parsimonious parameterization for network data with interference and contagion. Our parameterization corresponds to a particular family of graphical models known as chain graphs. We argue that, in some settings, chain graph models approximate the marginal distribution of a snapshot of a longitudinal data-generating process on interacting units. We illustrate the use of chain graphs for causal inference about collective decision making in social networks by using data from US Supreme Court decisions between 1994 and 2004 and in simulations.
    Language English
    Publishing date 2020-07-18
    Publishing country England
    Document type Journal Article
    ZDB-ID 1490715-X
    ISSN 1467-985X ; 0964-1998 ; 0035-9238
    ISSN (online) 1467-985X
    ISSN 0964-1998 ; 0035-9238
    DOI 10.1111/rssa.12594
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Rejoinder to "Robustness of ANCOVA in randomized trials with unequal randomization" by Jonathan W. Bartlett.

    Wang, Bingkai / Ogburn, Elizabeth L / Rosenblum, Michael

    Biometrics

    2019  Volume 76, Issue 3, Page(s) 1039

    MeSH term(s) Analysis of Variance ; Confidence Intervals ; Models, Statistical ; Random Allocation ; Randomized Controlled Trials as Topic
    Language English
    Publishing date 2019-12-11
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 213543-7
    ISSN 1541-0420 ; 0099-4987 ; 0006-341X
    ISSN (online) 1541-0420
    ISSN 0099-4987 ; 0006-341X
    DOI 10.1111/biom.13182
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  8. Article ; Online: Ogburn et al. Respond to "Estimation and Bounds Under Data Fusion".

    Ogburn, Elizabeth L / Rudolph, Kara E / Morello-Frosch, Rachel / Khan, Amber / Casey, Joan A

    American journal of epidemiology

    2021  Volume 191, Issue 4, Page(s) 679–680

    MeSH term(s) Computer Simulation ; Humans
    Language English
    Publishing date 2021-06-26
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Comment
    ZDB-ID 2937-3
    ISSN 1476-6256 ; 0002-9262
    ISSN (online) 1476-6256
    ISSN 0002-9262
    DOI 10.1093/aje/kwab195
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  9. Article ; Online: Analysis of covariance in randomized trials: More precision and valid confidence intervals, without model assumptions.

    Wang, Bingkai / Ogburn, Elizabeth L / Rosenblum, Michael

    Biometrics

    2019  Volume 75, Issue 4, Page(s) 1391–1400

    Abstract: Covariate adjustment" in the randomized trial context refers to an estimator of the average treatment effect that adjusts for chance imbalances between study arms in baseline variables (called "covariates"). The baseline variables could include, for ... ...

    Abstract "Covariate adjustment" in the randomized trial context refers to an estimator of the average treatment effect that adjusts for chance imbalances between study arms in baseline variables (called "covariates"). The baseline variables could include, for example, age, sex, disease severity, and biomarkers. According to two surveys of clinical trial reports, there is confusion about the statistical properties of covariate adjustment. We focus on the analysis of covariance (ANCOVA) estimator, which involves fitting a linear model for the outcome given the treatment arm and baseline variables, and trials that use simple randomization with equal probability of assignment to treatment and control. We prove the following new (to the best of our knowledge) robustness property of ANCOVA to arbitrary model misspecification: Not only is the ANCOVA point estimate consistent (as proved by Yang and Tsiatis, 2001) but so is its standard error. This implies that confidence intervals and hypothesis tests conducted as if the linear model were correct are still asymptotically valid even when the linear model is arbitrarily misspecified, for example, when the baseline variables are nonlinearly related to the outcome or there is treatment effect heterogeneity. We also give a simple, robust formula for the variance reduction (equivalently, sample size reduction) from using ANCOVA. By reanalyzing completed randomized trials for mild cognitive impairment, schizophrenia, and depression, we demonstrate how ANCOVA can achieve variance reductions of 4 to 32%.
    MeSH term(s) Analysis of Variance ; Computer Simulation ; Confidence Intervals ; Data Interpretation, Statistical ; Humans ; Linear Models ; Mental Disorders ; Models, Statistical ; Randomized Controlled Trials as Topic/statistics & numerical data ; Sample Size ; Treatment Outcome
    Language English
    Publishing date 2019-06-03
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 213543-7
    ISSN 1541-0420 ; 0099-4987 ; 0006-341X
    ISSN (online) 1541-0420
    ISSN 0099-4987 ; 0006-341X
    DOI 10.1111/biom.13062
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  10. Article ; Online: A Warning About Using Predicted Values From Regression Models for Epidemiologic Inquiry.

    Ogburn, Elizabeth L / Rudolph, Kara E / Morello-Frosch, Rachel / Khan, Amber / Casey, Joan A

    American journal of epidemiology

    2021  Volume 190, Issue 6, Page(s) 1142–1147

    Abstract: In many settings, researchers may not have direct access to data on 1 or more variables needed for an analysis and instead may use regression-based estimates of those variables. Using such estimates in place of original data, however, introduces ... ...

    Abstract In many settings, researchers may not have direct access to data on 1 or more variables needed for an analysis and instead may use regression-based estimates of those variables. Using such estimates in place of original data, however, introduces complications and can result in uninterpretable analyses. In simulations and observational data, we illustrate the issues that arise when an average treatment effect is estimated from data where the outcome of interest is predicted from an auxiliary model. We show that bias in any direction can result, under both the null and alternative hypotheses.
    MeSH term(s) Bias ; Data Interpretation, Statistical ; Epidemiologic Studies ; Forecasting ; Humans ; Models, Statistical ; Regression Analysis
    Language English
    Publishing date 2021-01-20
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2937-3
    ISSN 1476-6256 ; 0002-9262
    ISSN (online) 1476-6256
    ISSN 0002-9262
    DOI 10.1093/aje/kwaa282
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