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  1. Article ; Online: Combining information to answer epidemiological questions about a target population.

    Dahabreh, Issa J

    American journal of epidemiology

    2024  

    Abstract: ... novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2022;XXX(XX ...

    Abstract Epidemiologists are attempting to address research questions of increasing complexity by developing novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2022;XXX(XX):XXXX-XXXX) provide two examples of combining information to draw inferences about a population proportion. In this commentary, we consider combining information to learn about a target population as an epidemiological activity and distinguish it from more conventional meta-analyses. We examine possible rationales for combining information and discuss broad methodological considerations, with an emphasis on aspects of study design, including the selection among candidate data sources and the sampling of observations from these sources.
    Language English
    Publishing date 2024-03-07
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2937-3
    ISSN 1476-6256 ; 0002-9262
    ISSN (online) 1476-6256
    ISSN 0002-9262
    DOI 10.1093/aje/kwad014
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  2. Article ; Online: Toward Personalizing Care: Assessing Heterogeneity of Treatment Effects in Randomized Trials.

    Dahabreh, Issa J / Kazi, Dhruv S

    JAMA

    2023  Volume 329, Issue 13, Page(s) 1063–1065

    MeSH term(s) Randomized Controlled Trials as Topic ; Precision Medicine ; Treatment Outcome
    Language English
    Publishing date 2023-03-21
    Publishing country United States
    Document type Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Editorial ; Comment
    ZDB-ID 2958-0
    ISSN 1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484
    ISSN (online) 1538-3598
    ISSN 0254-9077 ; 0002-9955 ; 0098-7484
    DOI 10.1001/jama.2023.3576
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  3. Article ; Online: The US Health Equity Crisis-An Economic Case for a Moral Imperative?

    Wadhera, Rishi K / Dahabreh, Issa J

    JAMA

    2023  Volume 329, Issue 19, Page(s) 1647–1649

    MeSH term(s) Humans ; Health Equity/economics ; Health Equity/ethics ; Morals ; United States
    Language English
    Publishing date 2023-05-16
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 2958-0
    ISSN 1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484
    ISSN (online) 1538-3598
    ISSN 0254-9077 ; 0002-9955 ; 0098-7484
    DOI 10.1001/jama.2023.4018
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  4. Article ; Online: Using Effect Scores to Characterize Heterogeneity of Treatment Effects.

    Wang, Guanbo / Heagerty, Patrick J / Dahabreh, Issa J

    JAMA

    2024  Volume 331, Issue 14, Page(s) 1225–1226

    MeSH term(s) Humans ; Critical Illness/therapy ; Oximetry ; Oxygen/analysis ; Treatment Effect Heterogeneity ; Patient Acuity ; Randomized Controlled Trials as Topic ; Models, Statistical
    Chemical Substances Oxygen (S88TT14065)
    Language English
    Publishing date 2024-03-18
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 2958-0
    ISSN 1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484
    ISSN (online) 1538-3598
    ISSN 0254-9077 ; 0002-9955 ; 0098-7484
    DOI 10.1001/jama.2024.3376
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  5. Article ; Online: Cluster Randomized Trials Designed to Support Generalizable Inferences.

    Robertson, Sarah E / Steingrimsson, Jon A / Dahabreh, Issa J

    Evaluation review

    2024  , Page(s) 193841X231169557

    Abstract: When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in ...

    Abstract When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude simple random sampling from the cohort into the trial, and thus interfere with the goal of producing generalizable inferences about the target population. We describe a nested trial design where the randomized clusters are embedded within a cohort of trial-eligible clusters from the target population and where clusters are selected for inclusion in the trial with known sampling probabilities that may depend on cluster characteristics (e.g., allowing clusters to be chosen to facilitate trial conduct or to examine hypotheses related to their characteristics). We develop and evaluate methods for analyzing data from this design to generalize causal inferences to the target population underlying the cohort. We present identification and estimation results for the expectation of the average potential outcome and for the average treatment effect, in the entire target population of clusters and in its non-randomized subset. In simulation studies, we show that all the estimators have low bias but markedly different precision. Cluster randomized trials where clusters are selected for inclusion with known sampling probabilities that depend on cluster characteristics, combined with efficient estimation methods, can precisely quantify treatment effects in the target population, while addressing objectives of trial conduct that require oversampling clusters on the basis of their characteristics.
    Language English
    Publishing date 2024-01-17
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1500138-6
    ISSN 1552-3926 ; 0193-841X ; 0145-4692
    ISSN (online) 1552-3926
    ISSN 0193-841X ; 0145-4692
    DOI 10.1177/0193841X231169557
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  6. Article ; Online: Should We Add Fludrocortisone to Hydrocortisone for Treatment of Septic Shock?

    Matthay, Michael A / Dahabreh, Issa J / Thompson, B Taylor

    JAMA internal medicine

    2023  Volume 183, Issue 5, Page(s) 460–461

    MeSH term(s) Humans ; Hydrocortisone/therapeutic use ; Fludrocortisone/therapeutic use ; Shock, Septic/drug therapy ; Anti-Inflammatory Agents ; Drug Therapy, Combination
    Chemical Substances Hydrocortisone (WI4X0X7BPJ) ; Fludrocortisone (U0476M545B) ; Anti-Inflammatory Agents
    Language English
    Publishing date 2023-03-26
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 2699338-7
    ISSN 2168-6114 ; 2168-6106
    ISSN (online) 2168-6114
    ISSN 2168-6106
    DOI 10.1001/jamainternmed.2023.0257
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  7. Article ; Online: Randomization, randomized trials, and analyses using observational data: A commentary on Deaton and Cartwright.

    Dahabreh, Issa J

    Social science & medicine (1982)

    2018  Volume 210, Page(s) 41–44

    MeSH term(s) Random Allocation ; Randomized Controlled Trials as Topic
    Language English
    Publishing date 2018-05-09
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 4766-1
    ISSN 1873-5347 ; 0037-7856 ; 0277-9536
    ISSN (online) 1873-5347
    ISSN 0037-7856 ; 0277-9536
    DOI 10.1016/j.socscimed.2018.05.012
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  8. Article ; Online: Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population.

    Robertson, Sarah E / Steingrimsson, Jon A / Dahabreh, Issa J

    European journal of epidemiology

    2023  Volume 38, Issue 2, Page(s) 123–133

    Abstract: Most work on extending (generalizing or transporting) inferences from a randomized trial to a target population has focused on estimating average treatment effects (i.e., averaged over the target population's covariate distribution). Yet, in the presence ...

    Abstract Most work on extending (generalizing or transporting) inferences from a randomized trial to a target population has focused on estimating average treatment effects (i.e., averaged over the target population's covariate distribution). Yet, in the presence of strong effect modification by baseline covariates, the average treatment effect in the target population may be less relevant for guiding treatment decisions. Instead, the conditional average treatment effect (CATE) as a function of key effect modifiers may be a more useful estimand. Recent work on estimating target population CATEs using baseline covariate, treatment, and outcome data from the trial and covariate data from the target population only allows for the examination of heterogeneity over distinct subgroups. We describe flexible pseudo-outcome regression modeling methods for estimating target population CATEs conditional on discrete or continuous baseline covariates when the trial is embedded in a sample from the target population (i.e., in nested trial designs). We construct pointwise confidence intervals for the CATE at a specific value of the effect modifiers and uniform confidence bands for the CATE function. Last, we illustrate the methods using data from the Coronary Artery Surgery Study (CASS) to estimate CATEs given history of myocardial infarction and baseline ejection fraction value in the target population of all trial-eligible patients with stable ischemic heart disease.
    MeSH term(s) Humans ; Myocardial Infarction ; Regression Analysis ; Research Design
    Language English
    Publishing date 2023-01-10
    Publishing country Netherlands
    Document type Randomized Controlled Trial ; Journal Article
    ZDB-ID 632614-6
    ISSN 1573-7284 ; 0393-2990
    ISSN (online) 1573-7284
    ISSN 0393-2990
    DOI 10.1007/s10654-022-00901-5
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  9. Article ; Online: RE: USING NUMERICAL METHODS TO DESIGN SIMULATIONS: REVISITING THE BALANCING INTERCEPT.

    Robertson, Sarah E / Steingrimsson, Jon A / Dahabreh, Issa J

    American journal of epidemiology

    2022  Volume 191, Issue 10, Page(s) 1834–1835

    Language English
    Publishing date 2022-05-05
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 2937-3
    ISSN 1476-6256 ; 0002-9262
    ISSN (online) 1476-6256
    ISSN 0002-9262
    DOI 10.1093/aje/kwac084
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  10. Article ; Online: Systematically missing data in causally interpretable meta-analysis.

    Steingrimsson, Jon A / Barker, David H / Bie, Ruofan / Dahabreh, Issa J

    Biostatistics (Oxford, England)

    2023  Volume 25, Issue 2, Page(s) 289–305

    Abstract: Causally interpretable meta-analysis combines information from a collection of randomized controlled trials to estimate treatment effects in a target population in which experimentation may not be possible but from which covariate information can be ... ...

    Abstract Causally interpretable meta-analysis combines information from a collection of randomized controlled trials to estimate treatment effects in a target population in which experimentation may not be possible but from which covariate information can be obtained. In such analyses, a key practical challenge is the presence of systematically missing data when some trials have collected data on one or more baseline covariates, but other trials have not, such that the covariate information is missing for all participants in the latter. In this article, we provide identification results for potential (counterfactual) outcome means and average treatment effects in the target population when covariate data are systematically missing from some of the trials in the meta-analysis. We propose three estimators for the average treatment effect in the target population, examine their asymptotic properties, and show that they have good finite-sample performance in simulation studies. We use the estimators to analyze data from two large lung cancer screening trials and target population data from the National Health and Nutrition Examination Survey (NHANES). To accommodate the complex survey design of the NHANES, we modify the methods to incorporate survey sampling weights and allow for clustering.
    MeSH term(s) Humans ; Nutrition Surveys ; Early Detection of Cancer ; Lung Neoplasms/epidemiology ; Computer Simulation ; Research Design
    Language English
    Publishing date 2023-03-28
    Publishing country England
    Document type Meta-Analysis ; Journal Article
    ZDB-ID 2031500-4
    ISSN 1468-4357 ; 1465-4644
    ISSN (online) 1468-4357
    ISSN 1465-4644
    DOI 10.1093/biostatistics/kxad006
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