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  1. Article ; Online: Correction to: Much ado about something: a response to "COVID-19: underpowered randomised trials, or no randomised trials?"

    Haber, Noah A / Wieten, Sarah E / Smith, Emily R / Nunan, David

    Trials

    2021  Volume 22, Issue 1, Page(s) 951

    Language English
    Publishing date 2021-12-22
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2040523-6
    ISSN 1745-6215 ; 1468-6694 ; 1468-6708
    ISSN (online) 1745-6215 ; 1468-6694
    ISSN 1468-6708
    DOI 10.1186/s13063-021-05932-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: DAG With Omitted Objects Displayed (DAGWOOD): a framework for revealing causal assumptions in DAGs.

    Haber, Noah A / Wood, Mollie E / Wieten, Sarah / Breskin, Alexander

    Annals of epidemiology

    2022  Volume 68, Page(s) 64–71

    Abstract: Directed acyclic graphs (DAGs) are frequently used in epidemiology as a method to encode causal inference assumptions. We propose the DAGWOOD framework to bring many of those encoded assumptions to the forefront. DAGWOOD combines a root DAG (the DAG in ... ...

    Abstract Directed acyclic graphs (DAGs) are frequently used in epidemiology as a method to encode causal inference assumptions. We propose the DAGWOOD framework to bring many of those encoded assumptions to the forefront. DAGWOOD combines a root DAG (the DAG in the proposed analysis) and a set of branch DAGs (alternative hidden assumptions to the root DAG). All branch DAGs share a common ruleset, and must 1) change the root DAG, 2) be a valid DAG, and either 3a) change the minimally sufficient adjustment set or 3b) change the number of frontdoor paths. Branch DAGs comprise a list of assumptions which must be justified as negligible. We define two types of branch DAGs: exclusion branch DAGs add a single- or bidirectional pathway between two nodes in the root DAG (e.g., direct pathways and colliders), while misdirection branch DAGs represent alternative pathways that could be drawn between objects (e.g., creating a collider by reversing the direction of causation for a controlled confounder). The DAGWOOD framework 1) organizes causal model assumptions, 2) reinforces best DAG practices, 3) provides a framework for evaluation of causal models, and 4) can be used for generating causal models.
    MeSH term(s) Causality ; Confounding Factors, Epidemiologic ; Data Interpretation, Statistical ; Humans ; Models, Theoretical
    Language English
    Publishing date 2022-02-03
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1074355-8
    ISSN 1873-2585 ; 1047-2797
    ISSN (online) 1873-2585
    ISSN 1047-2797
    DOI 10.1016/j.annepidem.2022.01.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3225: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: Letter of concern regarding »Reduction in COVID-19 infection using surgical facial masks outside the healthcare system«.

    Haber, Noah A / Wieten, Sarah E / Smith, Emily R

    Danish medical journal

    2020  Volume 67, Issue 12

    MeSH term(s) COVID-19 ; Delivery of Health Care ; Humans ; Masks ; Pandemics ; SARS-CoV-2
    Language English
    Publishing date 2020-11-11
    Publishing country Denmark
    Document type Journal Article ; Comment
    ZDB-ID 2648771-8
    ISSN 2245-1919 ; 2245-1919
    ISSN (online) 2245-1919
    ISSN 2245-1919
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  4. Article ; Online: Much ado about something: a response to "COVID-19: underpowered randomised trials, or no randomised trials?"

    Haber, Noah A / Wieten, Sarah E / Smith, Emily R / Nunan, David

    Trials

    2021  Volume 22, Issue 1, Page(s) 780

    Abstract: Non-pharmaceutical interventions (NPI) for infectious diseases such as COVID-19 are particularly challenging given the complexities of what is both practical and ethical to randomize. We are often faced with the difficult decision between having weak ... ...

    Abstract Non-pharmaceutical interventions (NPI) for infectious diseases such as COVID-19 are particularly challenging given the complexities of what is both practical and ethical to randomize. We are often faced with the difficult decision between having weak trials or not having a trial at all. In a recent article, Dr. Atle Fretheim argues that statistically underpowered studies are still valuable, particularly in conjunction with other similar studies in meta-analysis in the context of the DANMASK-19 trial, asking "Surely, some trial evidence must be better than no trial evidence?" However, informative trials are not always feasible, and feasible trials are not always informative. In some cases, even a well-conducted but weakly designed and/or underpowered trial such as DANMASK-19 may be uninformative or worse, both individually and in a body of literature. Meta-analysis, for example, can only resolve issues of statistical power if there is a reasonable expectation of compatible well-designed trials. Uninformative designs may also invite misinformation. Here, we make the case that-when considering informativeness, ethics, and opportunity costs in addition to statistical power-"nothing" is often the better choice.
    MeSH term(s) COVID-19 ; Humans ; Randomized Controlled Trials as Topic
    Language English
    Publishing date 2021-11-07
    Publishing country England
    Document type Letter
    ZDB-ID 2040523-6
    ISSN 1745-6215 ; 1468-6694 ; 1745-6215
    ISSN (online) 1745-6215
    ISSN 1468-6694 ; 1745-6215
    DOI 10.1186/s13063-021-05755-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Correction to

    Noah A. Haber / Sarah E. Wieten / Emily R. Smith / David Nunan

    Trials, Vol 22, Iss 1, Pp 1-

    Much ado about something: a response to “COVID-19: underpowered randomised trials, or no randomised trials?”

    2021  Volume 1

    Keywords Medicine (General) ; R5-920
    Language English
    Publishing date 2021-12-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Impact Evaluation of Coronavirus Disease 2019 Policy: A Guide to Common Design Issues.

    Haber, Noah A / Clarke-Deelder, Emma / Salomon, Joshua A / Feller, Avi / Stuart, Elizabeth A

    American journal of epidemiology

    2021  Volume 190, Issue 11, Page(s) 2474–2486

    Abstract: Policy responses to coronavirus disease 2019 (COVID-19), particularly those related to nonpharmaceutical interventions, are unprecedented in scale and scope. However, evaluations of policy impacts require a complex combination of circumstance, study ... ...

    Abstract Policy responses to coronavirus disease 2019 (COVID-19), particularly those related to nonpharmaceutical interventions, are unprecedented in scale and scope. However, evaluations of policy impacts require a complex combination of circumstance, study design, data, statistics, and analysis. Beyond the issues that are faced for any policy, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and a multiplicity of interventions. The methods needed for policy-level impact evaluation are not often used or taught in epidemiology, and they differ in important ways that may not be obvious. Methodological complications of policy evaluations can make it difficult for decision-makers and researchers to synthesize and evaluate the strength of the evidence in COVID-19 health policy papers. Here we 1) introduce the basic suite of policy-impact evaluation designs for observational data, including cross-sectional analyses, pre-/post- analyses, interrupted time-series analysis, and difference-in-differences analysis; 2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19; and 3) provide decision-makers and reviewers with a conceptual and graphical guide to identifying these key violations. Our overall goal is to help epidemiologists, policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence.
    MeSH term(s) Bias ; COVID-19 ; Health Policy ; Humans ; Interrupted Time Series Analysis ; SARS-CoV-2
    Language English
    Publishing date 2021-06-28
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2937-3
    ISSN 1476-6256 ; 0002-9262
    ISSN (online) 1476-6256
    ISSN 0002-9262
    DOI 10.1093/aje/kwab185
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Un I Zs.310: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  7. Article ; Online: Much ado about something

    Noah A. Haber / Sarah E. Wieten / Emily R. Smith / David Nunan

    Trials, Vol 22, Iss 1, Pp 1-

    a response to “COVID-19: underpowered randomised trials, or no randomised trials?”

    2021  Volume 4

    Abstract: Abstract Non-pharmaceutical interventions (NPI) for infectious diseases such as COVID-19 are particularly challenging given the complexities of what is both practical and ethical to randomize. We are often faced with the difficult decision between having ...

    Abstract Abstract Non-pharmaceutical interventions (NPI) for infectious diseases such as COVID-19 are particularly challenging given the complexities of what is both practical and ethical to randomize. We are often faced with the difficult decision between having weak trials or not having a trial at all. In a recent article, Dr. Atle Fretheim argues that statistically underpowered studies are still valuable, particularly in conjunction with other similar studies in meta-analysis in the context of the DANMASK-19 trial, asking “Surely, some trial evidence must be better than no trial evidence?” However, informative trials are not always feasible, and feasible trials are not always informative. In some cases, even a well-conducted but weakly designed and/or underpowered trial such as DANMASK-19 may be uninformative or worse, both individually and in a body of literature. Meta-analysis, for example, can only resolve issues of statistical power if there is a reasonable expectation of compatible well-designed trials. Uninformative designs may also invite misinformation. Here, we make the case that—when considering informativeness, ethics, and opportunity costs in addition to statistical power—“nothing” is often the better choice.
    Keywords Non-pharmaceutical interventions ; Masks ; Ethics ; DANMASK-19 ; Statistical power ; Medicine (General) ; R5-920
    Subject code 170
    Language English
    Publishing date 2021-11-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  8. Article ; Online: Correction to: Malaria control across borders: quasi-experimental evidence from the Trans-Kunene malaria initiative (TKMI).

    Khadka, Aayush / Perales, Nicole A / Wei, Dorothy J / Gage, Anna D / Haber, Noah / Verguet, Stéphane / Patenaude, Bryan / Fink, Günther

    Malaria journal

    2019  Volume 18, Issue 1, Page(s) 365

    Abstract: Following publication of the original article [1], the authors flagged an error in Addition file 6. ...

    Abstract Following publication of the original article [1], the authors flagged an error in Addition file 6.
    Language English
    Publishing date 2019-11-14
    Publishing country England
    Document type Published Erratum
    ISSN 1475-2875
    ISSN (online) 1475-2875
    DOI 10.1186/s12936-019-2997-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Policy evaluation in COVID-19: A graphical guide to common design issues

    Haber, Noah A / Clarke-Deelder, Emma / Salomon, Joshua A / Feller, Avi / Stuart, Elizabeth A

    Abstract: Policy responses to COVID-19, particularly those related to non-pharmaceutical interventions, are unprecedented in scale and scope. Researchers and policymakers are striving to understand the impact of these policies on a variety of outcomes. Policy ... ...

    Abstract Policy responses to COVID-19, particularly those related to non-pharmaceutical interventions, are unprecedented in scale and scope. Researchers and policymakers are striving to understand the impact of these policies on a variety of outcomes. Policy impact evaluations always require a complex combination of circumstance, study design, data, statistics, and analysis. Beyond the issues that are faced for any policy, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and lags, lack of direct observation of key outcomes, and a multiplicity of interventions occurring on an accelerated time scale. In this paper, we (1) introduce the basic suite of policy impact evaluation designs for observational data, including cross-sectional analyses, pre/post, interrupted time-series, and difference-in-differences analysis, (2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19, and (3) provide decision-makers and reviewers a conceptual and graphical guide to identifying these key violations. The overall goal of this paper is to help policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence that is essential to decision-making.
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  10. Book ; Online: COVID-19 Policy Impact Evaluation

    Haber, Noah A / Clarke-Deelder, Emma / Salomon, Joshua A / Feller, Avi / Stuart, Elizabeth A

    A guide to common design issues

    2020  

    Abstract: Policy responses to COVID-19, particularly those related to non-pharmaceutical interventions, are unprecedented in scale and scope. Epidemiologists are more involved in policy decisions and evidence generation than ever before. However, policy impact ... ...

    Abstract Policy responses to COVID-19, particularly those related to non-pharmaceutical interventions, are unprecedented in scale and scope. Epidemiologists are more involved in policy decisions and evidence generation than ever before. However, policy impact evaluations always require a complex combination of circumstance, study design, data, statistics, and analysis. Beyond the issues that are faced for any policy, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and lags, lack of direct observation of key outcomes, and a multiplicity of interventions occurring on an accelerated time scale. The methods needed for policy-level impact evaluation are not often used or taught in epidemiology, and differ in important ways that may not be obvious. The volume and speed, and methodological complications of policy evaluations can make it difficult for decision-makers and researchers to synthesize and evaluate strength of evidence in COVID-19 health policy papers. In this paper, we (1) introduce the basic suite of policy impact evaluation designs for observational data, including cross-sectional analyses, pre/post, interrupted time-series, and difference-in-differences analysis, (2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19, and (3) provide decision-makers and reviewers a conceptual and graphical guide to identifying these key violations. The overall goal of this paper is to help epidemiologists, policy-makers, journal editors, journalists, researchers, and other research consumers understand and weigh the strengths and limitations of evidence that is essential to decision-making.
    Keywords Statistics - Methodology ; covid19
    Subject code 306 ; 360
    Publishing date 2020-09-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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