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  1. AU="Didelez, V"
  2. AU=Klimek Ludger
  3. AU="Van der Most, Peter J"
  4. AU="Talosig, A Rain"
  5. AU="Loveren, Henk Van"

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  1. Article ; Online: Invited Commentary: Where Do the Causal DAGs Come From?

    Didelez, Vanessa

    American journal of epidemiology

    2024  

    Abstract: How do we construct our causal DAGs, e.g. for life course modelling and analysis? In this commentary I review how the data-driven construction of causal DAGs (causal discovery) has evolved, what promises it holds and what limitations or caveats must be ... ...

    Abstract How do we construct our causal DAGs, e.g. for life course modelling and analysis? In this commentary I review how the data-driven construction of causal DAGs (causal discovery) has evolved, what promises it holds and what limitations or caveats must be considered. In conclusion I find that expert- or theory-driven model building might benefit from some more checking against the data and causal discovery could bring new ideas into old theories.
    Language English
    Publishing date 2024-04-03
    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/kwae028
    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:
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  2. Article: Epidemiology, genetic epidemiology and Mendelian randomisation: more need than ever to attend to detail

    Sheehan, Nuala / Didelez, Vanessa

    Human genetics Berlin, 139(1):121-136

    2019  

    Abstract: In the current era, with increasing availability of results from genetic association studies, finding genetic instruments for inferring causality in observational epidemiology has become apparently simple. Mendelian randomisation (MR) analyses are hence ... ...

    Institution Leibniz-Institut für Präventionsforschung und Epidemiologie
    Abstract In the current era, with increasing availability of results from genetic association studies, finding genetic instruments for inferring causality in observational epidemiology has become apparently simple. Mendelian randomisation (MR) analyses are hence growing in popularity and, in particular, methods that can incorporate multiple instruments are being rapidly developed for these applications. Such analyses have enormous potential, but they all rely on strong, different, and inherently untestable assumptions. These have to be clearly stated and carefully justified for every application in order to avoid conclusions that cannot be replicated. In this article, we review the instrumental variable assumptions and discuss the popular linear additive structural model. We advocate the use of tests for the null hypothesis of ‘no causal effect’ and calculation of the bounds for a causal effect, whenever possible, as these do not rely on parametric modelling assumptions. We clarify the difference between a randomised trial and an MR study and we comment on the importance of validating instruments, especially when considering them for joint use in an analysis. We urge researchers to stand by their convictions, if satisfied that the relevant assumptions hold, and to interpret their results causally since that is the only reason for performing an MR analysis in the first place.
    Keywords Genetic Variation ; Genome-Wide Association Study ; Humans ; Molecular Epidemiology ; Mendelian Randomization Analysis
    Language English
    Document type Article
    Database Repository for Life Sciences

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  3. Article: Constrained instruments and their application to Mendelian randomization with pleiotropy

    Jiang, Lai / Oualkacha, Karim / Didelez, Vanessa / Greenwood, Celia

    Genetic epidemiology, 43(4):373-401

    2019  

    Abstract: In Mendelian randomization (MR), inference about causal relationship between a phenotype of interest and a response or disease outcome can be obtained by constructing instrumental variables from genetic variants. However, MR inference requires three ... ...

    Institution Leibniz-Institut für Präventionsforschung und Epidemiologie
    Abstract In Mendelian randomization (MR), inference about causal relationship between a phenotype of interest and a response or disease outcome can be obtained by constructing instrumental variables from genetic variants. However, MR inference requires three assumptions, one of which is that the genetic variants only influence the outcome through phenotype of interest. Pleiotropy, that is, the situation in which some genetic variants affect more than one phenotype, can invalidate these genetic variants for use as instrumental variables; thus a naive analysis will give biased estimates of the causal relation. Here, we present new methods (constrained instrumental variable [CIV] methods) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists and when the pleiotropic phenotypes are available. We demonstrate that a smoothed version of CIV performs approximate selection of genetic variants that are valid instruments, and provides unbiased estimates of the causal effects. We provide details on a number of existing methods, together with a comparison of their performance in a large series of simulations. CIV performs robustly across different pleiotropic violations of the MR assumptions. We also analyzed the data from the Alzheimer’s disease (AD) neuroimaging initiative (ADNI; Mueller et al., 2005. Alzheimer's Dementia, 11(1), 55–66) to disentangle causal relationships of several biomarkers with AD progression.
    Keywords Instrumental variables ; Mendelian randomization ; Smoothed algorithm ; Pleiotropy
    Language English
    Document type Article
    Database Repository for Life Sciences

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  4. Conference proceedings: Inferring Time-Varying Treatment Effects from Observational Data: Lessons for Clinical Trials

    Didelez, Vanessa

    2021  , Page(s) Abstr. 189

    Event/congress 65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS); Berlin; Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie; 2020
    Keywords Medizin, Gesundheit ; causal inference
    Publishing date 2021-02-26
    Publisher German Medical Science GMS Publishing House; Düsseldorf
    Document type Conference proceedings
    DOI 10.3205/20gmds055
    Database German Medical Science

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  5. Article ; Online: Defining causal mediation with a longitudinal mediator and a survival outcome.

    Didelez, Vanessa

    Lifetime data analysis

    2018  Volume 25, Issue 4, Page(s) 593–610

    Abstract: In the context of causal mediation analysis, prevailing notions of direct and indirect effects are based on nested counterfactuals. These can be problematic regarding interpretation and identifiability especially when the mediator is a time-dependent ... ...

    Abstract In the context of causal mediation analysis, prevailing notions of direct and indirect effects are based on nested counterfactuals. These can be problematic regarding interpretation and identifiability especially when the mediator is a time-dependent process and the outcome is survival or, more generally, a time-to-event outcome. We propose and discuss an alternative definition of mediated effects that does not suffer from these problems, and is more transparent than the current alternatives. Our proposal is based on the extended graphical approach of Robins and Richardson (in: Shrout (ed) Causality and psychopathology: finding the determinants of disorders and their cures, Oxford University Press, Oxford, 2011), where treatment is decomposed into different components, or aspects, along different causal paths corresponding to real world mechanisms. This is an interesting alternative motivation for any causal mediation setting, but especially for survival outcomes. We give assumptions allowing identifiability of such alternative mediated effects leading to the familiar mediation g-formula (Robins in Math Model 7:1393, 1986); this implies that a number of available methods of estimation can be applied.
    MeSH term(s) Algorithms ; Causality ; Data Interpretation, Statistical ; Survival Analysis
    Language English
    Publishing date 2018-09-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1479719-7
    ISSN 1572-9249 ; 1380-7870
    ISSN (online) 1572-9249
    ISSN 1380-7870
    DOI 10.1007/s10985-018-9449-0
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  6. Book ; Online: Do we become wiser with time? On causal equivalence with tiered background knowledge

    Bang, Christine W. / Didelez, Vanessa

    2023  

    Abstract: Equivalence classes of DAGs (represented by CPDAGs) may be too large to provide useful causal information. Here, we address incorporating tiered background knowledge yielding restricted equivalence classes represented by 'tiered MPDAGs'. Tiered knowledge ...

    Abstract Equivalence classes of DAGs (represented by CPDAGs) may be too large to provide useful causal information. Here, we address incorporating tiered background knowledge yielding restricted equivalence classes represented by 'tiered MPDAGs'. Tiered knowledge leads to considerable gains in informativeness and computational efficiency: We show that construction of tiered MPDAGs only requires application of Meek's 1st rule, and that tiered MPDAGs (unlike general MPDAGs) are chain graphs with chordal components. This entails simplifications e.g. of determining valid adjustment sets for causal effect estimation. Further, we characterise when one tiered ordering is more informative than another, providing insights into useful aspects of background knowledge.

    Comment: Accepted for the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023)
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning ; Mathematics - Statistics Theory
    Publishing date 2023-06-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Re: Are Target Trial Emulations the Gold Standard for Observational Studies?

    Didelez, Vanessa / Haug, Ulrike / Garcia-Albeniz, Xabier

    Epidemiology (Cambridge, Mass.)

    2023  Volume 35, Issue 1, Page(s) e3

    Language English
    Publishing date 2023-11-27
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1053263-8
    ISSN 1531-5487 ; 1044-3983
    ISSN (online) 1531-5487
    ISSN 1044-3983
    DOI 10.1097/EDE.0000000000001667
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 3121: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  8. Article ; Online: Variable selection in linear regression models: Choosing the best subset is not always the best choice.

    Hanke, Moritz / Dijkstra, Louis / Foraita, Ronja / Didelez, Vanessa

    Biometrical journal. Biometrische Zeitschrift

    2023  Volume 66, Issue 1, Page(s) e2200209

    Abstract: We consider the question of variable selection in linear regressions, in the sense of identifying the correct direct predictors (those variables that have nonzero coefficients given all candidate predictors). Best subset selection (BSS) is often ... ...

    Abstract We consider the question of variable selection in linear regressions, in the sense of identifying the correct direct predictors (those variables that have nonzero coefficients given all candidate predictors). Best subset selection (BSS) is often considered the "gold standard," with its use being restricted only by its NP-hard nature. Alternatives such as the least absolute shrinkage and selection operator (Lasso) or the Elastic net (Enet) have become methods of choice in high-dimensional settings. A recent proposal represents BSS as a mixed-integer optimization problem so that large problems have become computationally feasible. We present an extensive neutral comparison assessing the ability to select the correct direct predictors of BSS compared to forward stepwise selection (FSS), Lasso, and Enet. The simulation considers a range of settings that are challenging regarding dimensionality (number of observations and variables), signal-to-noise ratios, and correlations between predictors. As fair measure of performance, we primarily used the best possible F1-score for each method, and results were confirmed by alternative performance measures and practical criteria for choosing the tuning parameters and subset sizes. Surprisingly, it was only in settings where the signal-to-noise ratio was high and the variables were uncorrelated that BSS reliably outperformed the other methods, even in low-dimensional settings. Furthermore, FSS performed almost identically to BSS. Our results shed new light on the usual presumption of BSS being, in principle, the best choice for selecting the correct direct predictors. Especially for correlated variables, alternatives like Enet are faster and appear to perform better in practical settings.
    MeSH term(s) Linear Models ; Computer Simulation
    Language English
    Publishing date 2023-08-29
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 131640-0
    ISSN 1521-4036 ; 0323-3847 ; 0006-3452
    ISSN (online) 1521-4036
    ISSN 0323-3847 ; 0006-3452
    DOI 10.1002/bimj.202200209
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    In stock of ZB MED Bonn / Germany

    Z 1267: Show issues

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  9. Article ; Online: Insights into the Cross-world Independence Assumption of Causal Mediation Analysis.

    Andrews, Ryan M / Didelez, Vanessa

    Epidemiology (Cambridge, Mass.)

    2021  Volume 32, Issue 2, Page(s) 209–219

    Abstract: Causal mediation analysis is a useful tool for epidemiologic research, but it has been criticized for relying on a "cross-world" independence assumption that counterfactual outcome and mediator values are independent even in causal worlds where the ... ...

    Abstract Causal mediation analysis is a useful tool for epidemiologic research, but it has been criticized for relying on a "cross-world" independence assumption that counterfactual outcome and mediator values are independent even in causal worlds where the exposure assignments for the outcome and mediator differ. This assumption is empirically difficult to verify and problematic to justify based on background knowledge. In the present article, we aim to assist the applied researcher in understanding this assumption. Synthesizing what is known about the cross-world independence assumption, we discuss the relationship between assumptions for causal mediation analyses, causal models, and nonparametric identification of natural direct and indirect effects. In particular, we give a practical example of an applied setting where the cross-world independence assumption is violated even without any post-treatment confounding. Further, we review possible alternatives to the cross-world independence assumption, including the use of bounds that avoid the assumption altogether. Finally, we carry out a numeric study in which the cross-world independence assumption is violated to assess the ensuing bias in estimating natural direct and indirect effects. We conclude with recommendations for carrying out causal mediation analyses.
    MeSH term(s) Bias ; Causality ; Humans ; Mediation Analysis ; Models, Statistical
    Language English
    Publishing date 2021-01-29
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 1053263-8
    ISSN 1531-5487 ; 1044-3983
    ISSN (online) 1531-5487
    ISSN 1044-3983
    DOI 10.1097/EDE.0000000000001313
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 3121: Show issues Location:
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  10. Article ; Online: On the logic of collapsibility for causal effect measures.

    Didelez, Vanessa / Stensrud, Mats Julius

    Biometrical journal. Biometrische Zeitschrift

    2021  Volume 64, Issue 2, Page(s) 235–242

    MeSH term(s) Logic
    Language English
    Publishing date 2021-02-12
    Publishing country Germany
    Document type Journal Article ; Comment
    ZDB-ID 131640-0
    ISSN 1521-4036 ; 0323-3847 ; 0006-3452
    ISSN (online) 1521-4036
    ISSN 0323-3847 ; 0006-3452
    DOI 10.1002/bimj.202000305
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    In stock of ZB MED Bonn / Germany

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