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  1. Article: Ground-dwelling arthropod response to fire and clearcutting in jack pine: implications for ecosystem management

    Venier, L.A / T.T. Work / J. Klimaszewski / D.M. Morris / J.J. Bowden / M.M. Kwiaton / K. Webster / P. Hazlett

    Canadian journal of forest research =. 2017 Sept. 28, v. 47, no. 12

    2017  

    Abstract: ... central Ontario (47°42′N, 83°36′W) in jack pine (Pinus banksiana Lamb.) dominated stands in 2013 ...

    Abstract We tested the response of species composition of three dominant litter-dwelling arthropod taxa (carabid beetles, spiders, and rove beetles) to wildfire and harvest. This study was conducted in north-central Ontario (47°42′N, 83°36′W) in jack pine (Pinus banksiana Lamb.) dominated stands in 2013 using pitfall trapping. Using 222 species (12 015 individuals), we compared the effects of disturbance using recently burned (3 years since fire) and clearcut sites (3 years since harvest; tree length, full tree, stump removal, and blading), as well as older, closed-canopy stands that have regenerated following clearcutting (51 years since harvest) and fire (92 years since fire), with multivariate regression trees. Taxa were more similar in the three controls (including recent fire) than between controls and harvest treatments, with increased forest floor disturbance in harvested plots being a likely explanation. In addition, taxa were different in the younger (51 years) harvest-origin plots than in the older (92 years) fire-origin plots, suggesting that communities had not yet recovered from the harvest disturbance possibly due to insufficient coarse woody debris in the younger stand. These results indicate that forest management practices that match natural forest floor disturbance could ameliorate short-term effects, whereas the maintenance of more coarse woody debris could reduce the recovery time of epigaeic communities.
    Keywords Araneae ; Carabidae ; Pinus banksiana ; Staphylinidae ; clearcutting ; coarse woody debris ; ecosystem management ; forest litter ; forest management ; pitfall traps ; soil arthropods ; species diversity ; stump extraction ; trees ; wildfires ; Ontario
    Language English
    Dates of publication 2017-0928
    Size p. 1614-1631.
    Publishing place NRC Research Press
    Document type Article
    ZDB-ID 1473096-0
    ISSN 1208-6037 ; 0045-5067
    ISSN (online) 1208-6037
    ISSN 0045-5067
    DOI 10.1139/cjfr-2017-0145
    Database NAL-Catalogue (AGRICOLA)

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  2. Book: Jack Nicklaus' Playing Lessons

    Nicklaus, Jack / Bowden, Ken

    1981  

    Author's details by Jack Nicklaus with Ken Bowden
    Size 143 Seiten: zahlreiche Illustrationen
    Publisher Golf Digest / Tennis; Norwalk, Conn.
    Document type Book
    HBZ-ID HT017613931
    ISBN 0-914178-42-3 ; 978-0-914178-42-2
    Database Central Library of Sport Science of the German Sport University Cologne

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  3. Article ; Online: Realising the full potential of MR-PHeWAS in cancer.

    Bowden, Jack

    British journal of cancer

    2020  Volume 124, Issue 3, Page(s) 529–530

    Abstract: MR-PHeWAS is a powerful new design for discovering causal mechanisms between a disease and its many candidate risk factors in a hypothesis-free manner. This technique has great potential in the field of cancer research, provided that both powerful and ... ...

    Abstract MR-PHeWAS is a powerful new design for discovering causal mechanisms between a disease and its many candidate risk factors in a hypothesis-free manner. This technique has great potential in the field of cancer research, provided that both powerful and principled statistical approaches are used.
    MeSH term(s) Glioma ; Humans ; Risk Factors
    Language English
    Publishing date 2020-11-25
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 80075-2
    ISSN 1532-1827 ; 0007-0920
    ISSN (online) 1532-1827
    ISSN 0007-0920
    DOI 10.1038/s41416-020-01165-0
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  4. Conference proceedings: Connecting Instrumental Variable methods for causal inference to the Estimand Framework

    Bowden, Jack

    2021  , Page(s) Abstr. 502

    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
    Publishing date 2021-02-26
    Publisher German Medical Science GMS Publishing House; Düsseldorf
    Document type Conference proceedings
    DOI 10.3205/20gmds057
    Database German Medical Science

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  5. Article ; Online: Misconceptions on the use of MR-Egger regression and the evaluation of the InSIDE assumption.

    Bowden, Jack

    International journal of epidemiology

    2017  Volume 46, Issue 6, Page(s) 2097–2099

    MeSH term(s) Genetic Pleiotropy ; Mendelian Randomization Analysis
    Language English
    Publishing date 2017-11-17
    Publishing country England
    Document type Letter ; Comment
    ZDB-ID 187909-1
    ISSN 1464-3685 ; 0300-5771
    ISSN (online) 1464-3685
    ISSN 0300-5771
    DOI 10.1093/ije/dyx192
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  6. Article ; Online: Sparse dimensionality reduction approaches in Mendelian randomisation with highly correlated exposures.

    Karageorgiou, Vasileios / Gill, Dipender / Bowden, Jack / Zuber, Verena

    eLife

    2023  Volume 12

    Abstract: Multivariable Mendelian randomisation (MVMR) is an instrumental variable technique that generalises the MR framework for multiple exposures. Framed as a regression problem, it is subject to the pitfall of multicollinearity. The bias and efficiency of ... ...

    Abstract Multivariable Mendelian randomisation (MVMR) is an instrumental variable technique that generalises the MR framework for multiple exposures. Framed as a regression problem, it is subject to the pitfall of multicollinearity. The bias and efficiency of MVMR estimates thus depends heavily on the correlation of exposures. Dimensionality reduction techniques such as principal component analysis (PCA) provide transformations of all the included variables that are effectively uncorrelated. We propose the use of sparse PCA (sPCA) algorithms that create principal components of subsets of the exposures with the aim of providing more interpretable and reliable MR estimates. The approach consists of three steps. We first apply a sparse dimension reduction method and transform the variant-exposure summary statistics to principal components. We then choose a subset of the principal components based on data-driven cutoffs, and estimate their strength as instruments with an adjusted
    MeSH term(s) Humans ; Genome-Wide Association Study ; Mendelian Randomization Analysis/methods ; Coronary Disease ; Causality ; Lipids
    Chemical Substances Lipids
    Language English
    Publishing date 2023-04-19
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.80063
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Validity of the jack-knife technique for analysing enzyme kinetic data.

    Cornish-Bowden, A / Wong, J T

    The Biochemical journal

    1980  Volume 185, Issue 2, Page(s) 535–536

    Abstract: ... by the jack-knife technique [Cornish-Bowden & Wong (1978) Biochem. J. 175, 969--976] are sometimes outside ...

    Abstract An observation by Duggleby [Biochem. J. (1979) 181, 255-256] that estimates of kinetic parameters by the jack-knife technique [Cornish-Bowden & Wong (1978) Biochem. J. 175, 969--976] are sometimes outside the range of estimates from which they are calculated has been examined. No significant correlation has been found between the occurrence of this behaviour and the actual quality of the estimates.
    MeSH term(s) Enzymes ; Kinetics ; Methods
    Chemical Substances Enzymes
    Language English
    Publishing date 1980-02-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 2969-5
    ISSN 1470-8728 ; 0264-6021 ; 0006-2936 ; 0306-3275
    ISSN (online) 1470-8728
    ISSN 0264-6021 ; 0006-2936 ; 0306-3275
    DOI 10.1042/bj1850535
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  8. Article ; Online: Sparse dimensionality reduction approaches in Mendelian randomisation with highly correlated exposures

    Vasileios Karageorgiou / Dipender Gill / Jack Bowden / Verena Zuber

    eLife, Vol

    2023  Volume 12

    Abstract: Multivariable Mendelian randomisation (MVMR) is an instrumental variable technique that generalises the MR framework for multiple exposures. Framed as a regression problem, it is subject to the pitfall of multicollinearity. The bias and efficiency of ... ...

    Abstract Multivariable Mendelian randomisation (MVMR) is an instrumental variable technique that generalises the MR framework for multiple exposures. Framed as a regression problem, it is subject to the pitfall of multicollinearity. The bias and efficiency of MVMR estimates thus depends heavily on the correlation of exposures. Dimensionality reduction techniques such as principal component analysis (PCA) provide transformations of all the included variables that are effectively uncorrelated. We propose the use of sparse PCA (sPCA) algorithms that create principal components of subsets of the exposures with the aim of providing more interpretable and reliable MR estimates. The approach consists of three steps. We first apply a sparse dimension reduction method and transform the variant-exposure summary statistics to principal components. We then choose a subset of the principal components based on data-driven cutoffs, and estimate their strength as instruments with an adjusted F-statistic. Finally, we perform MR with these transformed exposures. This pipeline is demonstrated in a simulation study of highly correlated exposures and an applied example using summary data from a genome-wide association study of 97 highly correlated lipid metabolites. As a positive control, we tested the causal associations of the transformed exposures on coronary heart disease (CHD). Compared to the conventional inverse-variance weighted MVMR method and a weak instrument robust MVMR method (MR GRAPPLE), sparse component analysis achieved a superior balance of sparsity and biologically insightful grouping of the lipid traits.
    Keywords Mendelian randomisation ; principal component analysis ; causal inference ; coronary heart disease ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
    Subject code 310
    Language English
    Publishing date 2023-04-01T00:00:00Z
    Publisher eLife Sciences Publications Ltd
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy.

    Cardoso, Pedro / Dennis, John M / Bowden, Jack / Shields, Beverley M / McKinley, Trevelyan J

    BMC medical informatics and decision making

    2024  Volume 24, Issue 1, Page(s) 12

    Abstract: Background: The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions from models for use in clinical ... ...

    Abstract Background: The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions from models for use in clinical practice.
    Methods: We utilise a flexible Bayesian approach for handling missing predictor information in regression models. This provides practitioners with full posterior predictive distributions for both the missing predictor information (conditional on the observed predictors) and the outcome-of-interest. We apply this approach to a previously proposed counterfactual treatment selection model for type 2 diabetes second-line therapies. Our approach combines a regression model and a Dirichlet process mixture model (DPMM), where the former defines the treatment selection model, and the latter provides a flexible way to model the joint distribution of the predictors.
    Results: We show that DPMMs can model complex relationships between predictor variables and can provide powerful means of fitting models to incomplete data (under missing-completely-at-random and missing-at-random assumptions). This framework ensures that the posterior distribution for the parameters and the conditional average treatment effect estimates automatically reflect the additional uncertainties associated with missing data due to the hierarchical model structure. We also demonstrate that in the presence of multiple missing predictors, the DPMM model can be used to explore which variable(s), if collected, could provide the most additional information about the likely outcome.
    Conclusions: When developing clinical prediction models, DPMMs offer a flexible way to model complex covariate structures and handle missing predictor information. DPMM-based counterfactual prediction models can also provide additional information to support clinical decision-making, including allowing predictions with appropriate uncertainty to be made for individuals with incomplete predictor data.
    MeSH term(s) Humans ; Bayes Theorem ; Diabetes Mellitus, Type 2/drug therapy ; Clinical Decision-Making ; Uncertainty
    Language English
    Publishing date 2024-01-08
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2046490-3
    ISSN 1472-6947 ; 1472-6947
    ISSN (online) 1472-6947
    ISSN 1472-6947
    DOI 10.1186/s12911-023-02400-3
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  10. Article ; Online: Weak and pleiotropy robust sex-stratified Mendelian randomization in the one sample and two sample settings.

    Karageorgiou, Vasilios / Tyrrell, Jess / Mckinley, Trevelyan J / Bowden, Jack

    Genetic epidemiology

    2023  Volume 47, Issue 2, Page(s) 135–151

    Abstract: Background: Mendelian randomization (MR) leverages genetic data as an instrumental variable to provide estimates for the causal effect of an exposure X on a health outcome Y that is robust to confounding. Unfortunately, horizontal pleiotropy-the direct ... ...

    Abstract Background: Mendelian randomization (MR) leverages genetic data as an instrumental variable to provide estimates for the causal effect of an exposure X on a health outcome Y that is robust to confounding. Unfortunately, horizontal pleiotropy-the direct association of a genetic variant with multiple phenotypes-is highly prevalent and can easily render a genetic variant an invalid instrument.
    Methods: Building on existing work, we propose a simple method for leveraging sex-specific genetic associations to perform weak and pleiotropy-robust MR analysis. This is achieved by constructing an MR estimator in which pleiotropy is perfectly removed by cancellation, while placing it within the powerful machinery of the robust adjusted profile score (MR-RAPS) method. Pleiotropy cancellation has the attractive property that it removes heterogeneity and therefore justifies a statistically efficient fixed effects model. We extend the method from the typical two-sample summary-data MR setting to the one-sample setting by adapting the technique of Collider-Correction. Simulation studies and applied examples are used to assess how the sex-stratified MR-RAPS estimator performs against other common approaches.
    Results: The sex-stratified MR-RAPS method is shown to be robust to pleiotropy even in cases where all genetic variants violated the standard Instrument Strength Independent of Direct Effect assumption. In some cases where the strength of the pleiotropic effect additionally varied by sex (and so perfect cancellation was not achieved), over-dispersed MR-RAPS implementations can still consistently estimate the true causal effect. In applied analyses, we investigate the causal effect of waist-hip ratio (WHR), an important marker of central obesity, on a range of downstream traits. While the conventional approaches suggested paradoxical links between WHR and height and body mass index, the sex-stratified approach obtained a more realistic null effect. Nonzero effects were also detected for systolic and diastolic blood pressure as well as high-density and low-density lipoprotein cholesterol.
    Discussion: We provide a simple but attractive method for weak and pleiotropy robust causal estimation of sexually dimorphic traits on downstream outcomes, by combining several existing approaches in a novel fashion.
    MeSH term(s) Humans ; Mendelian Randomization Analysis/methods ; Models, Genetic ; Genetic Pleiotropy ; Genetic Variation ; Causality ; Genome-Wide Association Study
    Language English
    Publishing date 2023-01-22
    Publishing country United States
    Document type Journal Article
    ZDB-ID 605785-8
    ISSN 1098-2272 ; 0741-0395
    ISSN (online) 1098-2272
    ISSN 0741-0395
    DOI 10.1002/gepi.22512
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