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  1. Article ; Online: Evaluating the Duration of Response With Mirvetuximab Soravtansine for Treating Platinum-Resistant Ovarian Cancer.

    McCaw, Zachary R / Tian, Lu / Wei, Lee-Jen

    Journal of clinical oncology : official journal of the American Society of Clinical Oncology

    2023  Volume 41, Issue 29, Page(s) 4704

    Language English
    Publishing date 2023-08-03
    Publishing country United States
    Document type Letter
    ZDB-ID 604914-x
    ISSN 1527-7755 ; 0732-183X
    ISSN (online) 1527-7755
    ISSN 0732-183X
    DOI 10.1200/JCO.23.00288
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1847: Show issues Location:
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  2. Article ; Online: Correction: Fitting Gaussian mixture models on incomplete data.

    McCaw, Zachary R / Aschard, Hugues / Julienne, Hanna

    BMC bioinformatics

    2022  Volume 23, Issue 1, Page(s) 255

    Language English
    Publishing date 2022-06-27
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-022-04808-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Assessing the Ability of Long Noncoding RNA Expression to Predict Patient Outcomes in Pediatric AML.

    McCaw, Zachary R / Richardson, Paul G / Wei, Lee-Jen

    Journal of clinical oncology : official journal of the American Society of Clinical Oncology

    2023  Volume 41, Issue 27, Page(s) 4446–4447

    MeSH term(s) Child ; Humans ; RNA, Long Noncoding/genetics ; RNA, Long Noncoding/metabolism ; MicroRNAs/genetics ; Leukemia, Myeloid, Acute/genetics ; Leukemia, Myeloid, Acute/metabolism
    Chemical Substances RNA, Long Noncoding ; MicroRNAs
    Language English
    Publishing date 2023-06-30
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 604914-x
    ISSN 1527-7755 ; 0732-183X
    ISSN (online) 1527-7755
    ISSN 0732-183X
    DOI 10.1200/JCO.23.00465
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1847: Show issues Location:
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  4. Article ; Online: Clinical Utility Assessment of Gonadotropin-Releasing Hormone Analogs Among Women Younger Than 35 Years.

    McCaw, Zachary R / Wei, Lee-Jen

    JAMA oncology

    2022  Volume 8, Issue 6, Page(s) 1

    MeSH term(s) Female ; Gonadotropin-Releasing Hormone ; Humans
    Chemical Substances Gonadotropin-Releasing Hormone (33515-09-2)
    Language English
    Publishing date 2022-04-14
    Publishing country United States
    Document type Journal Article ; Comment
    ISSN 2374-2445
    ISSN (online) 2374-2445
    DOI 10.1001/jamaoncol.2022.0488
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Fitting Gaussian mixture models on incomplete data.

    McCaw, Zachary R / Aschard, Hugues / Julienne, Hanna

    BMC bioinformatics

    2022  Volume 23, Issue 1, Page(s) 208

    Abstract: ... Results: Here we present missingness-aware Gaussian mixture models (MGMM), an R package for fitting GMMs ... publicly available as an R package on CRAN: https://CRAN.R-project.org/package=MGMM . ...

    Abstract Background: Bioinformatics investigators often gain insights by combining information across multiple and disparate data sets. Merging data from multiple sources frequently results in data sets that are incomplete or contain missing values. Although missing data are ubiquitous, existing implementations of Gaussian mixture models (GMMs) either cannot accommodate missing data, or do so by imposing simplifying assumptions that limit the applicability of the model. In the presence of missing data, a standard ad hoc practice is to perform complete case analysis or imputation prior to model fitting. Both approaches have serious drawbacks, potentially resulting in biased and unstable parameter estimates.
    Results: Here we present missingness-aware Gaussian mixture models (MGMM), an R package for fitting GMMs in the presence of missing data. Unlike existing GMM implementations that can accommodate missing data, MGMM places no restrictions on the form of the covariance matrix. Using three case studies on real and simulated 'omics data sets, we demonstrate that, when the underlying data distribution is near-to a GMM, MGMM is more effective at recovering the true cluster assignments than either the existing GMM implementations that accommodate missing data, or fitting a standard GMM after state of the art imputation. Moreover, MGMM provides an accurate assessment of cluster assignment uncertainty, even when the generative distribution is not a GMM.
    Conclusion: Compared to state-of-the-art competitors, MGMM demonstrates a better ability to recover the true cluster assignments for a wide variety of data sets and a large range of missingness rates. MGMM provides the bioinformatics community with a powerful, easy-to-use, and statistically sound tool for performing clustering and density estimation in the presence of missing data. MGMM is publicly available as an R package on CRAN: https://CRAN.R-project.org/package=MGMM .
    MeSH term(s) Cluster Analysis ; Computational Biology/methods ; Normal Distribution
    Language English
    Publishing date 2022-06-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-022-04740-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Correction

    Zachary R. McCaw / Hugues Aschard / Hanna Julienne

    BMC Bioinformatics, Vol 23, Iss 1, Pp 1-

    Fitting Gaussian mixture models on incomplete data

    2022  Volume 1

    Keywords Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
    Language English
    Publishing date 2022-06-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Questions About a Risk Prediction Model of Mortality After Esophagectomy for Cancer.

    McCaw, Zachary R / Wei, Lee-Jen

    JAMA surgery

    2021  Volume 157, Issue 3, Page(s) 279–280

    MeSH term(s) Esophageal Neoplasms/surgery ; Esophagectomy ; Humans ; Models, Statistical
    Language English
    Publishing date 2021-11-03
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 2701841-6
    ISSN 2168-6262 ; 2168-6254
    ISSN (online) 2168-6262
    ISSN 2168-6254
    DOI 10.1001/jamasurg.2021.5701
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Uk I Zs.72: Show issues Location:
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  8. Article ; Online: Quantifying the Effect of Lower vs Higher Positive End-Expiratory Pressure on Ventilator-Free Survival in ICU Patients.

    McCaw, Zachary R / Tian, Lu / Wei, Lee-Jen

    JAMA

    2021  Volume 325, Issue 15, Page(s) 1566–1567

    MeSH term(s) Humans ; Intensive Care Units ; Positive-Pressure Respiration ; Respiratory Distress Syndrome ; Ventilators, Mechanical
    Language English
    Publishing date 2021-04-20
    Publishing country United States
    Document type Letter ; 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.2021.1700
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Ua VI Zs.88: Show issues Location:
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  9. Article ; Online: Assessing the Clinical Utility of Oral Paclitaxel Plus Encequidar Versus Intravenous Paclitaxel in Patients With Metastatic Breast Cancer.

    McCaw, Zachary R / Ludmir, Ethan B / Wei, Lee-Jen

    Journal of clinical oncology : official journal of the American Society of Clinical Oncology

    2022  Volume 41, Issue 6, Page(s) 1323

    MeSH term(s) Humans ; Female ; Breast Neoplasms/drug therapy ; Breast Neoplasms/pathology ; Paclitaxel/adverse effects ; Treatment Outcome ; Disease-Free Survival ; Antineoplastic Combined Chemotherapy Protocols/adverse effects
    Chemical Substances Paclitaxel (P88XT4IS4D)
    Language English
    Publishing date 2022-11-03
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 604914-x
    ISSN 1527-7755 ; 0732-183X
    ISSN (online) 1527-7755
    ISSN 0732-183X
    DOI 10.1200/JCO.22.01759
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1847: Show issues Location:
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  10. Article ; Online: Leveraging a surrogate outcome to improve inference on a partially missing target outcome

    McCaw, Zachary R. / Gaynor, Sheila M. / Sun, Ryan / Lin, Xihong

    Biometrics. 2023 June, v. 79, no. 2 p.1472-1484

    2023  

    Abstract: Sample sizes vary substantially across tissues in the Genotype‐Tissue Expression (GTEx) project, where considerably fewer samples are available from certain inaccessible tissues, such as the substantia nigra (SSN), than from accessible tissues, such as ... ...

    Abstract Sample sizes vary substantially across tissues in the Genotype‐Tissue Expression (GTEx) project, where considerably fewer samples are available from certain inaccessible tissues, such as the substantia nigra (SSN), than from accessible tissues, such as blood. This severely limits power for identifying tissue‐specific expression quantitative trait loci (eQTL) in undersampled tissues. Here we propose Surrogate Phenotype Regression Analysis (Spray) for leveraging information from a correlated surrogate outcome (eg, expression in blood) to improve inference on a partially missing target outcome (eg, expression in SSN). Rather than regarding the surrogate outcome as a proxy for the target outcome, Spray jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. We describe and implement an expectation conditional maximization algorithm for performing estimation in the presence of bilateral outcome missingness. Spray estimates the same association parameter estimated by standard eQTL mapping and controls the type I error even when the target and surrogate outcomes are truly uncorrelated. We demonstrate analytically and empirically, using simulations and GTEx data, that in comparison with marginally modeling the target outcome, jointly modeling the target and surrogate outcomes increases estimation precision and improves power.
    Keywords algorithms ; blood ; phenotype ; quantitative traits ; regression analysis
    Language English
    Dates of publication 2023-06
    Size p. 1472-1484.
    Publishing place John Wiley & Sons, Ltd
    Document type Article ; Online
    Note JOURNAL ARTICLE
    ZDB-ID 213543-7
    ISSN 0099-4987 ; 0006-341X
    ISSN 0099-4987 ; 0006-341X
    DOI 10.1111/biom.13629
    Database NAL-Catalogue (AGRICOLA)

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