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Zhang M, Xiao OY, Lim J, Wang X. Goodness-of-fit testing for meta-analysis of rare binary events. Sci Rep 2023; 13:17712. [PMID: 37853012 PMCID: PMC10584850 DOI: 10.1038/s41598-023-44638-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/10/2023] [Indexed: 10/20/2023] Open
Abstract
Random-effects (RE) meta-analysis is a crucial approach for combining results from multiple independent studies that exhibit heterogeneity. Recently, two frequentist goodness-of-fit (GOF) tests were proposed to assess the fit of RE model. However, they tend to perform poorly when assessing rare binary events. Under a general binomial-normal framework, we propose a novel GOF test for the meta-analysis of rare events. Our method is based on pivotal quantities that play an important role in Bayesian model assessment. It further adopts the Cauchy combination idea proposed in a 2019 JASA paper, to combine dependent p-values computed using posterior samples from Markov Chain Monte Carlo. The advantages of our method include clear conception and interpretation, incorporation of all data including double zeros without the need for artificial correction, well-controlled Type I error, and generally improved ability in detecting model misfits compared to previous GOF methods. We illustrate the proposed method via simulation and three real data applications.
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Affiliation(s)
- Ming Zhang
- Department of Statistics and Data Science, Southern Methodist University, Dallas, Texas, 75205, USA
| | - Olivia Y Xiao
- Highland Park High School, Dallas, Texas, 75205, USA
| | - Johan Lim
- Department of Statistics, Seoul National University, Seoul, 08826, Korea
| | - Xinlei Wang
- Department of Statistics and Data Science, Southern Methodist University, Dallas, Texas, 75205, USA.
- Department of Mathematics, University of Texas at Arlington, Arlington, Texas, 76019, USA.
- Center for Data Science Research and Education, College of Science, University of Texas at Arlington, Arlington, Texas, 76019, USA.
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Hogg SE, Wang Y, Stone L. Effectiveness of joint species distribution models in the presence of imperfect detection. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | - Yan Wang
- Mathematics School of Science RMIT Melbourne Australia
| | - Lewi Stone
- Mathematics School of Science RMIT Melbourne Australia
- Biomathematics Unit School of Zoology Faculty of Life Science Tel Aviv University Tel Aviv Israel
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Burger DA, Schall R, Ferreira JT, Chen D. A robust Bayesian mixed effects approach for zero inflated and highly skewed longitudinal count data emanating from the zero inflated discrete Weibull distribution. Stat Med 2020; 39:1275-1291. [DOI: 10.1002/sim.8475] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 12/18/2019] [Accepted: 12/19/2019] [Indexed: 11/09/2022]
Affiliation(s)
| | - Robert Schall
- Department of Mathematical Statistics and Actuarial ScienceUniversity of the Free State Bloemfontein South Africa
| | | | - Ding‐Geng Chen
- Department of StatisticsUniversity of Pretoria Pretoria South Africa
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Ariyo O, Lesaffre E, Verbeke G, Quintero A. Model selection for Bayesian linear mixed models with longitudinal data: Sensitivity to the choice of priors. COMMUN STAT-SIMUL C 2019. [DOI: 10.1080/03610918.2019.1676439] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Oludare Ariyo
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), KU Leuven, Leuven, Belgium
- Department of Statistics, Federal University of Agriculture, Abeokuta, Nigeria
| | - Emmanuel Lesaffre
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), KU Leuven, Leuven, Belgium
| | - Geert Verbeke
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), KU Leuven, Leuven, Belgium
| | - Adrian Quintero
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), KU Leuven, Leuven, Belgium
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Mazumdar M, Moshier EL, Özbek U, Parsons R. Ten Essential Practices for Developing or Reforming a Biostatistics Core for a NCI Designated Cancer Center. JNCI Cancer Spectr 2018; 2:pky010. [PMID: 31360841 PMCID: PMC6649702 DOI: 10.1093/jncics/pky010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 02/11/2018] [Accepted: 03/06/2018] [Indexed: 01/17/2023] Open
Abstract
There are 69 National Cancer Institute (NCI) designated Cancer Centers (CCs) in the United States. Biostatistical collaboration is pivotal in cancer research, and support for a cancer biostatistics shared resource facility (C-BSRF) is included in the award. Although the services and staff needed in a C-BSRF have been outlined in general terms and best practices for biostatistical consultations and collaboration in an academic health center have been agreed upon, implementing these practices in the demanding setting of cancer centers interested in pursuing or maintaining NCI designation remains challenging. We surveyed all C-BSRF websites to assess their organizational charts, governance, size, services provided, and financial models and have identified 10 essential practices for the development of a successful C-BSRF. Here, we share our success with, and barriers to, implementation of these practices. Showcasing development plans for these essential practices resulted in an NCI score of "Excellent to Outstanding" for our C-BSRF in 2015, and performance metrics in 2016-2017 demonstrated notable improvement since our original Cancer Center Support Grant (CCSG) application in 2014. We believe that the essential practices described here can be adapted and adjusted, as needed, for CCs of various sizes and with different types of cancer research programs.
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Affiliation(s)
- Madhu Mazumdar
- Institute for Healthcare Delivery Science, Mount Sinai Health System, New York, NY
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY
- Biostatistics Shared Resource Facility, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Erin L Moshier
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY
- Biostatistics Shared Resource Facility, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Umut Özbek
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY
- Biostatistics Shared Resource Facility, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ramon Parsons
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
- Medicine, Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY
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