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  1. Article ; Online: On the point estimate from the common-effect meta-analysis model.

    Wang, Yipeng / Lin, Lifeng

    Journal of clinical epidemiology

    2024  , Page(s) 111363

    Language English
    Publishing date 2024-04-12
    Publishing country United States
    Document type Letter
    ZDB-ID 639306-8
    ISSN 1878-5921 ; 0895-4356
    ISSN (online) 1878-5921
    ISSN 0895-4356
    DOI 10.1016/j.jclinepi.2024.111363
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  2. Article ; Online: Comparisons of the mean differences and standardized mean differences for continuous outcome measures on the same scale.

    Jing, Yaqi / Lin, Lifeng

    JBI evidence synthesis

    2024  Volume 22, Issue 3, Page(s) 394–405

    Abstract: When conducting systematic reviews and meta-analyses of continuous outcomes, the mean differences (MDs) and standardized mean differences (SMDs) are 2 commonly used choices for effect measures. The SMDs are motivated by scenarios where studies collected ... ...

    Abstract When conducting systematic reviews and meta-analyses of continuous outcomes, the mean differences (MDs) and standardized mean differences (SMDs) are 2 commonly used choices for effect measures. The SMDs are motivated by scenarios where studies collected in a systematic review do not report the continuous measures on the same scale. The standardization process transfers the MDs to be unit-free measures that can be synthesized across studies. As such, some evidence synthesis researchers tend to prefer the SMD over the MD. However, other researchers have concerns about the interpretability of the SMD. The standardization process could also yield additional heterogeneity between studies. In this paper, we use simulation studies to illustrate that, in a scenario where the continuous measures are on the same scale, the SMD could have considerably poorer performance compared with the MD in some cases. The simulations compare the MD and SMD in various settings, including cases where the normality assumption of continuous measures does not hold. We conclude that although the SMD remains useful for evidence synthesis of continuous measures on different scales, the SMD could have substantially greater biases, greater mean squared errors, and lower coverage probabilities of CIs than the MD. The MD is generally more robust to the violation of the normality assumption for continuous measures. In scenarios where continuous measures are inherently comparable or can be transformed to a common scale, the MD is the preferred choice for an effect measure.
    MeSH term(s) Humans ; Outcome Assessment, Health Care ; Bias
    Language English
    Publishing date 2024-03-01
    Publishing country United States
    Document type Systematic Review ; Journal Article
    ISSN 2689-8381
    ISSN (online) 2689-8381
    DOI 10.11124/JBIES-23-00368
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  3. Article ; Online: Hybrid test for publication bias in meta-analysis.

    Lin, Lifeng

    Statistical methods in medical research

    2020  Volume 29, Issue 10, Page(s) 2881–2899

    Abstract: Publication bias frequently appears in meta-analyses when the included studies' results (e.g., ...

    Abstract Publication bias frequently appears in meta-analyses when the included studies' results (e.g.,
    MeSH term(s) Bias ; Computer Simulation ; Publication Bias ; Research Design
    Language English
    Publishing date 2020-04-15
    Publishing country England
    Document type Journal Article ; Meta-Analysis ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1136948-6
    ISSN 1477-0334 ; 0962-2802
    ISSN (online) 1477-0334
    ISSN 0962-2802
    DOI 10.1177/0962280220910172
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  4. Article ; Online: Comparisons of various estimates of the

    Wang, Yipeng / DelRocco, Natalie / Lin, Lifeng

    Statistical methods in medical research

    2024  Volume 33, Issue 5, Page(s) 745–764

    Abstract: Assessing heterogeneity between studies is a critical step in determining whether studies can be combined and whether the synthesized results are reliable. ... ...

    Abstract Assessing heterogeneity between studies is a critical step in determining whether studies can be combined and whether the synthesized results are reliable. The
    MeSH term(s) Humans ; Meta-Analysis as Topic ; Models, Statistical ; Computer Simulation ; Data Interpretation, Statistical
    Language English
    Publishing date 2024-03-19
    Publishing country England
    Document type Journal Article ; Comparative Study ; Research Support, Non-U.S. Gov't
    ZDB-ID 1136948-6
    ISSN 1477-0334 ; 0962-2802
    ISSN (online) 1477-0334
    ISSN 0962-2802
    DOI 10.1177/09622802241231496
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  5. Article ; Online: Use of Prediction Intervals in Network Meta-analysis.

    Lin, Lifeng

    JAMA network open

    2019  Volume 2, Issue 8, Page(s) e199735

    MeSH term(s) Confidence Intervals ; Data Interpretation, Statistical ; Humans ; Network Meta-Analysis
    Language English
    Publishing date 2019-08-02
    Publishing country United States
    Document type Letter ; Research Support, Non-U.S. Gov't
    ISSN 2574-3805
    ISSN (online) 2574-3805
    DOI 10.1001/jamanetworkopen.2019.9735
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  6. Article ; Online: Construction of a prognostic model based on genome-wide methylation analysis of miRNAs for hepatocellular carcinoma.

    Shi, Zhaoqi / Liu, Xiaolong / Li, Duguang / Fan, Xiaoxiao / He, Lifeng / Zhou, Daizhan / Lin, Hui

    Epigenomics

    2024  

    Abstract: Aim: ...

    Abstract Aim:
    Language English
    Publishing date 2024-03-13
    Publishing country England
    Document type Journal Article
    ZDB-ID 2537199-X
    ISSN 1750-192X ; 1750-1911
    ISSN (online) 1750-192X
    ISSN 1750-1911
    DOI 10.2217/epi-2023-0365
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  7. Article ; Online: Factors that impact fragility index and their visualizations.

    Lin, Lifeng

    Journal of evaluation in clinical practice

    2020  Volume 27, Issue 2, Page(s) 356–364

    Abstract: Rationale aims and objectives: As the recent literature has growing concerns about research replicability and the misuse and misconception of P-values, the fragility index (FI) has been an attractive measure to assess the robustness (or fragility) of ... ...

    Abstract Rationale aims and objectives: As the recent literature has growing concerns about research replicability and the misuse and misconception of P-values, the fragility index (FI) has been an attractive measure to assess the robustness (or fragility) of clinical study results with binary outcomes. It is defined as the minimum number of event status modifications that can alter a study result's statistical significance (or non-significance). Owing to its intuitive concept, the FI has been applied to assess the fragility of clinical studies of various specialties. However, the FI may be limited in certain settings. As a relatively new measure, more work is needed to examine its properties.
    Methods: This article explores several factors that may impact the derivation of the FI, including how event status is modified and the impact of significance levels. Moreover, we propose novel methods to visualize the fragility of a study's result. These factors and methods are illustrated using worked examples of artificial datasets. Randomized controlled trials on antidepressant drugs are also used to evaluate their real-world performance.
    Results: The FI depends on the treatment arm(s) in which event status is modified, whether the original study result is significant, the statistical method used for calculating the P-value, and the threshold for determining statistical significance. Also, the proposed visualization methods can clearly demonstrate a study result's fragility, which may be useful supplements to the single value of the FI.
    Conclusions: Our findings may help clinicians properly use the FI and appraise the reliability of a study's conclusion.
    MeSH term(s) Humans ; Reproducibility of Results
    Language English
    Publishing date 2020-06-10
    Publishing country England
    Document type Journal Article
    ZDB-ID 1327355-3
    ISSN 1365-2753 ; 1356-1294
    ISSN (online) 1365-2753
    ISSN 1356-1294
    DOI 10.1111/jep.13428
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Evidence inconsistency degrees of freedom in Bayesian network meta-analysis.

    Lin, Lifeng

    Journal of biopharmaceutical statistics

    2020  Volume 31, Issue 3, Page(s) 317–330

    Abstract: Network meta-analysis (NMA) is a popular tool to synthesize direct and indirect evidence for simultaneously comparing multiple treatments, while evidence inconsistency greatly threatens its validity. One may use the inconsistency degrees of freedom (ICDF) ...

    Abstract Network meta-analysis (NMA) is a popular tool to synthesize direct and indirect evidence for simultaneously comparing multiple treatments, while evidence inconsistency greatly threatens its validity. One may use the inconsistency degrees of freedom (ICDF) to assess the potential that an NMA might suffer from inconsistency. Multi-arm studies provide intrinsically consistent evidence and complicate the ICDF's calculation; they commonly appear in NMAs. The existing ICDF measure may not feasibly handle multi-arm studies. Motivated from the effective numbers of parameters of Bayesian hierarchical models, we propose new ICDF measures in generic NMAs that may contain multi-arm studies. Under the fixed- or random-effects setting, the new ICDF measure is the difference between the effective numbers of parameters of the consistency and inconsistency NMA models. We used artificial NMAs created based on an illustrative example and 39 empirical NMAs to evaluate the performance of the existing and new measures. In NMAs with two-arm studies only, the proposed ICDF measure under the fixed-effects setting was nearly the same with the existing measure. Among the empirical NMAs, 27 (69%) contained at least one multi-arm study. The existing measure was not applicable to them, while the proposed measures led to interpretable ICDFs in all NMAs.
    MeSH term(s) Bayes Theorem ; Humans ; Network Meta-Analysis
    Language English
    Publishing date 2020-12-09
    Publishing country England
    Document type Journal Article ; Meta-Analysis ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1131763-2
    ISSN 1520-5711 ; 1054-3406
    ISSN (online) 1520-5711
    ISSN 1054-3406
    DOI 10.1080/10543406.2020.1852247
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  9. Article ; Online: Beware of references when using ChatGPT as a source of information to write scientific articles.

    Sanchez-Ramos, Luis / Lin, Lifeng / Romero, Roberto

    American journal of obstetrics and gynecology

    2023  Volume 229, Issue 3, Page(s) 356–357

    MeSH term(s) Humans ; Information Sources ; Medical Writing ; Artificial Intelligence
    Language English
    Publishing date 2023-04-07
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 80016-8
    ISSN 1097-6868 ; 0002-9378
    ISSN (online) 1097-6868
    ISSN 0002-9378
    DOI 10.1016/j.ajog.2023.04.004
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  10. Article ; Online: Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses.

    Meng, Zhuo / Wang, Jingshen / Lin, Lifeng / Wu, Chong

    Statistics in medicine

    2024  Volume 43, Issue 8, Page(s) 1549–1563

    Abstract: Meta-analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be ... ...

    Abstract Meta-analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be carefully considered. A critical problem in meta-analyses and systematic reviews is that outlying studies are frequently included, which can lead to invalid conclusions and affect the robustness of decision-making. Outliers may be caused by several factors such as study selection criteria, low study quality, small-study effects, and so on. Although outlier detection is well-studied in the statistical community, limited attention has been paid to meta-analysis. The conventional outlier detection method in meta-analysis is based on a leave-one-study-out procedure. However, when calculating a potentially outlying study's deviation, other outliers could substantially impact its result. This article proposes an iterative method to detect potential outliers, which reduces such an impact that could confound the detection. Furthermore, we adopt bagging to provide valid inference for sensitivity analyses of excluding outliers. Based on simulation studies, the proposed iterative method yields smaller bias and heterogeneity after performing a sensitivity analysis to remove the identified outliers. It also provides higher accuracy on outlier detection. Two case studies are used to illustrate the proposed method's real-world performance.
    MeSH term(s) Humans ; Bias ; Computer Simulation ; Systematic Reviews as Topic ; Meta-Analysis as Topic
    Language English
    Publishing date 2024-02-06
    Publishing country England
    Document type Journal Article
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.10008
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