pcnetmeta - Patient-Centered Network Meta-Analysis

Performs Bayesian arm-based network meta-analysis for datasets with binary, continuous, and count outcomes (Zhang et al., 2014 <doi:10.1177/1740774513498322>; Lin et al., 2017 <doi:10.18637/jss.v080.i05>).

Last updated 3 years ago

jagscpp

2.08 score 1 stars 12 scripts 540 downloads

fragility - Assessing and Visualizing Fragility of Clinical Results with Binary Outcomes

A collection of user-friendly functions for assessing and visualizing fragility of individual studies (Walsh et al., 2014 <doi:10.1016/j.jclinepi.2013.10.019>; Lin, 2021 <doi:10.1111/jep.13428>), conventional pairwise meta-analyses (Atal et al., 2019 <doi:10.1016/j.jclinepi.2019.03.012>), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 <doi:10.1016/j.jclinepi.2020.07.003>). The included functions are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is illustrated in Lin et al. (2023 <doi:10.1016/j.ajog.2022.08.053>) and detailed in Lin and Chu (2022 <doi:10.1371/journal.pone.0268754>).

Last updated 2 months ago

1.48 score 5 scripts 511 downloads

altmeta - Alternative Meta-Analysis Methods

Provides alternative statistical methods for meta-analysis, including: - bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences (Chu et al., 2012 <doi:10.1177/0962280210393712>) - heterogeneity tests and measures and penalization methods that are robust to outliers (Lin et al., 2017 <doi:10.1111/biom.12543>; Wang et al., 2022 <doi:10.1002/sim.9261>); - measures, tests, and visualization tools for publication bias or small-study effects (Lin and Chu, 2018 <doi:10.1111/biom.12817>; Lin, 2019 <doi:10.1002/jrsm.1340>; Lin, 2020 <doi:10.1177/0962280220910172>; Shi et al., 2020 <doi:10.1002/jrsm.1415>); - meta-analysis of combining standardized mean differences and odds ratios (Jing et al., 2023 <doi:10.1080/10543406.2022.2105345>); - meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc. (Reitsma et al., 2005 <doi:10.1016/j.jclinepi.2005.02.022>; Chu and Cole, 2006 <doi:10.1016/j.jclinepi.2006.06.011>); - meta-analysis methods for synthesizing proportions (Lin and Chu, 2020 <doi:10.1097/ede.0000000000001232>); - models for multivariate meta-analysis, measures of inconsistency degrees of freedom in Bayesian network meta-analysis, and predictive P-score (Lin and Chu, 2018 <doi:10.1002/jrsm.1293>; Lin, 2020 <doi:10.1080/10543406.2020.1852247>; Rosenberger et al., 2021 <doi:10.1186/s12874-021-01397-5>).

Last updated 6 months ago

jagscpp

1.04 score 11 scripts 618 downloads