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Analysis code for: Co-exposure of micro/nanoplastics and plasticizers and mammalian cancer risk — a systematic review and three-level meta-analysis
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This is a code and data repository for a statistical analysis, not a new experiment. It provides scripts used to combine results from multiple animal studies on combined exposure to microplastics and plasticizers, examining links to cancer-related effects, along with tools to check and visualize the calculations.
meta_analysis.R — three-level random-effects meta-analysis of log-response-ratio (lnRR) effect sizes using the metafor package (rma.mv with nested random effects ~1|Study_ID and ~1|EffectID); reports pooled lnRR, τ² decomposition (between-study and within-study), I², and Q statistics, plus subgroup and moderator analyses. mixture_model.R — Scheffé simplex mixture modelling of the co-exposure composition–response relationship (lm(lnRR ~ w_MP)), reproducing the response-surface scenarios. sensitivity_loso.R — leave-one-study-out influence analysis and scenario sensitivity checks. verify_threelevel.py — a dependency-free Python cross-check of the three-level pooled estimate (no R required). figures/ — Python scripts (matplotlib, scikit-learn, xgboost) for the main and supplementary figures, including machine-learning comparators. data/ — source data extracts (effect-size明细, three-level outputs, dose worksheet) required to reproduce the results. README.md — full instructions, dependency versions, and expected outputs. Dependencies: R ≥ 4.0 with metafor; Python 3.13 with NumPy 2.5.3, SciPy 1.18.1, scikit-learn 1.9.1, xgboost 3.4.1, matplotlib 3.11.1 (see R_dependencies.R). Usage: see README.md. Run the R scripts to reproduce the meta-analytic estimates and the Python scripts to regenerate figures.
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