mediation-analysis
starsforks
How to install mediation-analysis?
gptomics/bioskills/mediation-analysis
$npx -y skills add GPTomics/bioSkills --skill mediation-analysisDecompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML. Use when testing whether a molecular phenotype (expression, methylation, protein) mediates
SKILL.md
| 1 | ## Version Compatibility |
| 2 | |
| 3 | Reference examples tested with: R 4.3+, mediation 4.5.0+, CMAverse 0.1.0+ (GitHub `BS1125/CMAverse`), HIMA >= 2.3.0 (GitHub `YinanZheng/HIMA`; archived from CRAN 2026-07), bama 1.3+, causalweight 1.0.5+ (medDML), MVMR 0.4+, TwoSampleMR 0.6+, EValue 4.1+, gesttools 1.3+, ipw 1.0.11+. |
| 4 | |
| 5 | Before using code patterns, verify installed versions match. If versions differ: |
| 6 | - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters |
| 7 | - HIMA must be pinned at `>= 2.3.0` for the code patterns below; the formula interface `hima(formula, data.pheno, data.M, mediator.type, penalty, ...)` was introduced in 2.3.0. On HIMA 2.2.x the classic engine is the top-level `hima(X, Y, M, COV.XM=, COV.MY=, Y.family=, penalty=)` (positional order X, Y, M) with no formula interface; HIMA 2.2.x is NOT API-compatible with the examples here. |
| 8 | - In 2.3+ the classic engine is `hima_classic(X, M, Y, COV.XM=, COV.MY=, Y.type=)` (positional order X, M, Y; outcome type via `Y.type`, not `Y.family`); use it only to reproduce the original 2016-2021 SIS+penalty pipelines. |
| 9 | |
| 10 | If code throws an error, introspect the installed package (`?hima`, `args(cmest)`) and adapt the example to match the actual API rather than retrying. |
| 11 | |
| 12 | # Mediation Analysis |
| 13 | |
| 14 | **"Does expression of GENE_X mediate the SNP-to-disease effect?"** -> Decompose the total effect of a treatment (genotype, exposure) on an outcome into direct and indirect paths through one or more mediators, with explicit handling of exposure-mediator interaction, sensitivity to unmeasured confounding, and high-dimensional mediator screening. |
| 15 | |
| 16 | - R (single-mediator, observational, sequential-ignorability assumed): `mediation::mediate(med_model, out_model, treat='X', mediator='M', boot=TRUE, sims=5000)` |
| 17 | - R (4-way decomposition with exposure-mediator interaction): `CMAverse::cmest(...EMint=TRUE, estimation='paramfunc', inference='bootstrap', nboot=1000)` |
| 18 | - R (high-dimensional / EWAS mediators): `HIMA::hima(Y ~ X + covariates, data.pheno, data.M, mediator.type='gaussian', penalty='DBlasso')` (modern v2.3+ formula interface) |
| 19 | - R (MR-based mediation): two-step `TwoSampleMR` with independent instruments OR `MVMR::ivw_mvmr` for joint direct effect |
| 20 | - R (doubly-robust double-ML): `causalweight::medDML(y, d, m, x)` |
| 21 | |
| 22 | Sequential ignorability (no unmeasured confounder of treatment-mediator, mediator-outcome, treatment-outcome) is the single load-bearing assumption of observational mediation and is fundamentally untestable. Every report should include a sensitivity result (Imai's rho via `medsens()` or a mediational E-value). |
| 23 | |
| 24 | ## Algorithmic Taxonomy |
| 25 | |
| 26 | | Method | Framework | Handles E-M interaction | High-D mediators | Min n | Fails when | |
| 27 | |--------|-----------|--------------------------|------------------|-------|------------| |
| 28 | | Baron-Kenny (1986) | Additive regression-based product/difference | No | No | ~100 | Any non-linearity, interaction, or binary outcome; deprecated for causal inference | |
| 29 | | Imai mediation R (Imai 2010 Psychol Methods) | Counterfactual ACME/ADE with bootstrap | Yes (via interaction term in outcome model) | No | ~200 | Sequential ignorability violated; exposure-induced M-Y confounder; rare binary outcome with logistic outcome model | |
| 30 | | VanderWeele 4-way (VanderWeele 2014 Epidemiology 25:749; 2015 OUP) | CDE + PIE + INTref + INTmed decomposition | Native | No | ~300 | Without interaction term reduces to standard mediation; binary outcome needs rare-disease assumption | |
| 31 | | CMAverse (Shi 2021 Epidemiology 32:e20) | 6 estimators: regression (`rb`), weighting (`wb`), IORW (`iorw`), natural effect models (`ne`), MSM (`msm`), g-formula (`gformula`) | Yes | No (single M, or M-vector) | ~300 | Estimator-specific; `wb` fails with rare exposure; `msm` needs censoring weights for survival | |
| 32 | | HIMA1 / hima_classic (Zhang 2016 Bioinformatics 32:3150) | SIS screen by beta (M->Y) + MCP penalty | No | Yes (up to ~10k) | ~150 + p>>n | Misses mediators with strong alpha and weak beta; screening-step false-negatives | |
| 33 | | HIMA2 / hima (Perera 2022 BMC Bioinformatics 23:296) | SIS screen by alpha*beta (indirect effect) + de-biased Lasso (DBlasso) | No (linear by default) | Yes | ~150 | Outcome family limited (gaussian/binomial); HIMA-Cox for survival; HIMA-Pois for count | |
| 34 | | HILAMA (Wang et al 2025) | High-D mediation with latent confounders | No | Yes (>= 100k) | ~500 | Newer; benchmarks evolv |
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