Can it handle survival analysis outcomes?

Yes, it includes implementation patterns for mediation with survival data using both the mediation and CMAverse packages, supporting Cox proportional hazards models.

Which R packages does this skill support for mediation?

The skill supports a wide range of packages including mediation, CMAverse, medflex, intmed, and lavaan for structural equation modeling.

Does this skill help with sensitivity analysis?

Absolutely. It provides specialized code for medsens, E-values, and CMAverse-based sensitivity checks to evaluate the impact of unmeasured confounding.

Can I analyze multiple mediators simultaneously?

Yes, it provides specific guidance for both parallel and sequential (serial) mediation models where one mediator influences another.

R Causal Mediation Analysis

Name: R Causal Mediation Analysis
Author: choxos

bychoxos

0•

데이터 과학 및 ML

Performs advanced causal mediation analysis in R to decompose total effects into direct and indirect pathways across various statistical models.

This skill equips Claude with the expertise to conduct complex causal mediation analysis within the R ecosystem, supporting everything from traditional Baron-Kenny steps to modern natural effect models and comprehensive frameworks like CMAverse. It provides implementation patterns for handling binary outcomes, survival data, and multiple mediators, while emphasizing rigorous causal inference through Directed Acyclic Graph (DAG) analysis and sensitivity testing for unmeasured confounding. It is particularly useful for researchers and data scientists needing to understand the underlying mechanisms of treatment effects in clinical, social, or behavioral studies.

주요 기능

01Advanced modeling of exposure-mediator interactions and sequential mediation pathways.

02Support for diverse outcome types including continuous, binary, and survival (Cox) data.

030 GitHub stars

04Implementation of traditional and modern causal mediation frameworks including mediation, CMAverse, and medflex.

05Robust sensitivity analysis tools for assessing unmeasured confounding, including E-values.

06Automated generation of causal DAGs and standardized reporting tables for research findings.

사용 사례

01Decomposing treatment effects in clinical trials to understand which biological mechanisms drive patient outcomes.

02Conducting sensitivity analyses to validate the robustness of indirect effect estimates against hidden biases or unmeasured confounders.

03Analyzing complex causal chains involving multiple parallel or serial mediators in longitudinal social science research.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/tidyrmodelling causal-mediation

For use in Claude.ai and ChatGPT

Download Skill