Which R packages are primarily used by this skill?

The skill utilizes industry-standard libraries including TrialEmulation, MatchIt, WeightIt, cobalt, PSweight, AIPW, and EValue.

What is target trial emulation in this context?

Target trial emulation is a framework provided by this skill to design and analyze observational data as if it were a randomized controlled trial, reducing common biases like immortal time bias.

Does this skill support time-varying confounding?

Yes, it provides implementation patterns for Marginal Structural Models (MSM) and Inverse Probability of Censoring Weighting (IPCW) to handle complex time-dependent variables.

How does the skill handle unmeasured confounding?

It includes specialized code for calculating E-values, performing tipping point analysis, and conducting quantitative probabilistic bias analysis.

Real-World Evidence Analysis in R

Name: Real-World Evidence Analysis in R
Author: choxos

bychoxos

数据科学与机器学习

Generates real-world evidence from observational data using advanced R-based causal inference and target trial emulation techniques.

关于

This skill provides a comprehensive framework for conducting Real-World Evidence (RWE) studies using the R programming language. it implements best-practice methodologies for comparative effectiveness research, including target trial emulation, various propensity score methods (matching, weighting, overlapping), and doubly robust estimation. Designed for biostatisticians and health data scientists, it streamlines the complex workflow of addressing selection bias, time-varying confounding, and informative censoring, while providing automated tools for sensitivity analysis such as E-values and quantitative bias analysis.

主要功能

Propensity score methods including matching, IPW, and overlap weighting
Sensitivity analysis for unmeasured confounding with E-values and tipping points
Integration and weighting of external control arms with clinical trial data
Time-varying confounding management via Marginal Structural Models (MSM)
Target trial emulation using cloning, censoring, and weighting approaches
0 GitHub stars

使用场景

Emulating randomized clinical trials using electronic health records or insurance claims data
Comparing the effectiveness of medical treatments in routine clinical practice settings
Quantifying the impact of potential unmeasured bias on observational study findings

关于

主要功能

Propensity score methods including matching, IPW, and overlap weighting
Sensitivity analysis for unmeasured confounding with E-values and tipping points
Integration and weighting of external control arms with clinical trial data
Time-varying confounding management via Marginal Structural Models (MSM)
Target trial emulation using cloning, censoring, and weighting approaches
0 GitHub stars

使用场景

Emulating randomized clinical trials using electronic health records or insurance claims data
Comparing the effectiveness of medical treatments in routine clinical practice settings
Quantifying the impact of potential unmeasured bias on observational study findings