What is the primary difference between STC and MAIC?

STC is an outcome regression-based approach that predicts treatment effects at specific covariate values, whereas MAIC is a design-based approach using propensity weighting to match population characteristics.

Does this skill help with Bayesian implementation?

Yes, it includes guidance on prior selection, posterior predictive checks, and implementation patterns for Bayesian anchored STC models.

Why is covariate centering necessary in STC?

Centering covariates on the external population's mean ensures the treatment intercept represents the effect in that specific target population, making the indirect comparison valid.

When should STC be preferred over MAIC?

STC is often preferred when model specification confidence is high, when dealing with continuous covariates that are difficult to categorize, or when MAIC results in a very low Effective Sample Size (ESS).

STC Methodology Expert

Name: STC Methodology Expert
Author: choxos

bychoxos

データサイエンスとML

Provides expert methodological guidance and implementation patterns for conducting rigorous Simulated Treatment Comparisons (STC) in clinical trial analysis.

概要

This skill equips Claude with deep technical knowledge of Simulated Treatment Comparisons (STC), specifically aligned with standards such as NICE DSU TSD 18. It assists researchers and biostatisticians in navigating the complexities of population-adjusted indirect comparisons (PAIC). The skill provides guidance on selecting effect modifiers, implementing covariate centering, and performing outcome regression in both frequentist and Bayesian frameworks. By offering structured implementation patterns in R and theoretical comparisons with Matching-Adjusted Indirect Comparison (MAIC), it ensures that health technology assessments and clinical data analyses are statistically robust and reproducible.

主な機能

0 GitHub stars
Effect modifier identification and statistical selection strategies
NICE DSU TSD 18 methodological compliance guidance
Bayesian STC implementation with prior specification patterns
Covariate centering logic for population-adjusted models
Comparative analysis between STC and MAIC approaches

ユースケース

Validating statistical assumptions and model specifications for health economics research
Conducting anchored or unanchored indirect treatment comparisons for HTA submissions
Developing and reviewing R code for population-adjusted clinical trial models

概要

主な機能

0 GitHub stars
Effect modifier identification and statistical selection strategies
NICE DSU TSD 18 methodological compliance guidance
Bayesian STC implementation with prior specification patterns
Covariate centering logic for population-adjusted models
Comparative analysis between STC and MAIC approaches

ユースケース

Validating statistical assumptions and model specifications for health economics research
Conducting anchored or unanchored indirect treatment comparisons for HTA submissions
Developing and reviewing R code for population-adjusted clinical trial models