How does STC differ from MAIC?

STC relies on outcome regression and modeling treatment interactions, making it more efficient if the model is correct. MAIC uses propensity weighting (design-based) and is often preferred when the outcome model is uncertain.

Why is covariate centering important in STC?

Centering covariates on the external population mean ensures that the treatment coefficient represents the effect at the external population's values, which is essential for a valid indirect comparison.

What is STC methodology?

Simulated Treatment Comparison (STC) is an outcome regression-based approach used to compare treatments across different clinical trials by adjusting for differences in patient populations.

Is this skill compliant with regulatory standards?

Yes, the guidance provided follows the framework established by NICE DSU TSD 18 for population-adjusted indirect comparisons.

STC Methodology Assistant

Name: STC Methodology Assistant
Author: choxos

bychoxos

数据科学与机器学习

Provides expert guidance for conducting and reviewing Simulated Treatment Comparisons (STC) following biostatistical best practices and NICE standards.

关于

This skill equips Claude with deep methodological expertise for Simulated Treatment Comparisons (STC), specifically aligned with the NICE DSU TSD 18 framework. It assists researchers and biostatisticians in navigating complex tasks such as selecting effect modifiers, implementing covariate centering, choosing between STC and MAIC, and executing both Frequentist and Bayesian STC models. By leveraging these patterns, users can ensure their indirect treatment comparisons are scientifically rigorous, properly adjusted for population differences, and ready for regulatory or Health Technology Assessment (HTA) submissions.

主要功能

Theoretical and practical comparison between STC and MAIC methods
Detailed protocols for covariate centering in outcome regression
NICE DSU TSD 18 compliant guidance for rigorous STC analyses
0 GitHub stars
Comprehensive strategy for effect modifier identification and selection
Support for both Frequentist and Bayesian STC implementation patterns

使用场景

Selecting and justifying clinical effect modifiers for statistical modeling
Reviewing and validating biostatistical R code or results for methodological accuracy
Preparing HTA submissions requiring population-adjusted indirect treatment comparisons

关于

主要功能

Theoretical and practical comparison between STC and MAIC methods
Detailed protocols for covariate centering in outcome regression
NICE DSU TSD 18 compliant guidance for rigorous STC analyses
0 GitHub stars
Comprehensive strategy for effect modifier identification and selection
Support for both Frequentist and Bayesian STC implementation patterns

使用场景

Selecting and justifying clinical effect modifiers for statistical modeling
Reviewing and validating biostatistical R code or results for methodological accuracy
Preparing HTA submissions requiring population-adjusted indirect treatment comparisons