itc-modeling

Design and execute a complete indirect treatment comparison analysis for: $ARGUMENTS

Review ITC analysis code for: $ARGUMENTS

Master ITC strategist specializing in evidence synthesis methodology, study design assessment, and indirect treatment comparison method selection. Helps choose between pairwise MA, NMA, MAIC, STC, and ML-NMR based on data availability and assumptions. Use PROACTIVELY when planning ITC analyses or needing guidance on methodology selection.

Expert ITC R code reviewer who identifies methodological errors, statistical issues, and code quality problems. Offers comprehensive or summary reviews, and can amend code (saved to subfolder to prevent data loss). Use PROACTIVELY when reviewing ITC analysis code.

Expert in Matching-Adjusted Indirect Comparison using the maicplus package. Handles weight estimation, anchored/unanchored MAIC, binary/continuous/TTE endpoints, ESS diagnostics, and Bucher comparisons. Use PROACTIVELY for MAIC analyses.

Expert in Multilevel Network Meta-Regression using the multinma package. Handles combined IPD/AgD networks, population adjustment, covariate integration, and prediction to target populations. Use PROACTIVELY for ML-NMR analyses.

Expert in frequentist and Bayesian network meta-analysis using netmeta and gemtc packages. Handles network visualization, consistency assessment, treatment rankings, and league tables. Use PROACTIVELY for NMA tasks involving multiple treatment comparisons.

Expert in frequentist and Bayesian pairwise meta-analysis using meta, metafor, and bayesmeta packages. Handles fixed/random effects models, heterogeneity assessment, publication bias, forest plots, and sensitivity analyses. Use PROACTIVELY for pairwise MA tasks.

Expert in Simulated Treatment Comparison using the stc package. Handles anchored/unanchored STC for binary, continuous, Poisson, and survival outcomes using both frequentist and Bayesian methods. Use PROACTIVELY for STC analyses.

Deep methodology knowledge for MAIC including assumptions, weight diagnostics, ESS interpretation, and anchored vs unanchored decisions. Use when conducting or reviewing MAIC analyses.

Deep methodology knowledge for ML-NMR including IPD/AgD integration, population adjustment, numerical integration, and prediction to target populations. Use when conducting or reviewing ML-NMR analyses.

Deep methodology knowledge for network meta-analysis including transitivity, consistency assessment, treatment rankings, and model selection. Use when conducting or reviewing NMA.

Deep methodology knowledge for pairwise meta-analysis including fixed vs random effects, heterogeneity assessment, publication bias, and sensitivity analysis. Use when conducting or reviewing pairwise MA.

Deep methodology knowledge for STC including outcome regression approach, effect modifier selection, covariate centering, and comparison with MAIC. Use when conducting or reviewing STC analyses.

Master tidy modelling patterns for ITC analyses following TMwR principles. Covers workflow structure, consistent interfaces, reproducibility best practices, and data validation. Use when setting up ITC analysis projects or building pipelines.