Individual Participant Data (IPD) Meta-Analysis

关于

This skill provides comprehensive guidance and implementation patterns for conducting Individual Participant Data (IPD) meta-analyses within the R environment. It covers critical methodologies including one-stage mixed-effects models and two-stage approaches, handling complex data structures like survival and binary outcomes, and integrating IPD with aggregate data. It is particularly useful for clinical researchers and biostatisticians needing to account for treatment-covariate interactions, manage missing multi-study data, or perform network meta-analyses while adhering to PRISMA-IPD standards.

主要功能

• Integration techniques for combining IPD with aggregate study-level data
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• Support for linear, logistic, and Cox mixed-effects modeling
• Multilevel missing data handling via multiple imputation and sensitivity analysis
• Implementation of one-stage and two-stage IPD meta-analysis workflows
• Advanced treatment-covariate interaction analysis (within vs. between-study)

使用场景

• Conducting clinical trial synthesis to identify patient-level treatment modifiers
• Performing network meta-analyses (NMA) using a mix of individual and aggregate evidence
• Harmonizing heterogeneous patient datasets for large-scale observational research