Systematic reviews and meta-analyses have become the cornerstone methodologies for integrating multi-source research data and enhancing the quality of evidence. Traditional meta-analyses often demonstrate limitations when handling multiple treatment options. Network meta-analysis (NMA) overcomes these limitations by constructing a network of evidence that encompasses various treatment options, allowing for the simultaneous comparison of both direct and indirect evidence across multiple treatment plans. This provides more comprehensive and precise support for clinical decision-making. This article comprehensively reviews the statistical principles of NMA, its three fundamental assumptions, and the statistical inference framework. It also critically analyzes the mainstream NMA software and packages currently available, such as R (including gemtc, netmeta, rjags, pcnetmeta), Stata (mvmeta, network), WinBUGS, SAS, ADDIS, and various online applications, highlighting their strengths, weaknesses, and suitable scenarios. This analysis provides researchers with a scientific and unified framework for conducting clinical studies and policy-making.
The calculation of sample size is a critical component in the design phase of clinical trials incorporating health economic evaluations. A reasonable sample size is essential to ensure the scientific validity and accuracy of trial results. This paper summarizes the sample size calculation methods in the frequentist framework based on two health economic evaluation metrics: incremental cost-effectiveness ratio (ICER) and net benefit. It focuses on their applicable conditions, advantages, and limitations. The ICER method derives the sample size calculation formula by computing the ratio of incremental cost to incremental effect, while the net benefit method determines the economic viability of interventions by calculating incremental net benefit, subsequently leading to the formulation of the sample size calculation. Furthermore, this paper briefly discusses other sample size calculation methods, such as the classical Bayesian approach and the value of information analysis, providing a reference for calculating sample size in clinical trials with integrated health economic evaluations.