Randomized controlled trials are the gold standard for evaluating the effects of medical interventions, primarily providing estimates of the average effect of an intervention in the overall study population. However, there may be significant differences in the effect of the same intervention across sub-populations with different characteristics, that is, treatment heterogeneity. Traditional subgroup analysis and interaction analysis tend to have low power to examine treatment heterogeneity or identify the sources of heterogeneity. With the recent development of machine learning techniques, causal forest has been proposed as a novel method to evaluate treatment heterogeneity, which can help overcome the limitations of the traditional methods. However, the application of causal forest in the evaluation of treatment heterogeneity in medicine is still in the beginning stage. In order to promote proper use of causal forest, this paper introduces its purposes, principles and implementation, interprets the examples and R codes, and highlights some attentions needed for practice.
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.
With the rapid global development of artificial intelligence (AI) technology, its applications in various industries continue to deepen. Against this backdrop, AI empowering high-quality development in the pharmaceutical and healthcare sector has become a global research hotspot, and AI-enabled health technology assessment (HTA) constitutes a key component of AI applications in the pharmaceutical field. Currently, the World Health Organization (WHO), International Society for Pharmacoeconomics and Outcomes Research (ISPOR), National Institute for Health and Care Excellence (NICE), and Canada’s Drug Agency (CDA-AMC) are all actively exploring the application pathways, technical challenges, developmental limitations, and solutions of AI in HTA. This paper aims to conduct a systematic analysis of AI-related basic concepts and classifications, typical application scenarios of AI in HTA, risk challenges, and position principles, based on the exploratory findings of the aforementioned entities regarding AI applications in HTA and integrating the technical characteristics of AI and HTA themselves. It further seeks to propose the application pathways, directions, and future prospects of AI in China’s HTA field by drawing on international beneficial experiences.