Clinical trial transparency, include clinical trial registration, unbiased reporting results and sharing individual participant data (IPD), is one of the most important revolutionary concepts following clinical epidemiology and evidence-based medicine in the medical field. Sharing IPD is a medical ethics issue reflected a new sense of worth and constructing new rules of clinical trials. Our viewpoint is that from the essential purpose of clinical research, IPD is a social public property. Sharing IPD is a one of the best ways for respecting the contributions of the participants, and one of the keys for changing face of clinical trials.
Network meta-analysis (NMA) is a statistical technique that integrates data from multiple clinical studies and compares the efficacy and safety of multiple interventions, which can provide pro and con ranking results for all intervention options in the evidence network and provide direct evidence support for clinical decision-making. At present, NMA is usually based on the aggregation of the same type of data set, and there are still methodological and software difficulties in achieving cross-study design and cross-data format data set merging. The crossnma package of R programming language is based on Bayesian framework and Markov chain Monte Carlo algorithm, extending the three-level hierarchical model to the standard NMA data model to achieve differential merging of varied data types. The crossnma package fully considers the impact of risk bias caused by the combination of different types of data on the results by introducing model variables. In addition, the package provides functions such as result output and easy graphing, which makes it possible to combine NMA across study designs and evidence across data formats. In this study, the model based on crossnma package method and software operation will be demonstrated and explained through the examples of four individual participant datasets and two aggregate datasets.
This article systematically reviews the series of articles on randomized controlled trial (RCT) methodology guidance published in JAMA Surgery between 2022 and 2023. It focuses on providing an overview and guidance on critical aspects such as trial implementation and oversight, participant recruitment, statistical applications, and key points in manuscript publication. The aim is to offer valuable insights and references for surgeons to conduct efficient clinical trials and successfully publish their research findings.