Utilising high-dimensional data in randomised clinical trials: a review of methods and practice
| Autor: | Cherlin, Svetlana, Bigirumurame, Theophile, Grayling, Michael J, Nsengimana, Jérémie, Ouma, Luke, Santaolalla, Aida, Wan, Fang, Williamson, S Faye, Wason, James M S |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Introduction: Even in effectively conducted randomised trials, the probability of a successful study remains relatively low. With recent advances in the next-generation sequencing technologies, there is a rapidly growing number of high-dimensional data, including genetic, molecular and phenotypic information, that have improved our understanding of driver genes, drug targets, and drug mechanisms of action. The leveraging of high-dimensional data holds promise for increased success of clinical trials. Methods: We provide an overview of methods for utilising high-dimensional data in clinical trials. We also investigate the use of these methods in practice through a review of recently published randomised clinical trials that utilise high-dimensional genetic data. The review includes articles that were published between 2019 and 2021, identified through the PubMed database. Results: Out of 174 screened articles, 100 (57.5%) were randomised clinical trials that collected high-dimensional data. The most common clinical area was oncology (30%), followed by chronic diseases (28%), nutrition and ageing (18%) and cardiovascular diseases (7%). The most common types of data analysed were gene expression data (70%), followed by DNA data (21%). The most common method of analysis (36.3%) was univariable analysis. Articles that described multivariable analyses used standard statistical methods. Most of the clinical trials had two arms. Discussion: New methodological approaches are required for more efficient analysis of the increasing amount of high-dimensional data collected in randomised clinical trials. We highlight the limitations and barriers to the current use of high-dimensional data in trials, and suggest potential avenues for improvement and future work. Comment: 17 pages, 3 figures, 2 tables |
| Databáze: | arXiv |
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