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Even in effectively conducted randomised trials, the probability of asuccessful study remains relatively low. With recent advances in thenext-generation sequencing technologies, there is a rapidly growing number ofhigh-dimensional data, including genetic, molecular and phenotypic information,that have improved our understanding of driver genes, drug targets, and drugmechanisms of action. The leveraging of high-dimensional data holds promise forincreased success of clinical trials.  Methods  We provide an overview of methods for utilising high-dimensional data inclinical trials. We also investigate the use of these methods in practicethrough a review of recently published randomised clinical trials that utilisehigh-dimensional genetic data. The review includes articles that were publishedbetween 2019 and 2021, identified through the PubMed database.  Results  Out of 174 screened articles, 100 (57.5%) were randomised clinical trialsthat collected high-dimensional data. The most common clinical area wasoncology (30%), followed by chronic diseases (28%), nutrition and ageing (18%)and cardiovascular diseases (7%). The most common types of data analysed weregene expression data (70%), followed by DNA data (21%). The most common methodof analysis (36.3%) was univariable analysis. Articles that describedmultivariable analyses used standard statistical methods. Most of the clinicaltrials had two arms.  Discussion  New methodological approaches are required for more efficient analysis of theincreasing amount of high-dimensional data collected in randomised clinicaltrials. We highlight the limitations and barriers to the current use ofhigh-dimensional data in trials, and suggest potential avenues for improvementand future work.

Utilising high-dimensional data in randomised clinical trials: a review of methods and practice