  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.
