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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Utilising high-dimensional data in randomised clinical trials
T2 - a review of methods and practice
AU - Cherlin, Svetlana
AU - Bigirumurame, Theophile
AU - Grayling, Michael
AU - Nsengimana, Jeremie
AU - Ouma, Luke
AU - Santaolalla, Aida
AU - Wan, Fang
AU - Williamson, Faye
AU - Wason, James
PY - 2023/12/25
Y1 - 2023/12/25
N2 - IntroductionEven 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.MethodsWe 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.ResultsOut 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.DiscussionNew 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.
AB - IntroductionEven 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.MethodsWe 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.ResultsOut 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.DiscussionNew 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.
U2 - 10.1177/26320843231186399
DO - 10.1177/26320843231186399
M3 - Journal article
JO - Research Methods in Medicine & Health Sciences
JF - Research Methods in Medicine & Health Sciences
ER -