Data-Driven and Confirmatory Subgroup Analysis in Clinical Trials

2020 ◽  
pp. 33-91
Author(s):  
Alex Dmitrienko ◽  
Ilya Lipkovich ◽  
Aaron Dane ◽  
Christoph Muysers
Keyword(s):  
Clinical Trials ◽  
Subgroup Analysis ◽  
Data Driven

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

Data-Driven Subgroup Identification in Confirmatory Clinical Trials

2021 ◽  
Author(s):  
Pierre Bunouf ◽  
Mélanie Groc ◽  
Alex Dmitrienko ◽  
Ilya Lipkovich
Keyword(s):  
Clinical Trials ◽  
Data Driven ◽  
Subgroup Identification ◽  
Confirmatory Clinical Trials

Subgroup analysis in obstetrics clinical trials

2007 ◽  
Vol 197 (2) ◽  
pp. 119-122 ◽  
Author(s):  
Mark A. Klebanoff
Keyword(s):  
Clinical Trials ◽  
Subgroup Analysis

scholarly journals Rainforest plots for the presentation of patient-subgroup analysis in clinical trials

2017 ◽  
pp. 485-485 ◽  
Author(s):  
Zhongheng Zhang ◽  
Michael Kossmeier ◽  
Ulrich S. Tran ◽  
Martin Voracek ◽  
Haoyang Zhang
Keyword(s):  
Clinical Trials ◽  
Subgroup Analysis ◽  
Patient Subgroup

scholarly journals Guidelines on how to assess the validity of results presented in subgroup analysis of clinical trials

2002 ◽  
Vol 57 (2) ◽  
pp. 83-88 ◽  
Author(s):  
Edson Duarte Moreira ◽  
Ezra Susser
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Randomized Clinical Trial ◽  
Subgroup Analysis ◽  
Observational Studies ◽  
Randomized Clinical Trials ◽  
Usual Method ◽  
Validity Of Results ◽  
Results Of Treatment ◽  
Trial Participants

In observational studies, identification of associations within particular subgroups is the usual method of investigation. As an exploratory method, it is the bread and butter of epidemiological research. Nearly everything that has been learned in epidemiology has been derived from the analysis of subgroups. In a randomized clinical trial, the entire purpose is the comparison of the test subjects and the controls, and when there is particular interest in the results of treatment in a certain section of trial participants, a subgroup analysis is performed. These subgroups are examined to see if they are liable to a greater benefit or risk from treatment. Thus, analyzing patient subsets is a natural part of the process of improving therapeutic knowledge through clinical trials. Nevertheless, the reliability of subgroup analysis can often be poor because of problems of multiplicity and limitations in the numbers of patients studied. The naive interpretation of the results of such examinations is a cause of great confusion in the therapeutic literature. We emphasize the need for readers to be aware that inferences based on comparisons between subgroups in randomized clinical trials should be approached more cautiously than those based on the main comparison. That is, subgroup analysis results derived from a sound clinical trial are not necessarily valid; one must not jump to conclusions and accept the validity of subgroup analysis results without an appropriate judgment.

Treatment evaluation for a data-driven subgroup in adaptive enrichment designs of clinical trials

2017 ◽  
Vol 37 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Zhiwei Zhang ◽  
Ruizhe Chen ◽  
Guoxing Soon ◽  
Hui Zhang
Keyword(s):  
Clinical Trials ◽  
Treatment Evaluation ◽  
Data Driven ◽  
Enrichment Designs ◽  
Adaptive Enrichment Designs

Subgroup analysis in clinical trials

2004 ◽  
Vol 180 (6) ◽  
pp. 289-291 ◽  
Author(s):  
David I Cook ◽  
Val J Gebski ◽  
Anthony C Keech
Keyword(s):  
Clinical Trials ◽  
Subgroup Analysis

scholarly journals A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials

2016 ◽  
Vol 23 (10) ◽  
pp. 1294-1301 ◽  
Author(s):  
Kaitlyn M. Gayvert ◽  
Neel S. Madhukar ◽  
Olivier Elemento
Keyword(s):  
Clinical Trials ◽  
Data Driven ◽  
Data Driven Approach ◽  
Successes And Failures
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