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

Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials

2016 ◽  
Vol 36 (1) ◽  
pp. 136-196 ◽  
Author(s):  
Ilya Lipkovich ◽  
Alex Dmitrienko ◽  
Ralph B.
Keyword(s):  
Clinical Trials ◽  
Data Driven ◽  
Subgroup Identification

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.

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
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