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

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

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

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

Subgroup Identification in Clinical Trials by Stochastic SIDEScreen Methods

2017 ◽  
Vol 9 (4) ◽  
pp. 368-378 ◽  
Author(s):  
Ilya Lipkovich ◽  
Alex Dmitrienko ◽  
Kaushik Patra ◽  
Bohdana Ratitch ◽  
Erik Pulkstenis
Keyword(s):  
Clinical Trials ◽  
Subgroup Identification

scholarly journals Repurposing FDA-Approved Drugs for COVID-19 Using a Data-Driven Approach

2020 ◽  
Author(s):  
Rodrigo R. R. Duarte ◽  
Dennis C. Copertino Jr. ◽  
Luis P. Iñiguez ◽  
Jez L. Marston ◽  
Douglas F. Nixon ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Drug Repositioning ◽  
Data Driven ◽  
Transcriptional Signature ◽  
Lung Carcinoma Cells ◽  
Main Protease ◽  
Data Driven Approach ◽  
Approved Drugs ◽  
Fda Approved Drugs

<p>There have been more than 116,000 recorded deaths worldwide to-date caused by the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 2019 (COVID-19), and over 1.8 million individuals are currently infected. Although there are now hundreds of clinical trials for COVID-19, there are currently no effective licensed treatments, while the numbers of infected individuals continue to rise at an exponential rate in many parts of the world. Here, we used a data-driven approach utilizing connectivity mapping and the transcriptional signature of lung carcinoma cells infected with SARS-CoV-2, to search for drugs across the spectrum of medicine that have repurposing potential for treating COVID-19. We also performed chemoinformatic analyses to test whether the identified compounds were predicted to physically interact with the SARS-CoV-2 RNA-dependent RNA polymerase or main protease enzymes. Our study identified commonly prescribed FDA-approved molecules as important candidates for drug repositioning against COVID-19, including flupentixol, reserpine, fluoxetine, trifluoperazine, sunitinib, atorvastatin, raloxifene, butoconazole, and metformin. These drugs should not be taken for treating or preventing COVID-19 without a doctor’s advice, as further research and clinical trials are now needed to elucidate their efficacy for this purpose.</p>

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