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

Adaptive Enrichment Designs in Clinical Trials

2021 ◽  
Vol 8 (1) ◽  
pp. 393-411
Author(s):  
Peter F. Thall
Keyword(s):  
Clinical Trials ◽  
Adaptive Randomization ◽  
Patient Heterogeneity ◽  
Different Types ◽  
Targeted Agent ◽  
Enrichment Designs ◽  
Adaptive Enrichment Designs ◽  
Adaptive Decision Making ◽  
Patient Subgroups ◽  
Practical Constraints

Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design's structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.

scholarly journals Adaptive enrichment designs for clinical trials

Biostatistics ◽  
2013 ◽  
Vol 14 (4) ◽  
pp. 613-625 ◽  
Author(s):  
N. Simon ◽  
R. Simon
Keyword(s):  
Clinical Trials ◽  
Enrichment Designs ◽  
Adaptive Enrichment Designs

scholarly journals Adaptive Enrichment Designs for Stroke Clinical Trials

Stroke ◽  
2017 ◽  
Vol 48 (7) ◽  
pp. 2021-2025 ◽  
Author(s):  
Michael Rosenblum ◽  
Daniel F. Hanley
Keyword(s):  
Clinical Trials ◽  
Enrichment Designs ◽  
Adaptive Enrichment Designs

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.

Adaptive enrichment designs: applications and challenges

2015 ◽  
Vol 5 (4) ◽  
pp. 383-391 ◽  
Author(s):  
Noah Simon
Keyword(s):  
Enrichment Designs ◽  
Adaptive Enrichment Designs

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 Adaptive enrichment designs for confirmatory trials

2018 ◽  
Vol 38 (4) ◽  
pp. 613-624 ◽  
Author(s):  
Tze Leung Lai ◽  
Philip W. Lavori ◽  
Ka Wai Tsang
Keyword(s):  
Enrichment Designs ◽  
Adaptive Enrichment Designs ◽  
Confirmatory Trials

Optimized adaptive enrichment designs for three-arm trials: learning which subpopulations benefit from different treatments

Biostatistics ◽  
2019 ◽  
Author(s):  
Jon Arni Steingrimsson ◽  
Joshua Betz ◽  
Tianchen Qian ◽  
Michael Rosenblum
Keyword(s):  
Sample Size ◽  
Disease Severity ◽  
Adaptive Design ◽  
Type I Error ◽  
Type I ◽  
Expected Sample Size ◽  
Common Control ◽  
Enrichment Designs ◽  
Adaptive Enrichment Designs ◽  
Standard Designs

Summary We consider the problem of designing a confirmatory randomized trial for comparing two treatments versus a common control in two disjoint subpopulations. The subpopulations could be defined in terms of a biomarker or disease severity measured at baseline. The goal is to determine which treatments benefit which subpopulations. We develop a new class of adaptive enrichment designs tailored to solving this problem. Adaptive enrichment designs involve a preplanned rule for modifying enrollment based on accruing data in an ongoing trial. At the interim analysis after each stage, for each subpopulation, the preplanned rule may decide to stop enrollment or to stop randomizing participants to one or more study arms. The motivation for this adaptive feature is that interim data may indicate that a subpopulation, such as those with lower disease severity at baseline, is unlikely to benefit from a particular treatment while uncertainty remains for the other treatment and/or subpopulation. We optimize these adaptive designs to have the minimum expected sample size under power and Type I error constraints. We compare the performance of the optimized adaptive design versus an optimized nonadaptive (single stage) design. Our approach is demonstrated in simulation studies that mimic features of a completed trial of a medical device for treating heart failure. The optimized adaptive design has $25\%$ smaller expected sample size compared to the optimized nonadaptive design; however, the cost is that the optimized adaptive design has $8\%$ greater maximum sample size. Open-source software that implements the trial design optimization is provided, allowing users to investigate the tradeoffs in using the proposed adaptive versus standard designs.

scholarly journals Multiple testing procedures for adaptive enrichment designs: combining group sequential and reallocation approaches

Biostatistics ◽  
2016 ◽  
Vol 17 (4) ◽  
pp. 650-662 ◽  
Author(s):  
Michael Rosenblum ◽  
Tianchen Qian ◽  
Yu Du ◽  
Huitong Qiu ◽  
Aaron Fisher
Keyword(s):  
Multiple Testing ◽  
Group Sequential ◽  
Testing Procedures ◽  
Multiple Testing Procedures ◽  
Enrichment Designs ◽  
Adaptive Enrichment Designs
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