Modern data analysis tools and statistical modeling techniques are increasingly used in clinical research to improve diagnosis, estimate disease progression and predict treatment outcomes. What seems less emphasized is the importance of the study design, which can have a serious impact on the study cost, time and statistical efficiency. This paper provides an overview of different types of adaptive designs in clinical trials and their applications to cardiovascular trials. We highlight recent proliferation of work on adaptive designs over the past two decades, including some recent regulatory guidelines on complex trial designs and master protocols. We also describe the increasing role of machine learning and use of metaheuristics to construct increasingly complex adaptive designs or to identify interesting features for improved predictions and classifications.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.cct.2021.106397 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
TAD-SIE: sample size estimation for clinical randomized controlled trials using a Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator.
Trials
January 2025
Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA.
Background: Phase-3 clinical trials provide the highest level of evidence on drug safety and effectiveness needed for market approval by implementing large randomized controlled trials (RCTs). However, 30-40% of these trials fail mainly because such studies have inadequate sample sizes, stemming from the inability to obtain accurate initial estimates of average treatment effect parameters.
Methods: To remove this obstacle from the drug development cycle, we present a new algorithm called Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator (TAD-SIE) that powers a parallel-group trial, a standard RCT design, by leveraging a state-of-the-art hypothesis testing strategy and a novel trend-adaptive design (TAD).
Enhanced Bayesian model for multienvironmental selection of winter hybrids maize: assessing grain yield using 'ProbBreed'.
Plant Methods
January 2025
Faculty of Agriculture, Agriculture and Forestry University, Bharatpur, 13712, Nepal.
Background: Crossover interactions stemming from phenotypic plasticity complicate selection decisions when evaluating hybrid maize with superior grain yield and consistent performance. Consequently, a two-year, region-wide investigation of 45 hybrids maize across Nepal was performed with the aim of disclosing both site and wide adapted hybrids. Utilizing an innovative "ProbBreed" package, based on Bayesian probability analysis of randomized complete block designs with three replicated trials at each station, this study substantively streamlines hybrids maize selection.
View Article and Find Full Text PDFIdentification of heat tolerant lentil genotypes through stress tolerance indices.
Sci Rep
January 2025
Division of Genetics, Indian Agricultural Research Institute, New Delhi, India.
With climate change projections indicating an increase in the frequency of extreme heat events and irregular rainfall patterns globally, the threat to global food security looms large. Terminal heat stress, which occurs during the critical reproductive stage, significantly limits lentil productivity. Therefore, there is an urgent need to improve lentil's resilience to heat stress to sustain production.
View Article and Find Full Text PDFInvestigating the implementation of a Trial of Labour After Caesarean (TOLAC) delivery bundle with respect to decreasing caesarean delivery rates: a multisite quality improvement initiative.
BMJ Open
January 2025
Department of Obstetrics and Gynecology, University of Toronto, Toronto, Ontario, Canada.
Objectives: To study the effect of implementing a Trial of Labour After Caesarean (TOLAC) delivery bundle with respect to decreasing caesarean delivery rates across five hospitals.
Design: Prospective quality improvement study.
Setting: Five Canadian hospital sites participated, two academic centres and three community hospitals, with annual delivery rates ranging from 2500 to 7500 per site.
TPepRet: a deep learning model for characterizing T cell receptors-antigen binding patterns.
Bioinformatics
January 2025
School of Computer Science and engineering, Central South University, Changsha, 410083, China.
Motivation: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer immunotherapy, vaccine design, and autoimmune disease management. Understanding the intricate binding patterns between TCRs and peptides is critical for advancing these clinical applications. While several computational tools have been developed, they neglect the directional semantics inherent in sequence data, which are essential for accurately characterizing TCR-peptide interactions.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!