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Su Q, Cheng G, Huang J. A review of research on eligibility criteria for clinical trials. Clin Exp Med 2023; 23:1867-1879. [PMID: 36602707 PMCID: PMC9815064 DOI: 10.1007/s10238-022-00975-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/06/2022] [Indexed: 01/06/2023]
Abstract
The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials. Inappropriate eligibility criteria will lead to insufficient recruitment, which is an important reason for the eventual failure of many clinical trials. We have investigated the research status of eligibility criteria for clinical trials on academic platforms such as arXiv and NIH. We have classified and sorted out all the papers we found, so that readers can understand the frontier research in this field. Eligibility criteria are the most important part of a clinical trial study. The ultimate goal of research in this field is to formulate more scientific and reasonable eligibility criteria and speed up the clinical trial process. The global research on the eligibility criteria of clinical trials is mainly divided into four main aspects: natural language processing, patient pre-screening, standard evaluation, and clinical trial query. Compared with the past, people are now using new technologies to study eligibility criteria from a new perspective (big data). In the research process, complex disease concepts, how to choose a suitable dataset, how to prove the validity and scientific of the research results, are challenges faced by researchers (especially for computer-related researchers). Future research will focus on the selection and improvement of artificial intelligence algorithms related to clinical trials and related practical applications such as databases, knowledge graphs, and dictionaries.
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Affiliation(s)
- Qianmin Su
- Department of Computer Science, School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, No. 333 Longteng Road, Shanghai, 201620, China.
| | - Gaoyi Cheng
- Department of Computer Science, School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, No. 333 Longteng Road, Shanghai, 201620, China
| | - Jihan Huang
- Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
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Gao D, Liu Y, Zeng D. Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials. JOURNAL OF MACHINE LEARNING RESEARCH : JMLR 2022; 23:https://www.jmlr.org/papers/v23/21-0354.html. [PMID: 37576335 PMCID: PMC10419117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Learning optimal individualized treatment rules (ITRs) has become increasingly important in the modern era of precision medicine. Many statistical and machine learning methods for learning optimal ITRs have been developed in the literature. However, most existing methods are based on data collected from traditional randomized controlled trials and thus cannot take advantage of the accumulative evidence when patients enter the trials sequentially. It is also ethically important that future patients should have a high probability to be treated optimally based on the updated knowledge so far. In this work, we propose a new design called sequentially rule-adaptive trials to learn optimal ITRs based on the contextual bandit framework, in contrast to the response-adaptive design in traditional adaptive trials. In our design, each entering patient will be allocated with a high probability to the current best treatment for this patient, which is estimated using the past data based on some machine learning algorithm (for example, outcome weighted learning in our implementation). We explore the tradeoff between training and test values of the estimated ITR in single-stage problems by proving theoretically that for a higher probability of following the estimated ITR, the training value converges to the optimal value at a faster rate, while the test value converges at a slower rate. This problem is different from traditional decision problems in the sense that the training data are generated sequentially and are dependent. We also develop a tool that combines martingale with empirical process to tackle the problem that cannot be solved by previous techniques for i.i.d. data. We show by numerical examples that without much loss of the test value, our proposed algorithm can improve the training value significantly as compared to existing methods. Finally, we use a real data study to illustrate the performance of the proposed method.
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Affiliation(s)
- Daiqi Gao
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, The University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA
| | - Donglin Zeng
- Department of Biostatistics, The University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA
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Ritz C, Astrup A, Larsen TM, Hjorth MF. Weight loss at your fingertips: personalized nutrition with fasting glucose and insulin using a novel statistical approach. Eur J Clin Nutr 2019; 73:1529-1535. [DOI: 10.1038/s41430-019-0423-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 03/26/2019] [Accepted: 03/26/2019] [Indexed: 01/09/2023]
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Ansart M, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Reduction of recruitment costs in preclinical AD trials: validation of automatic pre-screening algorithm for brain amyloidosis. Stat Methods Med Res 2019; 29:151-164. [PMID: 30698081 DOI: 10.1177/0962280218823036] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We propose a method for recruiting asymptomatic Amyloid positive individuals in clinical trials, using a two-step process. We first select during a pre-screening phase a subset of individuals which are more likely to be amyloid positive based on the automatic analysis of data acquired during routine clinical practice, before doing a confirmatory PET-scan to these selected individuals only. This method leads to an increased number of recruitments and to a reduced number of PET-scans, resulting in a decrease in overall recruitment costs. We validate our method on three different cohorts, and consider five different classification algorithms for the pre-screening phase. We show that the best results are obtained using solely cognitive, genetic and socio-demographic features, as the slight increased performance when using MRI or longitudinal data is balanced by the cost increase they induce. We show that the proposed method generalizes well when tested on an independent cohort, and that the characteristics of the selected set of individuals are identical to the characteristics of a population selected in a standard way. The proposed approach shows how Machine Learning can be used effectively in practice to optimize recruitment costs in clinical trials.
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Affiliation(s)
- Manon Ansart
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
| | - Geoffroy Gagliardi
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
- AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Didier Dormont
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Bruno Dubois
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
| | - Harald Hampel
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
- AXA Research Fund & Sorbonne University Chair, Paris, France
- Sorbonne University, GRC no 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris, France
| | - Stanley Durrleman
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
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El Naqa I, Kosorok MR, Jin J, Mierzwa M, Ten Haken RK. Prospects and challenges for clinical decision support in the era of big data. JCO Clin Cancer Inform 2018; 2:CCI.18.00002. [PMID: 30613823 PMCID: PMC6317743 DOI: 10.1200/cci.18.00002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Recently, there has been burgeoning interest in developing more effective and robust clinical decision support systems (CDSSs) for oncology. This has been primarily driven by the demands for more personalized and precise medical practice in oncology in the era of so-called Big Data (BD); an era that promises to harness the power of large-scale data flow to revolutionize cancer treatment. This interest in BD analytics has created new opportunities as well as new unmet challenges. These include: routine aggregation and standardization of clinical data; patient privacy; transformation of current analytical approaches to handle such noisy and heterogeneous data; and expanded use of advanced statistical learning methods based on confluence of modern statistical methods and machine learning algorithms. In this review, we present the current status of CDSSs in oncology, the prospects and current challenges of BD analytics, and the promising role of integrated modern statistics and machine learning algorithms in predicting complex clinical endpoints, individualizing treatment rules, and optimizing dynamic personalized treatment regimens. We discuss issues pertaining to these topics and present application examples from an aggregate of experiences. We also discuss the role of human factors in improving the utilization and acceptance of such enhanced CDSSs and how to mitigate possible sources of human error to achieve optimal performance and wider acceptance.
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Affiliation(s)
- Issam El Naqa
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Michael R. Kosorok
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Judy Jin
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Michelle Mierzwa
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
| | - Randall K. Ten Haken
- Issam El Naqa, Judy Jin, Michelle Mierzwa, and Randall K. Ten Haken, University of Michigan, Ann Arbor, MI; and Michael R. Kosorok, University of North Carolina, Chapel Hill, NC
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Sundin I, Peltola T, Micallef L, Afrabandpey H, Soare M, Mamun Majumder M, Daee P, He C, Serim B, Havulinna A, Heckman C, Jacucci G, Marttinen P, Kaski S. Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge. Bioinformatics 2018; 34:i395-i403. [PMID: 29949984 PMCID: PMC6022689 DOI: 10.1093/bioinformatics/bty257] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Motivation Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large. Results We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach. Availability and implementation Source code implementing the introduced computational methods is freely available at https://github.com/AaltoPML/knowledge-elicitation-for-precision-medicine. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Iiris Sundin
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Tomi Peltola
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Luana Micallef
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Homayun Afrabandpey
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Marta Soare
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Muntasir Mamun Majumder
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Pedram Daee
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Chen He
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland
| | - Baris Serim
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland
| | - Aki Havulinna
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.,National Institute for Health and Welfare THL, Helsinki, Finland
| | - Caroline Heckman
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Giulio Jacucci
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland
| | - Pekka Marttinen
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Samuel Kaski
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
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7
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Barrett JE. Information-adaptive clinical trials with selective recruitment and binary outcomes. Stat Med 2017; 36:2803-2813. [PMID: 28585256 DOI: 10.1002/sim.7353] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Revised: 05/06/2017] [Accepted: 05/09/2017] [Indexed: 11/09/2022]
Abstract
Selective recruitment designs preferentially recruit individuals who are estimated to be statistically informative onto a clinical trial. Individuals who are expected to contribute less information have a lower probability of recruitment. Furthermore, in an information-adaptive design, recruits are allocated to treatment arms in a manner that maximises information gain. The informativeness of an individual depends on their covariate (or biomarker) values, and how information is defined is a critical element of information-adaptive designs. In this paper, we define and evaluate four different methods for quantifying statistical information. Using both experimental data and numerical simulations, we show that selective recruitment designs can offer a substantial increase in statistical power compared with randomised designs. In trials without selective recruitment, we find that allocating individuals to treatment arms according to information-adaptive protocols also leads to an increase in statistical power. Consequently, selective recruitment designs can potentially achieve successful trials using fewer recruits thereby offering economic and ethical advantages. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- James E Barrett
- Division of Health and Social Care Research, King's College London, London, SE1 1UL, U.K.,UCL Cancer Institute, University College London, London, WC1E 6DD, U.K
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