Dataset Information

The Randomized CRM: An Approach to Overcoming the Long-Memory Property of the CRM.

ABSTRACT: The primary object of a Phase I clinical trial is to determine the maximum tolerated dose (MTD). Typically, the MTD is identified using a dose-escalation study, where initial subjects are treated at the lowest dose level and subsequent subjects are treated at progressively higher dose levels until the MTD is identified. The continual reassessment method (CRM) is a popular model-based dose-escalation design, which utilizes a formal model for the relationship between dose and toxicity to guide dose finding. Recently, it was shown that the CRM has a tendency to get "stuck" on a dose level, with little escalation or de-escalation in the late stages of the trial, due to the long-memory property of the CRM. We propose the randomized CRM (rCRM), which introduces random escalation and de-escalation into the standard CRM dose-finding algorithm, as well as a hybrid approach that incorporates escalation and de-escalation only when certain criteria are met. Our simulation results show that both the rCRM and the hybrid approach reduce the trial-to-trial variability in the number of cohorts treated at the MTD but that the hybrid approach has a more favorable tradeoff with respect to the average number treated at the MTD.

SUBMITTER: Koopmeiners JS

PROVIDER: S-EPMC5581285 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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The Randomized CRM: An Approach to Overcoming the Long-Memory Property of the CRM.

Koopmeiners Joseph S JS Wey Andrew A

Journal of biopharmaceutical statistics 20170325 6

The primary object of a Phase I clinical trial is to determine the maximum tolerated dose (MTD). Typically, the MTD is identified using a dose-escalation study, where initial subjects are treated at the lowest dose level and subsequent subjects are treated at progressively higher dose levels until the MTD is identified. The continual reassessment method (CRM) is a popular model-based dose-escalation design, which utilizes a formal model for the relationship between dose and toxicity to guide dos ...[more]

PMID: 28340333

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Project description:BackgroundAn allergic reaction is the immune system's overreacting to a previously encountered, typically benign molecule, frequently a protein. Allergy reactions can result in rashes, itching, mucous membrane swelling, asthma, coughing, and other bizarre symptoms. To anticipate allergies, a wide range of principles and methods have been applied in bioinformatics. The sequence similarity approach's positive predictive value is very low and ineffective for methods based on FAO/WHO criteria, making it difficult to predict possible allergens.MethodThis work advocated the use of a deep learning model LSTM (Long Short-Term Memory) to overcome the limitations of traditional approaches and machine learning lower performance models in predicting the allergenicity of dietary proteins. A total of 2,427 allergens and 2,427 non-allergens, from a variety of sources, including the Central Science Laboratory and the NCBI are used. The data was divided 80:20 for training and testing purposes. These techniques have all been implemented in Python. To describe the protein sequences of allergens and non-allergens, five E-descriptors were used. E1 (hydrophilic character of peptides), E2 (length), E3(propensity to form helices), E4(abundance and dispersion), and E5 (propensity of beta strands) are used to make the variable-length protein sequence to uniform length using ACC transformation. A total of eight machine learning techniques have been taken into consideration.ResultsThe Gaussian Naive Bayes as accuracy of 64.14 %, Radius Neighbour's Classifier with 49.2 %, Bagging Classifier was 85.8 %, ADA Boost was 76.9 %, Linear Discriminant Analysis has 76.13 %, Quadratic Discriminant Analysis was 84.2 %, Extra Tree Classifier was 90%, and LSTM is 91.5 %.ConclusionAs the LSTM, has an AUC value of 91.5 % is regarded best in predicting allergens. A web server called ProAll-D has been created that successfully identifies novel allergens using the LSTM approach. Users can use the link https://doi.org/10.17632/tjmt97xpjf.1 to access the ProAll-D server and data.

Dataset Information

The Randomized CRM: An Approach to Overcoming the Long-Memory Property of the CRM.

Publications

The Randomized CRM: An Approach to Overcoming the Long-Memory Property of the CRM.

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