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1. Predictive modeling of clinical trial terminations using feature engineering and embedding learning

   https://doi.org/10.1038/s41598-021-82840-x  

   Elkin, Magdalyn E. ; Zhu, Xingquan ( December 2021 , Scientific Reports)

   null (Ed.)

   Abstract In this study, we propose to use machine learning to understand terminated clinical trials. Our goal is to answer two fundamental questions: (1) what are common factors/markers associated to terminated clinical trials? and (2) how to accurately predict whether a clinical trial may be terminated or not? The answer to the first question provides effective ways to understand characteristics of terminated trials for stakeholders to better plan their trials; and the answer to the second question can direct estimate the chance of success of a clinical trial in order to minimize costs. By using 311,260 trials to build a testbed with 68,999 samples, we use feature engineering to create 640 features, reflecting clinical trial administration, eligibility, study information, criteria etc. Using feature ranking, a handful of features, such as trial eligibility, trial inclusion/exclusion criteria, sponsor types etc. , are found to be related to the clinical trial termination. By using sampling and ensemble learning, we achieve over 67% Balanced Accuracy and over 0.73 AUC (Area Under the Curve) scores to correctly predict clinical trial termination, indicating that machine learning can help achieve satisfactory prediction results for clinical trial study. 

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2. Understanding and predicting COVID-19 clinical trial completion vs. cessation

   https://doi.org/10.1371/journal.pone.0253789  

   Elkin, Magdalyn E. ; Zhu, Xingquan ( July 2021 , PLOS ONE)

   Gadekallu, Thippa Reddy (Ed.)

   As of March 30 2021, over 5,193 COVID-19 clinical trials have been registered through Clinicaltrial.gov. Among them, 191 trials were terminated, suspended, or withdrawn (indicating the cessation of the study). On the other hand, 909 trials have been completed (indicating the completion of the study). In this study, we propose to study underlying factors of COVID-19 trial completion vs . cessation, and design predictive models to accurately predict whether a COVID-19 trial may complete or cease in the future. We collect 4,441 COVID-19 trials from ClinicalTrial.gov to build a testbed, and design four types of features to characterize clinical trial administration, eligibility, study information, criteria, drug types, study keywords, as well as embedding features commonly used in the state-of-the-art machine learning. Our study shows that drug features and study keywords are most informative features, but all four types of features are essential for accurate trial prediction. By using predictive models, our approach achieves more than 0.87 AUC (Area Under the Curve) score and 0.81 balanced accuracy to correctly predict COVID-19 clinical trial completion vs . cessation. Our research shows that computational methods can deliver effective features to understand difference between completed vs . ceased COVID-19 trials. In addition, such models can also predict COVID-19 trial status with satisfactory accuracy, and help stakeholders better plan trials and minimize costs. 

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3. Statistical Significance: Reliability of P-Values Compared to Other Statistical Summaries

   https://doi.org/10.32474/CTBB.2019.02.000128  

   Zawada, Jacqueline ; Kolassa, John ; Seifu, Yodit ( December 2019 , Current trends on biostatistics biometrics)

   Statistical inference has strongly relied on the use of p-values to draw conclusions. For over a decade this reliance on the p-value has been questioned by researches and academics. The question of whether p-values are truly the best standard, and what other possible statistics could replace p-values l has been discussed deeply. We set out to understand the amount of variation within p-values, and to find if they really are as reliable as the frequency of their use would suggest. To answer this question, we studied a set of clinical trials over the past two years. We also aim to describe the variety of information included in drag labels, and determine whether this information conforms to FDA guidelines. We found a large variation in the presentation of clinical trial data, much of which was not in line with the guidelines of the FDA. Our findings also show that among the clinical trials we studied there is more variation among the p-values than among the estimates. From this, we can conclude that the estimates from clinical trials should hold a heavy weight in the decision of whether or not to approve the drug. This finding suggests that there is validity to the skepticism of the reliance on p-values, and that further studies need to be done to find a new, more reliable, standard in statistical inference. 

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4. STAN: spatio-temporal attention network for pandemic prediction using real-world evidence

   https://doi.org/10.1093/jamia/ocaa322  

   Gao, Junyi ; Sharma, Rakshith ; Qian, Cheng ; Glass, Lucas M. ; Spaeder, Jeffrey ; Romberg, Justin ; Sun, Jimeng ; Xiao, Cao ( January 2021 , Journal of the American Medical Informatics Association)

   We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients’ claims data from different counties and states that capture local disease status and medical resource utilization; (2) utilizing demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into a deep learning model.

   We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses a graph attention network to capture spatio-temporal trends of disease dynamics and to predict the number of cases for a fixed number of days into the future. We also designed a dynamics-based loss term for enhancing long-term predictions. STAN was tested using both real-world patient claims data and COVID-19 statistics over time across US counties.

   STAN outperforms traditional epidemiological models such as susceptible-infectious-recovered (SIR), susceptible-exposed-infectious-recovered (SEIR), and deep learning models on both long-term and short-term predictions, achieving up to 87% reduction in mean squared error compared to the best baseline prediction model.

   By combining information from real-world claims data and disease case counts data, STAN can better predict disease status and medical resource utilization.

    

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5. Trialstreamer: A living, automatically updated database of clinical trial reports

   https://doi.org/10.1093/jamia/ocaa163  

   Marshall, Iain J ; Nye, Benjamin ; Kuiper, Joël ; Noel-Storr, Anna ; Marshall, Rachel ; Maclean, Rory ; Soboczenski, Frank ; Nenkova, Ani ; Thomas, James ; Wallace, Byron C ( September 2020 , Journal of the American Medical Informatics Association)

   null (Ed.)

   Abstract Objective Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in health care but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports. Materials and Methods Trialstreamer continuously monitors PubMed and the World Health Organization International Clinical Trials Registry Platform, looking for new RCTs in humans using a validated classifier. We combine machine learning and rule-based methods to extract information from the RCT abstracts, including free-text descriptions of trial PICO (populations, interventions/comparators, and outcomes) elements and map these snippets to normalized MeSH (Medical Subject Headings) vocabulary terms. We additionally identify sample sizes, predict the risk of bias, and extract text conveying key findings. We store all extracted data in a database, which we make freely available for download, and via a search portal, which allows users to enter structured clinical queries. Results are ranked automatically to prioritize larger and higher-quality studies. Results As of early June 2020, we have indexed 673 191 publications of RCTs, of which 22 363 were published in the first 5 months of 2020 (142 per day). We additionally include 304 111 trial registrations from the International Clinical Trials Registry Platform. The median trial sample size was 66. Conclusions We present an automated system for finding and categorizing RCTs. This yields a novel resource: a database of structured information automatically extracted for all published RCTs in humans. We make daily updates of this database available on our website (https://trialstreamer.robotreviewer.net). 

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