Addressing data sparsity in clinical trials – and female sparsity in data science

Addressing data sparsity in clinical trials – and female sparsity in data science

Digital Health Technologies (DHTs) are democratizing data collection during clinical trials, while promising to make research more efficient and more patient centric. However, shifting the power to input data from clinicians to participants, increases the risk of missed datapoints.

Where it occurs, this data sparsity can lead to incomplete submissions, threatening the success of otherwise highly promising drug candidates.

Speaking at a Women in Data Science (WIDS) Kenya conference, held at the Microsoft Africa Development Centre in Kenya in March, Pamela Adede, Data Operations Programmer at Phastar, explained the causes, consequences, and possible solutions to data sparsity in digital-era clinical trials – and why a woman’s place is in STEM.

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Site-level anomaly detection in multi-site clinical trials: A machine learning approach

At Phastar we have experience supporting study monitoring during clinical trials and our visualisation tools and expertise (see Delivering an Interim Analysis During a Pandemic from a Data Management perspective) successfully enable teams to monitor the data as it is collected during the trial. We explored whether machine learning could be utilised to support our monitoring activities, identifying unusual behaviour or data anomalies that are not easily detectable by a human.

Phastar data management teams, like others, monitor the trial sites to minimise the risk of poor-quality data. One approach they use is centralized and statistical monitoring where site data is evaluated for risks in real time from a single off-site location. Centralized and statistical monitoring can involve sophisticated visualisations and complex statistical algorithms to discover data outliers and anomalies. As part of the Centre For Analytical Excellence, we undertook a research project to explore methods to improve the detection of site level anomalies in multi-site clinical trials using machine learning. The goal would be to apply such an approach and use this to inform study teams unusual site behaviour for them to follow up accordingly.

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Phastar's Statistics Manager, Stephen Corson, wins prestigious DIA Award

Phastar's Statistics Manager, Stephen Corson, has been chosen by the Board of Directors and the Fellows of DIA to receive the DIA 2023 Global Inspire Award for Community Engagement. This prestigious award recognises the outstanding contribution of the DIA Community Chairs who consistently drive engagement and promote knowledge sharing, while advancing thought leadership within the membership community.

Stephen has been part of the DIA’s Statistics and Data Science Community, presenting at the DIA Global Annual Meetings for the past three years and sharing his vast knowledge of statistics. He will be delivering a half-day virtual workshop on 14th June from 13:00 – 16:00 and presenting at the upcoming DIA Annual Conference in Boston on 29th June from 8:30 – 9:30 track 11 statistics, providing a deeper understanding of the interpretation of common statistical terminology and knowledge to have effective discussions with statistical colleagues. Find out more about Stephen's presentation here.

This award is a testament to Stephen’s commitment to DIA’s mission, their stakeholders, and the patients they serve. Stephen will be presented with the award at the DIA Annual Conference on the evening of June 26th, 2023. Congratulations, Stephen!

Data science collaboration with - part two

Click here to read part one 

Phastar embarked on a collaboration project with to work on a data science project to automate the medical coding process for adverse events using machine learning approaches.

In clinical trials adverse events are coded using the MedDRA coding dictionary to standardize and allow consistent interpretation of results. There are five MedDRA classifications that each verbatim term (the term reported during the trial) needs to be mapped. Even with the aid of auto-coders this is a manual and time-consuming process that is prone to human error. The recent wider adoption of machine learning within clinical trials has led to the semi-automation of certain tasks to increase efficiency in the clinical trial process. The focus of this project was the verbatim mapping to the Lowest Level Term (LLT) in the MedDRA hierarchy.

The goal of the 8-week project was to ascertain if the auto-coding process could be improved with the application of Natural Language Processing (NLP) to the verbatim terms that would suggest a list of the most appropriate LLT for the verbatim term with a confidence interval for adjudication by the data manager.

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Season's greetings from Phastar's SAS Art competition winners!

Season's greetings from PHASTAR

Each year Phastar holds an internal competition among its programmers to create a work of festive art, using only their imagination and SAS software!

With so many exciting entries this year, our panel of judges was unable to choose a single winner, and decided to pick two winners instead! 

Take a look at our 2022 winners, and scroll down to reveal the SAS code if you'd like to try something similar yourself!

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