The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
In clinical development, a high-quality evidence package is a prerequisite for a new therapy to gain approval from regulators and other key decision-makers. As such, the quality of your clinical data is one of the key factors determining whether an effective new therapy reaches patients.
Implementing a data strategy can help to protect the quality of your evidence package. However, many companies start planning their strategy quite late in the development process, which makes it difficult to address (sufficiently address) the complex considerations involved. As we explore in our new eBook, a data strategy planned well in advance of starting Phase 1 and following the industry’s best practices can help you reduce risk, expedite clinical development, and successfully achieve your business objectives.
Download the new eBook, “Are you Harnessing the Power of your Clinical Data?” to find out how to optimize your data strategy to advance clinical development.
In our previous blog, we talked about the value of planning a data strategy for the entire duration of your program (i.e., a ‘program-wide’ strategy). However, it is also important to plan for specific phases of clinical development, because they each have unique challenges. Below we discuss the major challenges commonly encountered in Phase 1 and Phase 2 studies, and the tactics you can use to resolve them. An upcoming article will engage with challenges in Phase 3 and post-market.
In place of collecting data from patients recruited for a trial who have been assigned to the control or standard-of-care arm, an external control creates a comparator arm using either real-world data-sets such as electronic health records or previous clinical trials. The external control offers a practical, effective way to leverage real-world evidence and has been applied in regulatory approvals. In this blog, we share an illustrative example of how we can help customers in this emerging area of interest.
This article was originally published as part of a series by pharmaphorum in association with Cytel and is reproduced with their permission.
Ever felt like you ended up somewhere unexpected, not quite knowing what path you took to get there? This is the situation pharma companies can face without the right input from statisticians on clinical trial design – and the results can be very costly. As part of a series of articles in association with Cytel, pharmaphorum spoke with Natasa Rajicic about the vital role statisticians play in improving trial design and preventing expensive mistakes and trial failures.
The term biomarker signature describes the behavior of a set of biomarkers that define a signature to maximize the prediction performance. We examine the behavior of specific biomarkers as a set that consistently fluctuate together to maximize the accuracy on predicting the disease-related outcome.
How we apply a biomarker signature depends on the prediction problem. A prognostic biomarker signature is used to predict the disease progression, a risk biomarker signature is used to identify sets of subjects that are likely to develop a disease, and a predictive biomarker signature is used to determine the patients that are likely to respond to a particular treatment. Predictive biomarker signatures are used often in oncology to stratify patients with a specific cancer into sub-populations and develop targeted therapies for the diseased population subtypes defined by the biomarker signature.
In this blog, we share an example project that our data science team has worked on supporting this work. The case study forms part of a new ebook 'Innovative Data Science and Real-World Analytics Approaches in Practice' and we are also delighted to provide the link for download as part of the article.
Cytel recently hosted a very well-attended and engaging webinar on the topic of “Estimands, not just a statistical issue” presented by Paul Terrill, Associate Principal of Strategic Consulting at Cytel.
The webinar covered a range of issues from what is an estimand to how to structure early discussions on estimands.
In this blog, we are happy to share the replay of the webinar as well a summary of Q&As that arose on this very important topic. For an introduction to the topic, check out our previous blog post 'Estimands 101 with Mouna Akacha'.
By Nicolas Rouillé and Eric Henniger
The right design and the right data ultimately leads to the right decisions, so obtaining fit-for-purpose data, collected based on what your protocol is looking for is vital. However, there are several data pressure points facing oncology drug developers that need specialized expertise and processes to handle. In this blog, we run through some key aspects to consider to smooth your data collection and analysis.
The Cytel team made its annual trip to the PSI (Statisticians in the Pharmaceutical Industry) conference 2nd to 5th June. Taking place in London, UK, the theme of this year's meeting was Data-driven decision-making in medical research. As ever, the discussions both within the official conference agenda and during the networking breaks were engaging and productive.
In this blog, we share some of the particular highlights from the sessions that our team attended. We look forward to participating again in 2020 when the conference will return to Europe.
This article was originally published as part of a series by pharmaphorum in association with Cytel and is reproduced with their permission. Scott Harris, a four-time biotech Chief Medical Officer, and principal at Middleburg Consultants, a pharmaceutical consulting organization, told pharmaphorum’s Richard Staines that using novel adaptive or seamless clinical trial models can help to cut development costs. In doing so they can reduce the risks of trial failure that can spell the end for those biotech companies without the deep pockets of big pharma behind them.
In case you haven’t noticed, the traditional three-phase clinical development process is changing. While big late-stage trials are still pretty common, it’s also no longer a surprise to see sponsors refer to phase 1/2 trials, or phase 2/3, indicating that a smaller trial can be progressed to the next phase if an interim data readout supports further evaluation.
This is known as a “seamless” trial as the boundaries between each development stage have become less defined, and there are other options too.
Middleburg Consultants’ Scott Harris is a proponent of this new way of working and has personal experience of the approach after using it to steer a gastroenterology drug through the approval process.