Many thanks to Grammati Sarri and Michael Groff for their comments in developing this blog.
An indirect treatment comparison compares two trials A and B, by first comparing the results of Trial A to Trial C, and then comparing the results of Trial C to Trial B. Unlike head-to-head comparisons where these trials (A and B) would be directly compared with each other, indirect treatment comparisons, or ITCs, utilize at least one other trial to create a network for comparison.
Often, there are fundamental differences between the trials, such as population, or the interaction of the treatment with features of the population (e.g., age, sex, etc.). These are either caused by prognostic variables or by effect modifiers. The methods with which we handle the complexities raised by each differ slightly. Population-Adjusted Indirect Treatment Comparisons (PAICs) use individual patient data from one or more trials to adjust for characteristics of patients and ensure populations across all trials are reasonably comparable.
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The ability to draw on electronic medical records is foundational to the use of real-world data (RWD). In China, companies like HappyLife Tech (HLT), an affiliated company of Yidu Tech Inc., can build digital ecosystems with 1.3 billion data records and over 300 million patients, thereby opening new horizons for real world solutions. Such solutions have the potential to improve every stage of the product lifecycle and study journey including feasibility studies, clinical trial design, and market access. They also open new development pathways including those for decentralized trials and AI-driven solutions. According to Dr. Luyan Connie Dai in her recent webinar‚ “Embrace the Era of Digital Clinical Trials in China: Challenges & Opportunities” there are seven key ways in which RWD is transforming the product journey in China.
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Returning to Cytel after the winter holidays, I am excited to begin a year that will likely prove memorable for both my colleagues at Cytel and the industry at large. In 2022, Cytel laid the foundations for many projects that will bear fruit this year. Read more »
When evaluating the efficacy of a candidate investigational therapy, a standard clinical trial paradigm is to conduct a two-arm randomized trial, typically evaluating that therapy against a control arm. And if multiple therapies are being evaluated against the same disease, there may be multiple parallel two-arm trials. However, this creates redundancies, wasting time and resources.
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Written by Jing Ping Yeo and Charles Warne
Adaptive designs are studies that “include a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of data (usually interim data) from subjects in the study, “1 with the aim to provide an opportunity to gather more complete information about the therapeutic intervention from interim analyses. This lessens the unpredictability and reduces the risk of failure of Phase III confirmatory trials due to safety and efficacy concerns. In a traditional fixed-trial approach, the trial is designed and conducted as prescribed by the design until the end of the study when the final data are analyzed. Phases of clinical research are conducted as sequential, stand-alone studies from first-in-human Phase I dose escalation, to Phase II dose-finding, until final Phase III confirmatory trials leading to marketing approval. In adaptive trials, scheduled interim analyses are performed to learn from the accumulating data and make necessary modifications while the trial is ongoing, maintaining the study’s integrity and the validity of final conclusions. The most frequently appearing types of adaptations are seamless Phase II/III (57%), group sequential (21%), biomarker adaptive (20%), and adaptive dose-finding designs (16%).
In a recent DIA-Singapore conference presentation, Cytel biostatistician Charles Warne further elaborated on the three types:
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Written by Natalia Muehlemann, Vice President, Clinical Development, and Ari Brettman, Senior Managing Director, Blackstone Life Sciences
When working with investors, it is critical that drug and device developers consider how their clinical trial design aligns with investment priorities. Natalia Muehlemann and Ari Brettman discuss what investors look for when evaluating clinical development programs, and the value of simulation-guided design in informing decisions and facilitating communication with investors. Let’s take a closer look:
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