There are multiple difficulties seen in data integration (ISS/ISE), especially with the quality of data input into the integration. Quality should/must be everyone’s responsibility at all levels. After all, a study is only as good as its data and the practices used to interpret that data. As programmers, we have a unique role in this process as we often review the study data at a detailed level over the course of a study, review and approve data input specifications for completeness, and work with the study team to consolidate and the clean data in preparation for analysis. With some proper processes and planning, we can work to resolve data issues and questions early lest they result in rework later in the programming process.
Any single clinical study can present numerous challenges and the same can certainly be true for an Integrated Summary of Effectiveness (ISE). An ISE takes several clinical studies and pulls (or pools) them together to present a coherent and hopefully compelling story of a drug or treatment’s effectiveness. Importantly, an ISE is also considered important for explaining the differences in results between the individual studies. Why might one study have been more or less effective than another – is it because of the differences in doses between studies or perhaps the subset of the target population that was included? From a statistical perspective, an ISE might be able to answer interesting questions that not only provide a better understanding of the treatment’s efficacy overall, but the greater power of pooled analyses may offer additional insight into the nature of the treatment’s effectiveness in demographic and other subpopulations.
I always feel a huge sense of responsibility when involved in an Independent Data Monitoring Committee (IDMC) in any capacity but being the Independent Data Analysis Centre (IDAC) for a recent COVID-19 study certainly amplified those feelings greatly. PHASTAR recently successfully supported a large pharmaceutical company in their IDAC requirements for a COVID-19 study. It was an honour to be involved in such an important piece of work that already has and will continue to save many people’s lives.
Many COVID-19 studies have been set up at breakneck speed and this trial was no different. There are many very good reasons why clinical trials typically take a long time to set up. They require huge levels of detailed thinking, organisation and of course a lot of preparation. But during the pandemic, there has been an urgent need to get treatments to people as quickly as possible whilst maintaining trial integrity and not compromising subject safety. This has not been an easy task. This COVID-19 study (as I’m sure is also true with many others) also had accelerated timelines all round. Whilst a typical IDAC may be given a number of days, or even weeks to prepare the blinded and unblinded package for communication with the IDMC members, in this instance PHASTAR was given only 24 hours in which to turn the analysis around, with the IDMC meetings typically only 2-3 weeks apart.
When the World Health Organisation (WHO) declared the COVID-19 pandemic on 11th March 2020, countries the world over implemented restrictions to manage this accordingly. As well as challenges faced by people and communities around the globe, the pandemic presented unique challenges for the clinical trials community, with many existing studies being either temporarily paused and / or adapting their process.
Interim analysis provides several options and opportunities for sponsors, including modifications of the trial design and adaptive designs, monitoring the safety of the trials with DSMB’s, post marketing surveillance and long-term follow up and monitoring if the trial is going to go ahead as originally intended (i.e. sample size). The statistical output for an Interim Analysis may include (but is not limited to) tables, figures and listings, data listings, interim study reports, IDMC and DSMB requirements. The Interim Analysis may be blinded, partially blinded, unblinded, or will use a “dummy” randomisation schedule.