In our final blog of the series, we examine the impact of missing data caused by the COVID-19 pandemic on clinical trials. It is generally accepted that there will be an increased amount of missing data in most clinical trials due to the COVID-19 pandemic. Missing data should be anticipated and the appropriate methods of dealing with it need to be in place. Missing data and premature termination are not new to statisticians and well-established methods exist on how to deal with these issues, but the disruptions of the COVID-19 pandemic to clinical trials are expected to be so diverse that no single solution will be appropriate for all trials.
As per regulatory guidance, all missed visits and assessments will need to be captured and summarised in the tables, figures and listings (TFLs) and CSR. This should include an assessment of the reasons for the missing data, the impact of missed visits on a case-by-case basis and for each endpoint in the trial. In general, missed visits will be classified as missing or censored (for time-to-event endpoints) in the statistical analyses.
The extent of missing data and its impact on the trial will need to be assessed as part of the risk assessment process. It is likely that the mechanism of missingness caused by the pandemic will be at random (either missing completely at random or missing at random) but it will still be necessary to further investigate and understand the source of the missing data. An imbalance of missingness between treatment arms can be of concern and it may be appropriate to utilise an IDMC to investigate the imbalance. Since trial integrity is of primary concern, any data monitoring should be on baseline and safety data rather than efficacy data in order to avoid inflating the Type I error.
In our latest blog of the series, we examine the topic of estimands with respect to clinical trials affected by the COVID-19 pandemic. When evaluating whether the primary objective of the trial is affected by the pandemic, it is necessary to consider the treatment effect in the absence of the COVID-19 pandemic and not confounded by pandemic-related events. This should be done via the estimands framework, looking at each of the five attributes of the estimand. It is critical to ensure that it is still possible to estimate the treatment effects aligned to the objectives and that the planned methods of analyses are still fully aligned to the estimands.
The Estimands Framework
Treatment Condition of Interest: This should remain the same as originally intended, although the pandemic may cause operational and logistical issues with study drug supply and delivery. Any pandemic-related issues with adherence to study drug and concomitant medications should be considered as intercurrent events.
Population: This should remain as originally intended to avoid deviating from the primary objective of the trial. However, consideration should be given to address the potential effect of COVID-19 vaccines, particularly in multi-region trials as COVID-19 vaccines became available at different times.
Variable: In most cases, the endpoint that is used to address the clinical question should remain as originally intended. If alternative data collection methods are required for the endpoint, it must be ensured that the endpoint is not compromised. All alternative data collection methods that are employed should be assessed for their potential effects on endpoint variability. Any different methods of collection that are used need to be comparable.
In this blog series, we examine the impact of COVID-19 on the conduct, analysis, and interpretation of ongoing clinical trials. The safety of trial participants and all staff involved remains the primary concern in all clinical trials. The key message has always been (and remains) that clinical trials are critical and should continue during the COVID-19 pandemic, if it was safe to do so.
If it is safe for a trial to continue, it is important to understand and mitigate against the impact of the pandemic. For all clinical trials affected by the COVID-19 pandemic, the first step should be to perform a risk assessment to assess the impact to the trial and the ability of the trial to still meet its planned objectives. In blinded trials, the risk assessment should be performed on blinded data. This is an ongoing process and should be conducted on a regular basis throughout the duration of the trial. Once the risks are understood, contingency measures/mitigation can then be looked at. It will be necessary to document any changes made to the trial in the protocol and the statistical analysis plan (SAP) and the impact of COVID-19 will need to be addressed in the clinical study report (CSR). It is recommended to discuss and interact with regulatory agencies as early as possible.
Throughout this blog series, we will be discussing how the COVID-19 pandemic situation has affected clinical trials, focussing on the pertinent statistical issues and associated key recommendations. Rather than providing an exhaustive summary of all potential issues and possible solutions, the aim is to concentrate on the key areas and questions that statisticians on project teams should be asking and thinking about. Whilst there are important aspects regarding safety data, the focus of this series is on the considerations for efficacy data and so the main points covered are estimands and missing data
The World Health Organisation (WHO) declared COVID-19 a global pandemic on 11th March 2020 and the subsequent restrictions on everyday life caused significant disruption to clinical trials. The full extent of the impact of COVID-19 will not be known for some time but the effect on clinical trials will most certainly be significant. Although sponsors have been quick to make changes to clinical trials there will undoubtably still be many issues to address. The range of disruptions of COVID-19 to clinical trials are expected to be so diverse that no single solution will be appropriate for all trials. It is likely that each trial will need to be assessed on a case-by-case basis, although there will be many common issues. It is critical for sponsors and regulators to work together and make the best use of clinical trials affected by COVID-19. Collaboration and sharing of learnings during the pandemic are going to be vitally important for a long time to come.
Written by Prof. Jennifer Rogers on . Posted in Blog
On 22nd October, PHASTAR’s Vice President for Statistical Research and Consultancy, Jennifer Rogers, appeared on BBC Radio 5 Live (Click Here for Radio 5 Live Interviewwhich starts at 12:50) to discuss the latest COVID-19 statistics and one of the questions that she was asked considered the expected split of vaccinated versus unvaccinated people in hospital throughout the potential “winter wave”. Here Jen looks at hospitalisation data for vaccinated versus unvaccinated people in more detail and argues that the wrong questions are being asked.
On the 19th July, Sir Patrick Vallance announced that 60% of people in hospital from COVID-19 in the UK were not from double vaccinated people (after a brief bit of confusion when he mistakenly said in the press conference that 60% of people in hospital were double vaccinated). This means that 40% of people in hospital from COVID-19 were double vaccinated. Following this, we have seen the percentage of people in hospital being vaccinated versus unvaccinated be repeatedly reported as a useful statistic.
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