A great many new and varied approaches to clinical trial management are being adopted during the COVID-19 pandemic through the help of virtualization tools, strong partnerships, and regulatory guidance. Despite the upheaval this year and last, there appears to be a silver lining largely due to the systemic changes leading to the remarkably quick development in adapting trials to accommodate different environments and the incredible speed at which COVID-19 vaccines have been developed and administered. Regulatory guidance has accommodated this abrupt shift. In this article, we will cover the key factors needed prior to adapting to patient-centric clinical trials.
For starters, there are quite a few differences regarding attaining and disseminating patient-level data in a decentralized or remote trial setting versus the traditional way an in-person study is designed. Telemedicine or remote visits, for instance, have traditionally only been used for patient-physician consultations in the health care setting. However, the value of telemedicine for use in clinical trials has grown ever more promising due to the greater access to research and reduced attrition it can deliver.
Data collection methods tend to be the primary component that changes with decentralized clinical trials in comparison to in-person studies. With remote trials, the engagement with digital technology for data capture provides the potential to receive information at a higher frequency, which means more data is available.
Omicron has been a real step change for COVID-19 with reports of it being a highly transmissible variant of the virus, but potentially not as serious. On the 27th November the UK Government published that the first cases of Omicron had been identified in the UK. Following this, we saw a huge increase in cases with the 7-day average peaking on the 2nd January at 183,084. Just to put this figure into perspective, prior to the Omicron wave, the next largest figure that we’d seen for the 7-day cases average was 59,660. The media was full of experts telling us that even though Omicron was suspected to be less serious than other variants, we were still likely to see relatively big increases in hospitalisations and deaths simply due to the huge number of Omicron cases.
Let’s say, for example, that we have a variant A that has a hospitalisation rate of 10% and variant B that has a hospitalisation rate of 5% (so half the hospitalisation rate). If variant A has 59,660 cases, this would give us an expected number of hospitalisations of 5,966. If variant B has 183,084 cases, this would give us an expected number of hospitalisations of 9,154. So theoretically, we could still have a problem on our hands.
Hospital admissions did increase, but these now look to be levelling off and deaths now look to be on a steep upward trajectory. But what makes a COVID-19 hospitalisation a COVID-19 hospitalisation? And what makes a COVID-19 death a COVID-19 death? Looking into this in more detail suggests that things may be more optimistic than the daily reported number of hospital admissions and deaths suggest.
Let’s look at the definitions for COVID-19 hospital admission and COVID-19 death. Hospital admissions includes all admissions with a positive COVID-19 test. So, if you went into hospital with a broken leg or needed to be admitted into hospital with appendicitis, but you happened to test positive for COVID-19 at the time, you would be formally defined as a COVID-19 hospital admission. This article in The Guardian quotes Chris Hopson, Chief Executive of NHS Providers, who said “What our guys are saying is that incidental cases are about 25% to 30% of cases arriving … They are seeing an increase in the number of hospital admissions, but it’s not going up in an exponential way,”
Similarly, COVID-19 deaths are defined as a death where the person tested positive for COVID-19 within the preceding 28 days. I actually tested positive for COVID-19 26 days ago. If I happened to be knocked over by a car whilst out walking my dogs today, I would officially be recorded as a COVID-19 death on the UK Government COVID-19 dashboard. As the number of COVID-19 cases increases, and if these cases are in fact milder, the proportion of COVID-19 hospital admissions and COVID-19 deaths that are “true COVID” will decrease. There will be a higher likelihood of people who happen to have COVID-19 being admitted to hospital for other reasons and a higher likelihood of people dying from an unrelated reason in the 28 days following a positive COVID-19 test result.
But there are more reliable metrics that we can look at. I always view the number of COVID-19 occupied mechanical ventilation beds as a more consistent estimate of the impact of severe COVID-19. We can see from Figure 1 that Omicron has not resulted in an increase in these figures. In fact, these numbers seem to be on a slow and steady downward trajectory. That’s not to say that Omicron has not had an impact on healthcare services. Having to separate COVID-19 positive patients is undoubtably a burden on hospitals. And an increase in Omicron cases has also resulted in an increase in staff absences. But the number of people being admitted into hospital because of COVID-19 may not have increased as suggested by the UK Government COVID-19 dashboard.
Figure 1: COVID-19 Occupied Mechanical Ventilation Beds
Likewise, the Office for National Statistics (ONS) collects the number of deaths where COVID-19 is reported on the death certificate as a cause of death. This information is also presented on the UK Government COVID-19 dashboard but is slower to collect and process. The latest data only goes up until the 31st December (Figure 2), so whilst we should have started to see the effects of Omicron by this point, it could be the next few weeks before we see its full impact. Typically, we assume a 14-day lag between COVID-19 cases and deaths, so we should have started to see the impact of Omicron already and the data looks to be declining in line with the number of COVID-19 occupied mechanical ventilation beds. We will need to wait to see if this trend will continue.
Figure 2: Daily deaths with COVID-19 on the death certificate by date of death
Why do we have so many different death counts? Surely a COVID-19 death is a COVID-19 death. How can we have different deaths? And why are these ONS figures two weeks old? The ONS remains the gold standard as they must be certified by a doctor, registered, and processed. But this also makes them the slowest. The daily counts that we have become accustomed to seeing every day only include deaths in hospital of those who have tested positive for COVID-19 within the last 28 days. This makes them much quicker to collect, but also less accurate. The COVID-19 pandemic has remained a rapidly changing situation and there will always be a balancing act between getting access to “dirty” data quickly and having the most accurate data available but having to wait for it.
The next week will be interesting as we wait to see if the hospitalisation data continues to come down and we keep our eye on the death data. As discussed in this PHASTAR blog, the rules for PCR testing have changed and this could theoretically be influencing case numbers. The coming weeks will be very interesting to see what happens to the data.
From the 11th January, COVID-19 testing rules are to be relaxed in England for people without symptoms. A positive lateral flow test (LFT) result, will no longer need to be confirmed with a follow-up positive polymerase chain reaction (PCR) test. Those with a positive LFT will still need to isolate, they just won’t need the extra PCR test. Firstly, what are the differences between PCR and LFTs?
PCR tests work by looking for a small genetic fragment of the COVID-19 virus and then using PCR technology in a lab to create multiple copies. The lower the number of cycles it takes to find the virus, the stronger the infection. There is a threshold which determines whether the signal is too weak to be an active virus. PCR testing has the ability to pick up tiny amounts of the virus, meaning that a person can test positive before they have a viral load high enough to be infectious and it’s also possible to test positive via a PCR test a while after infectiousness has ended.
LFTs work by placing a sample on a porous strip that has a line of antibodies designed to bind to the COVID-19 virus. When this binding occurs, the line changes colour, indicating a positive test. LFTs are not as good at detecting the virus as PCR tests, but their value lies in the speed at which they give results. A LFT gives a result in 30 minutes, compared with PCR tests which must be sent away to a lab for testing. LFTs do adequately pick up people while they are at their most infectious, however. LFTs have also been a crucial tool in picking up asymptomatic COVID-19 through routine regular testing.
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.