Assessing the Impact of COVID-19 on the Clinical Trial Objective and Analysis of Oncology Clinical Trials—Application of the Estimand Framework

Degtyarev, Evgeny; Rufibach, Kaspar; Shentu, Yue; Yung, Godwin; Casey, Michelle; Englert, Stefan; Liu, Feng; Liu, Yi; Sailer, Oliver; Siegel, Jonathan; Sun, Steven; Tang, Rui; Zhou, Jia

doi:10.1080/19466315.2020.1785543

Cited by 28 publications

(28 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A pandemic will not typically change estimand attributes A, B, C and E, although there will be exceptions [14,15]. It is most likely to introduce a number of new intercurrent (post-randomisation) events, which are defined as events affecting the interpretation or existence of trial outcomes [6,11,14]. A pandemic may directly affect participant outcomes if they become infected with the underlying disease (e.g.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic

Cro

Morris

Kahan

et al. 2020

BMC Med Res Methodol

View full text Add to dashboard Cite

Background: The coronavirus pandemic (Covid-19) presents a variety of challenges for ongoing clinical trials, including an inevitably higher rate of missing outcome data, with new and non-standard reasons for missingness. International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking. Methods: We present a four-step strategy for handling missing outcome data in the analysis of randomised trials that are ongoing during a pandemic. We consider handling missing data arising due to (i) participant infection, (ii) treatment disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a 'pandemicfree world' and 'world including a pandemic' are of interest. Results: In any trial, investigators should; (1) Clarify the treatment estimand of interest with respect to the occurrence of the pandemic; (2) Establish what data are missing for the chosen estimand; (3) Perform primary analysis under the most plausible missing data assumptions followed by; (4) Sensitivity analysis under alternative plausible assumptions. To obtain an estimate of the treatment effect in a 'pandemic-free world', participant data that are clinically affected by the pandemic (directly due to infection or indirectly via treatment disruptions) are not relevant and can be set to missing. For primary analysis, a missing-at-random assumption that conditions on all observed data that are expected to be associated with both the outcome and missingness may be most plausible. For the treatment effect in the 'world including a pandemic', all participant data is relevant and should be included in the analysis. For primary analysis, a missing-at-random assumptionpotentially incorporating a pandemic time-period indicator and participant infection statusor a missing-not-at-random assumption with a poorer response may be most relevant, depending on the setting. In all scenarios, sensitivity analysis under credible missing-not-at-random assumptions should be used to evaluate the robustness of results. We highlight controlled multiple imputation as an accessible tool for conducting sensitivity analyses. Conclusions: Missing data problems will be exacerbated for trials active during the Covid-19 pandemic. This four-step strategy will facilitate clear thinking about the appropriate analysis for relevant questions of interest.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The most appropriate estimand of interest will be trial specific and should be guided by the question of key clinical interest [14,15]. For many trials there may be more than one estimand of interest.…”

Section: Resultsmentioning

confidence: 99%

A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic

Cro

Morris

Kahan

et al. 2020

BMC Med Res Methodol

View full text Add to dashboard Cite

show abstract

“…Due to wide-reaching effects of COVID-19 and its unique interaction with the microbiome, it is important to consider how representative of the target population participants have become. Characteristics of patients enrolled before, during and after the pandemic may now be systematically different ( 48 ). These characteristics extend at least to the microbiota composition and diversity, but there may be more subtle changes.…”

Section: The Effect Of Covid-19 On Microbiome Studiesmentioning

confidence: 99%

The Unique Impact of COVID-19 on Human Gut Microbiome Research

et al. 2021

View full text Add to dashboard Cite

The coronavirus (COVID-19) pandemic has disrupted clinical trials globally, with unique implications for research into the human gut microbiome. In this mini-review, we explore the direct and indirect influences of the pandemic on the gut microbiome and how these can affect research and clinical trials. We explore the direct bidirectional relationships between the COVID-19 virus and the gut and lung microbiomes. We then consider the significant indirect effects of the pandemic, such as repeated lockdowns, increased hand hygiene, and changes to mood and diet, that could all lead to longstanding changes to the gut microbiome at an individual and a population level. Together, these changes may affect long term microbiome research, both in observational as well as in population studies, requiring urgent attention. Finally, we explore the unique implications for clinical trials using faecal microbiota transplants (FMT), which are increasingly investigated as potential treatments for a range of diseases. The pandemic introduces new barriers to participation in trials, while the direct and indirect effects laid out above can present a confounding factor. This affects recruitment and sample size, as well as study design and statistical analyses. Therefore, the potential impact of the pandemic on gut microbiome research is significant and needs to be specifically addressed by the research community and funders.

show abstract

“…Strategies for handling treatment discontinuation are summarised in Table 3. We differentiate between treatment discontinuation due to internal factors [22], such as adverse events, problems with the route of administration, or a clinician's decision to discontinue for end-of-life care, and discontinuation due to external factors [22], for example, because drug supply issues mean the treatment is no longer available or a lack of personal protective equipment means that clinicians cannot administer the treatment as intended.…”

Section: Healthcare Systems Perspectivementioning

confidence: 99%

Treatment estimands in clinical trials of patients hospitalised for COVID-19: ensuring trials ask the right questions

Kahan

Morris

White

et al. 2020

BMC Med

View full text Add to dashboard Cite

When designing a clinical trial, explicitly defining the treatment estimands of interest (that which is to be estimated) can help to clarify trial objectives and ensure the questions being addressed by the trial are clinically meaningful. There are several challenges when defining estimands. Here, we discuss a number of these in the context of trials of treatments for patients hospitalised with COVID-19 and make suggestions for how estimands should be defined for key outcomes. We suggest that treatment effects should usually be measured as differences in proportions (or risk or odds ratios) for outcomes such as death and requirement for ventilation, and differences in means for outcomes such as the number of days ventilated. We further recommend that truncation due to death should be handled differently depending on whether a patient- or resource-focused perspective is taken; for the former, a composite approach should be used, while for the latter, a while-alive approach is preferred. Finally, we suggest that discontinuation of randomised treatment should be handled from a treatment policy perspective, where non-adherence is ignored in the analysis (i.e. intention to treat).

show abstract

Assessing the Impact of COVID-19 on the Clinical Trial Objective and Analysis of Oncology Clinical Trials—Application of the Estimand Framework

Cited by 28 publications

References 27 publications

A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic

A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic

The Unique Impact of COVID-19 on Human Gut Microbiome Research

Treatment estimands in clinical trials of patients hospitalised for COVID-19: ensuring trials ask the right questions

Contact Info

Product

Resources

About