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

Exploring the effect of COVID-19 restrictions on the social functioning scale in a clinical trial of antipsychotic reduction: using multiple imputation to target a hypothetical estimand

OBJECTIVES

Many trials are affected by unforeseen events after recruitment has commenced. The aim of this study is to explore a hypothetical strategy for dealing with an intercurrent event that occurred during trial follow-up; COVID-19 restrictions.

STUDY DESIGN AND SETTING

Secondary analysis of a randomized controlled trial (RCT) in schizophrenia, comparing antipsychotic reduction vs maintenance medication on the social functioning scale (SFS) score at 12 months' follow-up. A hypothetical analysis strategy was used to estimate the treatment effect in a COVID-19 restriction-free world. Outcome data were set to missing, and multiple imputation was used to replace values affected by COVID-19.

RESULTS

The trial randomized 253 participants, 187 participants had an SFS score at 12 months, and 75 of those were collected during COVID-19 restrictions. In the original complete case regression analysis, targeting a treatment policy estimand, the treatment effect was estimated to be 0.51 (95% CI -1.33, 2.35) points higher in the reduction group. After multiple imputation, targeting the hypothetical estimand, the mean SFS score was -3.01 (95% CI -7.22, 1.20) points lower in the reduction group, but varied with different assumptions about the timing of events and in sensitivity analyses to increase the size of difference between randomized groups.

CONCLUSION

We demonstrated how the intervention effect can change when estimating the intervention effect in a pandemic world (treatment policy estimand) vs a pandemic restriction-free world (hypothetical estimand), and that estimates are sensitive to imputation and input assumptions. Trialists should be aware of potential intercurrent events and plan the analysis to take them into account.

PLAIN LANGUAGE SUMMARY

Many medical research studies that enable us to find out how well things work had to change due to COVID-19 restrictions. This may have altered the results. We used data from a randomized controlled trial (RCT) to examine whether there was evidence for this. The main outcome included questions on how often participants went to the cinema, swimming, to church or saw friends or relatives. Many of these activities were not possible during COVID-19 restrictions and became possible again over time. We used statistical methods to replace data that were collected during COVID-19 restrictions with the best possible estimate if COVID-19 had not happened. We found that the trial results were likely to have been different if the effect of COVID-19 restrictions were taken away. It is likely that most studies will be interested in results that do not include data collected during COVID-19.

Time-to-event estimands and loss to follow-up in oncology in light of the estimands guidance

Time-to-event estimands are central to many oncology clinical trials. The estimands framework (addendum to the ICH E9 guideline) calls for precisely defining the treatment effect of interest to align with the clinical question of interest and requires predefining the handling of intercurrent events (ICEs) that occur after treatment initiation and "affect either the interpretation or the existence of the measurements associated with the clinical question of interest." We discuss a practical problem in clinical trial design and execution, that is, in some clinical contexts it is not feasible to systematically follow patients to an event of interest. Loss to follow-up in the presence of intercurrent events can affect the meaning and interpretation of the study results. We provide recommendations for trial design, stressing the need for close alignment of the clinical question of interest and study design, impact on data collection, and other practical implications. When patients cannot be systematically followed, compromise may be necessary to select the best available estimand that can be feasibly estimated under the circumstances. We discuss the use of sensitivity and supplementary analyses to examine assumptions of interest.

Lisocabtagene Maraleucel in Relapsed/Refractory Mantle Cell Lymphoma: Primary Analysis of the Mantle Cell Lymphoma Cohort From TRANSCEND NHL 001, a Phase I Multicenter Seamless Design Study

PURPOSE

To report the primary analysis results from the mantle cell lymphoma (MCL) cohort of the phase I seamless design TRANSCEND NHL 001 (ClinicalTrials.gov identifier: NCT02631044) study.

METHODS

Patients with relapsed/refractory (R/R) MCL after ≥two lines of previous therapy, including a Bruton tyrosine kinase inhibitor (BTKi), an alkylating agent, and a CD20-targeted agent, received lisocabtagene maraleucel (liso-cel) at a target dose level (DL) of 50 × 106 (DL1) or 100 × 106 (DL2) chimeric antigen receptor-positive T cells. Primary end points were adverse events (AEs), dose-limiting toxicities, and objective response rate (ORR) by independent review committee per Lugano criteria.

RESULTS

Of 104 leukapheresed patients, liso-cel was infused into 88. Median (range) number of previous lines of therapy was three (1-11) with 30% receiving ≥five previous lines of therapy, 73% of patients were age 65 years and older, 69% had refractory disease, 53% had BTKi refractory disease, 23% had TP53 mutation, and 8% had secondary CNS lymphoma. Median (range) on-study follow-up was 16.1 months (0.4-60.5). In the efficacy set (n = 83; DL1 + DL2), ORR was 83.1% (95% CI, 73.3 to 90.5) and complete response (CR) rate was 72.3% (95% CI, 61.4 to 81.6). Median duration of response was 15.7 months (95% CI, 6.2 to 24.0) and progression-free survival was 15.3 months (95% CI, 6.6 to 24.9). Most common grade ≥3 treatment-emergent AEs were neutropenia (56%), anemia (37.5%), and thrombocytopenia (25%). Cytokine release syndrome (CRS) was reported in 61% of patients (grade 3/4, 1%; grade 5, 0), neurologic events (NEs) in 31% (grade 3/4, 9%; grade 5, 0), grade ≥3 infections in 15%, and prolonged cytopenia in 40%.

CONCLUSION

Liso-cel demonstrated high CR rate and deep, durable responses with low incidence of grade ≥3 CRS, NE, and infections in patients with heavily pretreated R/R MCL, including those with high-risk, aggressive disease.

A randomized double-blind clinical trial on safety and efficacy of tauroursodeoxycholic acid (TUDCA) as add-on treatment in patients affected by amyotrophic lateral sclerosis (ALS): the statistical analysis plan of TUDCA-ALS trial

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a highly debilitating neurodegenerative condition. Despite recent advancements in understanding the molecular mechanisms underlying ALS, there have been no significant improvements in therapeutic options for ALS patients in recent years. Currently, there is no cure for ALS, and the only approved treatment in Europe is riluzole, which has been shown to slow the disease progression and prolong survival by approximately 3 months. Recently, tauroursodeoxycholic acid (TUDCA) has emerged as a promising and effective treatment for neurodegenerative diseases due to its neuroprotective activities.

METHODS

The ongoing TUDCA-ALS study is a double-blinded, parallel arms, placebo-controlled, randomized multicenter phase III trial with the aim to assess the efficacy and safety of TUDCA as add-on therapy to riluzole in patients with ALS. The primary outcome measure is the treatment response defined as a minimum of 20% improvement in the ALS Functional Rating Scale-Revised (ALSFRS-R) slope during the randomized treatment period (18 months) compared to the lead-in period (3 months). Randomization will be stratified by country. Primary analysis will be conducted based on the intention-to-treat principle through an unadjusted logistic regression model. Patient recruitment commenced on February 22, 2019, and was closed on December 23, 2021. The database will be locked in September 2023.

DISCUSSION

This paper provides a comprehensive description of the statistical analysis plan in order to ensure the reproducibility of the analysis and avoid selective reporting of outcomes and data-driven analysis. Sensitivity analyses have been included in the protocol to assess the impact of intercurrent events related to the coronavirus disease 2019. By focusing on clinically meaningful and robust outcomes, this trial aims to determine whether TUDCA can be effective in slowing the disease progression in patients with ALS.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03800524 . Registered on January 11, 2019.

Randomized open-label controlled study of cancer vaccine OSE2101 versus chemotherapy in HLA-A2-positive patients with advanced non-small-cell lung cancer with resistance to immunotherapy: ATALANTE-1

BACKGROUND

Patients with advanced non-small-cell lung cancer (NSCLC) treated with immune checkpoint blockers (ICBs) ultimately progress either rapidly (primary resistance) or after durable benefit (secondary resistance). The cancer vaccine OSE2101 may invigorate antitumor-specific immune responses after ICB failure. The objective of ATALANTE-1 was to evaluate its efficacy and safety in these patients.

PATIENTS AND METHODS

ATALANTE-1 was a two-step open-label study to evaluate the efficacy and safety of OSE2101 compared to standard-of-care (SoC) chemotherapy (CT). Patients with human leukocyte antigen (HLA)-A2-positive advanced NSCLC without actionable alterations, failing sequential or concurrent CT and ICB were randomized (2 : 1) to OSE2101 or SoC (docetaxel or pemetrexed). Primary endpoint was overall survival (OS). Interim OS futility analysis was planned as per Fleming design. In April 2020 at the time of interim analysis, a decision was taken to prematurely stop the accrual due to coronavirus disease 2019 (COVID-19). Final analysis was carried out in all patients and in the subgroup of patients with ICB secondary resistance defined as failure after ICB monotherapy second line ≥12 weeks.

RESULTS

Two hundred and nineteen patients were randomized (139 OSE2101, 80 SoC); 118 had secondary resistance to sequential ICB. Overall, median OS non-significantly favored OSE2101 over SoC {hazard ratio (HR) [95% confidence interval (CI)] 0.86 [0.62-1.19], P = 0.36}. In the secondary resistance subgroup, OSE2101 significantly improved median OS versus SoC [11.1 versus 7.5 months; HR (95% CI) 0.59 (0.38-0.91), P = 0.017], and significantly improved post-progression survival (HR 0.46, P = 0.004), time to Eastern Cooperative Oncology Group (ECOG) performance status deterioration (HR 0.43, P = 0.006) and Quality of Life Questionnaire Core 30 (QLQ-C30) global health status compared to SoC (P = 0.045). Six-month disease control rates and progression-free survival were similar between groups. Grade ≥3 adverse effects occurred in 11.4% of patients with OSE2101 and 35.1% in SoC (P = 0.002).

CONCLUSIONS

In HLA-A2-positive patients with advanced NSCLC and secondary resistance to immunotherapy, OSE2101 increased survival with better safety compared to CT. Further evaluation in this population is warranted.

Defining estimands for efficacy assessment in single arm phase 1b or phase 2 clinical trials in oncology early development

The addendum of the ICH E9 guideline on the statistical principles for clinical trials introduced the estimand framework. The framework is designed to strengthen the dialog between different stakeholders, to introduce greater clarity in the clinical trial objectives and to provide alignment between the estimand and statistical analysis. Estimand framework related publications thus far have mainly focused on randomized clinical trials. The intention of the Early Development Estimand Nexus (EDEN), a task force of the cross-industry Oncology Estimand Working Group (www.oncoestimand.org), is to apply it to single arms Phase 1b or Phase 2 trials designed to detect a treatment-related efficacy signal, typically measured by objective response rate. Key recommendations regarding the estimand attributes include that in a single arm early clinical trial, the treatment attribute should start when the first dose is received by the participant. Focusing on the estimation of an absolute effect, the population-level summary measure should reflect only the property used for the estimation. Another major component introduced in the ICH E9 addendum is the definition of intercurrent events and the associated possible ways to handle them. Different strategies reflect different clinical questions of interest that can be answered based on the journeys an individual subject can take during a trial. We provide detailed strategy recommendations for intercurrent events typically seen in early-stage oncology. We highlight where implicit assumptions should be made transparent as whenever follow-up is suspended, a while-on-treatment strategy is implied.

An Expectation-Maximization Algorithm for Including Oncological COVID-19 Deaths in Survival Analysis

We address the problem of how COVID-19 deaths observed in an oncology clinical trial can be consistently taken into account in typical survival estimates. We refer to oncological patients since there is empirical evidence of strong correlation between COVID-19 and cancer deaths, which implies that COVID-19 deaths cannot be treated simply as non-informative censoring, a property usually required by the classical survival estimators. We consider the problem in the framework of the widely used Kaplan-Meier (KM) estimator. Through a counterfactual approach, an algorithmic method is developed allowing to include COVID-19 deaths in the observed data by mean-imputation. The procedure can be seen in the class of the Expectation-Maximization (EM) algorithms and will be referred to as Covid-Death Mean-Imputation (CoDMI) algorithm. We discuss the CoDMI underlying assumptions and the convergence issue. The algorithm provides a completed lifetime data set, where each Covid-death time is replaced by a point estimate of the corresponding virtual lifetime. This complete data set is naturally equipped with the corresponding KM survival function estimate and all available statistical tools can be applied to these data. However, mean-imputation requires an increased variance of the estimates. We then propose a natural extension of the classical Greenwood's formula, thus obtaining expanded confidence intervals for the survival function estimate. To illustrate how the algorithm works, CoDMI is applied to real medical data extended by the addition of artificial Covid-death observations. The results are compared with the estimates provided by the two naïve approaches which count COVID-19 deaths as censoring or as deaths by the disease under study. In order to evaluate the predictive performances of CoDMI an extensive simulation study is carried out. The results indicate that in the simulated scenarios CoDMI is roughly unbiased and outperforms the estimates obtained by the naïve approaches. A user-friendly version of CoDMI programmed in R is freely available.

Estimands and Complex Innovative Designs

Since the release of the ICH E9(R1) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials) document in 2019, the estimand framework has become a fundamental part of clinical trial protocols. In parallel, complex innovative designs have gained increased popularity in drug development, in particular in early development phases or in difficult experimental situations. While the estimand framework is relevant to any study in which a treatment effect is estimated, experience is lacking as regards its application to these designs. In a basket trial for example, should a different estimand be specified for each subpopulation of interest, defined, for example, by cancer site? Or can a single estimand focusing on the general population (defined, for example, by the positivity to a certain biomarker) be used? In the case of platform trials, should a different estimand be proposed for each drug investigated? In this work we discuss possible ways of implementing the estimand framework for different types of complex innovative designs. We consider trials that allow adding or selecting experimental treatment arms, modifying the control arm or the standard of care, and selecting or pooling populations. We also address the potentially data-driven, adaptive selection of estimands in an ongoing trial and disentangle certain statistical issues that pertain to estimation rather than to estimands, such as the borrowing of nonconcurrent information. We hope this discussion will facilitate the implementation of the estimand framework and its description in the study protocol when the objectives of the trial require complex innovative designs.

Impact of the COVID-19 pandemic on clinical trials: a cross-sectional questionnaire study in China

Background

The number of Chinese clinical trials has continued to grow throughout the coronavirus disease 2019 (COVID-19) pandemic, but we know little about clinical trial team members' perceptions and attitudes toward the impacts of the pandemic. This study aimed to assess the impact of the COVID-19 pandemic on clinical trials in China from the perspective of research staff to provide a deeper understanding and some recommendations for the ongoing and upcoming clinical trials during the pandemic.

Methods

A nationwide cross-sectional questionnaire was distributed to respondents throughout mainland China between September 2021 and October 2021. The participants assessed the impact of the COVID-19 pandemic on clinical trials based on a 5-point Likert-type scale, and exploratory factor analysis (EFA) was used to confirm the factor structure. Descriptive statistical analysis and the Mann-Whitney test were used to discover the differences between different groups.

Results

A total of 2,393 questionnaires from 272 hospitals were collected in mainland China. Factor analysis resulted in 4 factors, with a cumulative explained variance of 64.93%, as follows: subject enrollment, patient care, study supplies and data management, and research milestones and quality management. The research team members, predominantly represented by clinical research coordinators (CRCs), basically agreed with all but 3 preset scenarios of the impact of COVID-19 on clinical trials. Most respondents did not agree that the pandemic was associated with more serious adverse events (SAEs), missed reports of safety events, or any increase of unscheduled unblinding. In addition, significant differences were revealed in different age, gender, and role groups of respondents based on their views on the impact of the pandemic.

Conclusions

The current pandemic situation has had a negative impact on clinical trials, especially in terms of subject recruitment and protocol compliance, yet research team members feel confident that some of the effective measures proposed in the study can moderate the negative impact.

The case against censoring of progression-free survival in cancer clinical trials - A pandemic shutdown as an illustration

Background

Missing data may lead to loss of statistical power and introduce bias in clinical trials. The Covid-19 pandemic has had a profound impact on patient health care and on the conduct of cancer clinical trials. Although several endpoints may be affected, progression-free survival (PFS) is of major concern, given its frequent use as primary endpoint in advanced cancer and the fact that missed radiographic assessments are to be expected. The recent introduction of the estimand framework creates an opportunity to define more precisely the target of estimation and ensure alignment between the scientific question and the statistical analysis.

Methods

We used simulations to investigate the impact of two basic approaches for handling missing tumor scans due to the pandemic: a “treatment policy” strategy, which consisted in ascribing events to the time they are observed, and a “hypothetical” approach of censoring patients with events during the shutdown period at the last assessment prior to that period. We computed the power of the logrank test, estimated hazard ratios (HR) using Cox models, and estimated median PFS times without and with a hypothetical 6-month shutdown period with no patient enrollment or tumor scans being performed, varying the shutdown starting times.

Results

Compared with the results in the absence of shutdown, the “treatment policy” strategy slightly overestimated median PFS proportionally to the timing of the shutdown period, but power was not affected. Except for one specific scenario, there was no impact on the estimated HR. In general, the pandemic had a greater impact on the analyses using the “hypothetical” strategy, which led to decreased power and overestimated median PFS times to a greater extent than the “treatment policy” strategy.

Conclusion

As a rule, we suggest that the treatment policy approach, which conforms with the intent-to-treat principle, should be the primary analysis to avoid unnecessary loss of power and minimize bias in median PFS estimates.

Estimands for factorial trials

Factorial trials offer an efficient method to evaluate multiple interventions in a single trial, however the use of additional treatments can obscure research objectives, leading to inappropriate analytical methods and interpretation of results. We define a set of estimands for factorial trials, and describe a framework for applying these estimands, with the aim of clarifying trial objectives and ensuring appropriate primary and sensitivity analyses are chosen. This framework is intended for use in factorial trials where the intent is to conduct "two-trials-in-one" (ie, to separately evaluate the effects of treatments A and B), and is comprised of four steps: (i) specifying how additional treatment(s) (eg, treatment B) will be handled in the estimand, and how intercurrent events affecting the additional treatment(s) will be handled; (ii) designating the appropriate factorial estimator as the primary analysis strategy; (iii) evaluating the interaction to assess the plausibility of the assumptions underpinning the factorial estimator; and (iv) performing a sensitivity analysis using an appropriate multiarm estimator to evaluate to what extent departures from the underlying assumption of no interaction may affect results. We show that adjustment for other factors is necessary for noncollapsible effect measures (such as odds ratio), and through a trial re-analysis we find that failure to consider the estimand could lead to inappropriate interpretation of results. We conclude that careful use of the estimands framework clarifies research objectives and reduces the risk of misinterpretation of trial results, and should become a standard part of both the protocol and reporting of factorial trials.

Estimators for handling COVID-19-related intercurrent events with a hypothetical strategy

The COVID-19 pandemic has affected clinical trials across disease areas, raising the questions how interpretable results can be obtained from impacted studies. Applying the estimands framework, analyses may seek to estimate the treatment effect in the hypothetical absence of such impact. However, no established estimators exist. This simulation study, based on an ongoing clinical trial in patients with Tourette syndrome, compares the performance of candidate estimators for estimands including either a continuous or binary variable and applying a hypothetical strategy for COVID-19-related intercurrent events (IE). The performance is investigated in a wide range of scenarios, under the null and the alternative hypotheses, including different modeling assumptions for the effect of the IE and proportions of affected patients ranging from 10% to 80%. Bias and type I error inflation were minimal or absent for most estimators under most scenarios, with only multiple imputation- and weighting-based methods displaying a type I error inflation in some scenarios. Of more concern, all methods that discarded post-IE data displayed a sharp decrease of power proportional to the proportion of affected patients, corresponding to both a reduced precision of estimation and larger confidence intervals. The simulation study shows that de-mediation via g-estimation is a promising approach. Besides showing the best performance in our simulation study, these approaches allow to estimate the effect of the IE on the outcome and cross-compare between different studies affected by similar IEs. Importantly, the results can be extrapolated to IEs not related to COVID-19 that follow a similar causal structure.

The impact of Covid-19 on the initiation of clinical trials in Europe and the United States

The coronavirus disease 2019 (COVID‐19) pandemic has a major impact not only on public health and daily living, but also on clinical trials worldwide. To investigate the potential impact of the COVID‐19 pandemic on the initiation of clinical trials, we have descriptively analyzed the longitudinal change in phase II and III interventional clinical trials initiated in Europe and in the United States. Based on the public clinical trial register EU Clinical Trials Register and clinicaltrials.gov, we conducted (i) a yearly comparison of the number of initiated trials from 2010 to 2020 and (ii) a monthly comparison from January 2020 to February 2021 of the number of initiated trials. The analyses indicate that the COVID‐19 pandemic affected both the initiation of clinical trials overall and the initiation of non‐COVID‐19 trials. An increase in the overall numbers of clinical trials could be observed both in Europe and the United States in 2020 as compared with 2019. However, the number of non‐COVID‐19 trials initiated is reduced as compared with the previous decade, with a slightly larger relative decrease in the United States as compared to Europe. Additionally, the monthly trend for the initiation of non‐COVID‐19 trials differs between regions. In the United States, after a sharp decrease in April 2020, trial numbers reached the levels of 2019 from June 2020 onward. In Europe, the decrease was less pronounced, but trial numbers mainly remained below the 2019 average until February 2021.

The impact of the SARS-CoV-2 pandemic on the management of chronic limb-threatening ischemia and wound care

In the wake of the coronavirus pandemic, the critical limb ischemia (CLI) Global Society aims to develop improved clinical guidance that will inform better care standards to reduce tissue loss and amputations during and following the new SARS‐CoV‐2 era. This will include developing standards of practice, improve gaps in care, and design improved research protocols to study new chronic limb‐threatening ischemia treatment and diagnostic options. Following a round table discussion that identified hypotheses and suppositions the wound care community had during the SARS‐CoV‐2 pandemic, the CLI Global Society undertook a critical review of literature using PubMed to confirm or rebut these hypotheses, identify knowledge gaps, and analyse the findings in terms of what in wound care has changed due to the pandemic and what wound care providers need to do differently as a result of these changes. Evidence was graded using the Oxford Centre for Evidence‐Based Medicine scheme. The majority of hypotheses and related suppositions were confirmed, but there is noticeable heterogeneity, so the experiences reported herein are not universal for wound care providers and centres. Moreover, the effects of the dynamic pandemic vary over time in geographic areas. Wound care will unlikely return to prepandemic practices. Importantly, Levels 2–5 evidence reveals a paradigm shift in wound care towards a hybrid telemedicine and home healthcare model to keep patients at home to minimize the number of in‐person visits at clinics and hospitalizations, with the exception of severe cases such as chronic limb‐threatening ischemia. The use of telemedicine and home care will likely continue and improve in the postpandemic era.

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

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.

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

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).

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

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 'pandemic-free 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 assumption - potentially incorporating a pandemic time-period indicator and participant infection status - or 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.