Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue?

The impact of heterogeneity on the analysis of platform trials with normally distributed outcomes

BACKGROUND

A platform trial approach allows adding arms to on-going trials to speed up intervention discovery programs. A control arm remains open for recruitment in a platform trial while intervention arms may be added after the onset of the study and could be terminated early for efficacy and/or futility when early stopping is allowed. The topic of utilising non-concurrent control data in the analysis of platform trials has been explored and discussed extensively. A less familiar issue is the presence of heterogeneity, which may exist for example due to modification of enrolment criteria and recruitment strategy.

METHOD

We conduct a simulation study to explore the impact of heterogeneity on the analysis of a two-stage platform trial design. We consider heterogeneity in treatment effects and heteroscedasticity in outcome data across stages for a normally distributed endpoint. We examine the performance of some hypothesis testing procedures and modelling strategies. The use of non-concurrent control data is also considered accordingly. Alongside standard regression analysis, we examine the performance of a novel method that was known as the pairwise trials analysis. It is similar to a network meta-analysis approach but adjusts for treatment comparisons instead of individual studies using fixed effects.

RESULTS

Several testing strategies with concurrent control data seem to control the type I error rate at the required level when there is heteroscedasticity in outcome data across stages and/or a random cohort effect. The main parameter of treatment effects in some analysis models correspond to overall treatment effects weighted by stage wise sample sizes; while others correspond to the effect observed within a single stage. The characteristics of the estimates are not affected significantly by the presence of a random cohort effect and/ or heteroscedasticity.

CONCLUSION

In view of heterogeneity in treatment effect across stages, the specification of null hypotheses in platform trials may need to be more subtle. We suggest employing testing procedure of adaptive design as opposed to testing the statistics from regression models; comparing the estimates from the pairwise trials analysis method and the regression model with interaction terms may indicate if heterogeneity is negligible.

Point estimation for adaptive trial designs II: Practical considerations and guidance

In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred.

A Multi-component Intervention (NEXpro) Reduces Neck Pain-Related Work Productivity Loss: A Randomized Controlled Trial Among Swiss Office Workers

Purpose Neck pain is common among office workers and leads to work productivity loss. This study aimed to investigate the effect of a multi-component intervention on neck pain-related work productivity loss among Swiss office workers. Methods Office workers, aged 18-65 years, and without serious neck-related health problems were recruited from two organisations for our stepped-wedge cluster randomized controlled trial. The 12-week multi-component intervention included neck exercises, health-promotion information, and workplace ergonomics. The primary outcome of neck pain-related work productivity loss was measured using the Work Productivity and Activity Impairment Questionnaire and expressed as percentages of working time. In addition, we reported the weekly monetary value of neck pain-related work productivity loss. Data was analysed on an intention-to-treat basis using a generalized linear mixed-effects model. Results Data from 120 participants were analysed with 517 observations. At baseline, the mean age was 43.7 years (SD 9.8 years), 71.7% of participants were female (N = 86), about 80% (N = 95) reported mild to moderate neck pain, and neck pain-related work productivity loss was 12% of working time (absenteeism: 1.2%, presenteeism: 10.8%). We found an effect of our multi-component intervention on neck pain-related work productivity loss, with a marginal predicted mean reduction of 2.8 percentage points (b = -0.27; 95% CI: -0.54 to -0.001, p = 0.049). Weekly saved costs were Swiss Francs 27.40 per participant. Conclusions: Our study provides evidence for the effectiveness of a multi-component intervention to reduce neck pain-related work productivity loss with implications for employers, employees, and policy makers.Trial Registration ClinicalTrials.gov, NCT04169646. Registered 15 November 2019-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04169646 .

Using dichotomized survival data to construct a prior distribution for a Bayesian seamless Phase II/III clinical trial

Master protocol designs allow for simultaneous comparison of multiple treatments or disease subgroups. Master protocols can also be designed as seamless studies, in which two or more clinical phases are considered within the same trial. They can be divided into two categories: operationally seamless, in which the two phases are separated into two independent studies, and inferentially seamless, in which the interim analysis is considered an adaptation of the study. Bayesian designs are scarcely studied. Our aim is to propose and compare Bayesian operationally seamless Phase II/III designs using a binary endpoint for the first stage and a time-to-event endpoint for the second stage. At the end of Phase II, arm selection is based on posterior (futility) and predictive (selection) probabilities. The results of the first phase are then incorporated into prior distributions of a time-to-event model. Simulation studies showed that Bayesian operationally seamless designs can approach the inferentially seamless counterpart, allowing for an increasing simulated power with respect to the operationally frequentist design.

Cluster randomised trial of a complex interprofessional intervention (interprof ACT) to reduce hospital admission of nursing home residents

BACKGROUND

Some hospital admissions of nursing home residents (NHRs) might be attributed to inadequate interprofessional collaboration. To improve general practitioner-nurse collaboration in nursing homes (NHs), we developed an intervention package (interprof ACT) in a previous study.

OBJECTIVE

To assess the impact of interprof ACT on the proportion of hospitalisation and other clinical parameters within 12 months from randomisation among NHRs.

METHODS

Multicentre, cluster randomised controlled trial in 34 German NHs. NHRs of the control group received usual care, whereas NHRs in the intervention group received interprof ACT. Eligible NHs had at least 40 long-term care residents. NHs were randomised 1:1 pairwise. Blinded assessors collected primary outcome data.

RESULTS

Seventeen NHs (320 NHRs) were assigned to interprof ACT and 17 NHs (323 NHRs) to usual care. In the intervention group, 136 (42.5%) NHRs were hospitalised at least once within 12 months from randomisation and 151 (46.7%) in the control group (odds ratio (OR): 0.82, 95% confidence interval (CI): [0.55; 1.22], P = 0.33). No differences were found for the average number of hospitalisations: 0.8 hospitalisations per NHR (rate ratio (RR) 0.90, 95% CI: [0.66, 1.25], P = 0.54). Average length of stay was 5.7 days for NHRs in the intervention group and 6.5 days in the control group (RR: 0.70, 95% CI: [0.45, 1.11], P = 0.13). Falls were the most common adverse event, but none was related to the study intervention.

CONCLUSIONS

The implementation of interprof ACT did not show a statistically significant and clinically relevant effect on hospital admission of NHRs.

Point estimation for adaptive trial designs I: A methodological review

Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.

The short-term effects of instrument-based mobilization compared with manual mobilization for low back pain: A randomized clinical trial

BACKGROUND

Despite being used as a manipulation technique, no studies have examined the effectiveness of physiotherapy instrument mobilization (PIM) as a mobilization technique on pain and functional status in patients with low back pain (LBP).

OBJECTIVE

To investigate the effectiveness of PIM in patients with LBP and to compare it with the effectiveness of manual mobilization.

METHODS

This is a double blind, randomized clinical trial. Thirty-two participants with LBP were randomly assigned to one of two groups. The PIM group received lumbar mobilization using an activator instrument, stabilization exercises, and education; and the manual group received lumbar mobilization using a pisiform grip, stabilization exercises, and education. Both groups had a total of 4 treatment sessions over 2-3 weeks. The following outcomes were measured before the intervention, and after the first and fourth sessions: Numeric Pain Rating Scale (NPRS), Oswestry Disability Index (ODI) scale, Pressure pain threshold (PPT), lumbar spine range of motion (ROM), and lumbar multifidus muscle activation.

RESULTS

There were no differences between the PIM group and the manual group in any outcome measures. However, over the period of study, there were improvements in both groups in NPRS (PIM: 3.23, Manual: 3.64 points), ODI (PIM: 17.34%, Manual: 14.23%), PPT (PIM: ⩽ 1.25, Manual: ⩽ 0.85 kg.cm2), lumbar spine ROM (PIM: ⩽ 9.49∘, Manual: ⩽ 0.88∘), and/or lumbar multifidus muscle activation (percentage thickness change: PIM: ⩽ 4.71, Manual: ⩽ 4.74 cm; activation ratio: PIM: ⩽ 1.17, Manual: ⩽ 1.15 cm).

CONCLUSIONS

Both methods of lumbar spine mobilization demonstrated comparable improvements in pain and disability in patients with LBP, with neither method exhibiting superiority over the other.

Research interrupted: applying the CONSERVE 2021 Statement to a randomized trial of rehabilitation during critical illness affected by the COVID-19 pandemic

RATIONALE

The COVID-19 pandemic disrupted non-COVID critical care trials globally as intensive care units (ICUs) prioritized patient care and COVID-specific research. The international randomized controlled trial CYCLE (Critical Care Cycling to Improve Lower Extremity Strength) was forced to halt recruitment at all sites in March 2020, creating immediate challenges. We applied the CONSERVE (CONSORT and SPIRIT Extension for RCTs Revised in Extenuating Circumstance) statement as a framework to report the impact of the pandemic on CYCLE and describe our mitigation approaches.

METHODS

On March 23, 2020, the CYCLE Methods Centre distributed a standardized email to determine the number of patients still in-hospital and those requiring imminent 90-day endpoint assessments. We assessed protocol fidelity by documenting attempts to provide the in-hospital randomized intervention (cycling or routine physiotherapy) and collect the primary outcome (physical function 3-days post-ICU discharge) and 90-day outcomes. We advised sites to prioritize data for the study's primary outcome. We sought feedback on pandemic barriers related to trial procedures.

RESULTS

Our main Methods Centre mitigation strategies included identifying patients at risk for protocol deviations, communicating early and frequently with sites, developing standardized internal tools focused on high-risk points in the protocol for monitoring patient progress, data entry, and validation, and providing guidance to conduct some research activities remotely. For study sites, our strategies included determining how institutional pandemic research policies applied to CYCLE, communicating with the Methods Centre about capacity to continue any part of the research, and developing contingency plans to ensure the protocol was delivered as intended. From 15 active sites (12 Canada, 2 US, 1 Australia), 5 patients were still receiving the study intervention in ICUs, 6 required primary outcomes, and 17 required 90-day assessments. With these mitigation strategies, we attempted 100% of ICU interventions, 83% of primary outcomes, and 100% of 90-day assessments per our protocol.

CONCLUSIONS

We retained all enrolled patients with minimal missing data using several time-sensitive strategies. Although CONSERVE recommends reporting only major modifications incurred by extenuating circumstances, we suggest that it also provides a helpful framework for reporting mitigation strategies with the goal of improving research transparency and trial management.

TRIAL REGISTRATION

NCT03471247. Registered on March 20, 2018.

Decision rules for identifying combination therapies in open-entry, randomized controlled platform trials

Platform trials have become increasingly popular for drug development programs, attracting interest from statisticians, clinicians and regulatory agencies. Many statistical questions related to designing platform trials-such as the impact of decision rules, sharing of information across cohorts, and allocation ratios on operating characteristics and error rates-remain unanswered. In many platform trials, the definition of error rates is not straightforward as classical error rate concepts are not applicable. For an open-entry, exploratory platform trial design comparing combination therapies to the respective monotherapies and standard-of-care, we define a set of error rates and operating characteristics and then use these to compare a set of design parameters under a range of simulation assumptions. When setting up the simulations, we aimed for realistic trial trajectories, such that for example, a priori we do not know the exact number of treatments that will be included over time in a specific simulation run as this follows a stochastic mechanism. Our results indicate that the method of data sharing, exact specification of decision rules and a priori assumptions regarding the treatment efficacy all strongly contribute to the operating characteristics of the platform trial. Furthermore, different operating characteristics might be of importance to different stakeholders. Together with the potential flexibility and complexity of a platform trial, which also impact the achieved operating characteristics via, for example, the degree of efficiency of data sharing this implies that utmost care needs to be given to evaluation of different assumptions and design parameters at the design stage.

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Design aspects of COVID-19 treatment trials: Improving probability and time of favorable events

As a reaction to the pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a multitude of clinical trials for the treatment of SARS-CoV-2 or the resulting corona disease 2019 (COVID-19) are globally at various stages from planning to completion. Although some attempts were made to standardize study designs, this was hindered by the ferocity of the pandemic and the need to set up clinical trials quickly. We take the view that a successful treatment of COVID-19 patients (i) increases the probability of a recovery or improvement within a certain time interval, say 28 days; (ii) aims to expedite favorable events within this time frame; and (iii) does not increase mortality over this time period. On this background, we discuss the choice of endpoint and its analysis. Furthermore, we consider consequences of this choice for other design aspects including sample size and power and provide some guidance on the application of adaptive designs in this particular context.

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.

Health Product Manufacturers and Innovators COVID-19 Impact Assessment: Lessons Learned and Compelling Needs

United States health care spending consumes nearly a fifth of the GDP [1]. While, in many respects, the U.S. health care system is enviable and highly innovative, it is also characterized by elements of ineffectiveness, inefficiency, and inequity. These aspects, resulting from pre-existing vulnerabilities within the system and interactions between the various stakeholders, were acutely highlighted by the COVID-19 pandemic. As health product manufacturers and innovators (HPMI) took steps to mitigate the immediate crisis and simultaneously begin to develop a longer-term sustainable solution, six common themes arose as areas for transformational change: support for science, data sharing, supply chain resiliency, stockpiling, and surge capacity, regulatory and reimbursement clarity and flexibility, public- and private-sector coordination and communication, and minimizing substandard care offerings. Within these categories, the authors of this paper suggest policy priorities to increase the effectiveness, efficiency, and equity of the HPMI sector and writ large across the U.S. health care system. These priorities call for increased scientific funding to diversify the pipeline for research and development, strengthening the nation’s public health infrastructure, building and maintaining “ever warm” manufacturing capacity and related stockpiles, instituting efficient and effective regulatory and reimbursement frameworks that promote innovation and creativity, devising structures and processes that enable more efficient collaboration and more effective communication to the public, and implementing rewards that incentivize desired behaviors among stakeholders. This assessment draws from the collective experience of the authors to provide a perspective for the diagnostics, hospital supplies and equipment, medical devices, therapeutics, and vaccines segments. While the authors of this paper agree on a common set of key policies, sub-sector-specific nuances are important to consider when putting any action priority into effect. With thoughtful implementation, these policies will enable a quicker, more robust response to future pandemics and enhance the overall performance of the U.S. health care system.

What Do Physicians Think About the Use of Telemedicine to Recruit and Assess Participants in mHealth-Related Clinical Studies as a Consequence of the COVID-19 Pandemic?

Objectives: To evaluate physician's opinion and availability to participate in mHealth-related clinical studies with patient recruitment and assessment via telemedicine and to identify characteristics associated with the willingness to participate. Methods: Cross-sectional, observational study, based on an anonymous web survey conducted in May-Jun of 2020 to 237 physicians, from Portugal and Spain that collaborated with an asthma mHealth project (INSPIRERS). Results: Response rate was 51% (n = 120). Most (74%, n = 89) physicians were available to participate in such studies, but 62% anticipated lower recruiting capacity and 40% increased difficulty in obtaining quality data. Physicians aged ≤40 years, from secondary care (vs. general practitioners) and that used apps in personal life or clinical practice were more likely to be available. Conclusions: Three-quarters of the physicians were available to participate in mHealth-related clinical studies with patient recruitment and assessment through telemedicine. Age group, medical specialty, and app use were associated with the willingness to participate.

Psychosocial Health and Physical Activity in People With Major Depression in the Context of COVID-19

Introduction: Major depression is a psychiatric disease associated with physical inactivity, which in turn affects mental and physical health. A randomized controlled trial is being implemented to facilitate physical activity in people with major depression. In March 2020, Swiss state authorities temporarily legislated a lockdown to contain the Coronavirus disease-19 (COVID-19), which influenced health, behavior and research. The aim of this study was to find out whether data gathered before and during/after the lockdown among in-patients with major depression differ with regard to psychosocial health, physical activity and related attitudes and to establish whether baseline data have been affected by the lockdown. Methods: This is a cross-sectional analysis within a randomized controlled trial. Physically inactive, adult in-patients diagnosed with major depression were recruited from four Swiss psychiatric clinics between January 2019 and December 2020. Psychosocial health was measured with questionnaires pertaining to stress, sleep and health-related quality of life. Physical activity was measured with the Simple Physical Activity Questionnaire. Explicit attitudes were measured with seven questionnaires pertaining to physical activity-related motivation and volition. Implicit attitudes toward physical activity were captured with a single target implicit association test. Results: The sample consisted of 165 participants (n = 119 before lockdown, n = 46 during/after lockdown). No statistically significant differences were found between in-patients with major depression assessed before and during/after the COVID-19 lockdown with regard to psychosocial health (stress, p = 0.51; sleep, p = 0.70; physical component of health-related quality of life, p = 0.55; mental component of health-related quality of life, p = 0.64), self-reported physical activity (p = 0.16) and explicit as well as implicit attitudes toward physical activity (p = 0.94). Hence, the COVID-19-induced lockdown seems not to have led to group differences. Conclusion: Baseline data gathered in in-patients suffering from major depression who are physically inactive upon admission to in-patient treatment in Switzerland seem to be unaffected by the COVID-19-induced lockdown. To assess changes in said population regarding psychosocial health and physical activity patterns over time, longitudinal data are needed.

Why ITT analysis is not always the answer for estimating treatment effects in clinical trials

For many years there has been a consensus among the Clinical Research community that ITT analysis represents the correct approach for the vast majority of trials. Recent worldwide regulatory guidance for pharmaceutical industry trials has allowed discussion of alternatives to the ITT approach to analysis; different treatment effects can be considered which may be more clinically meaningful and more relevant to patients and prescribers. The key concept is of a trial "estimand", a precise description of the estimated treatment effect. The strategy chosen to account for patients who discontinue treatment or take alternative medications which are not part of the randomised treatment regimen are important determinants of this treatment effect. One strategy to account for these events is treatment policy, which corresponds to an ITT approach. Alternative equally valid strategies address what the treatment effect is if the patient actually takes the treatment or does not use specific alternative medication. There is no single right answer to which strategy is most appropriate, the solution depends on the key clinical question of interest. The estimands framework discussed in the new guidance has been particularly useful in the context of the current COVID-19 pandemic and has clarified what choices are available to account for the impact of COVID-19 on clinical trials. Specifically, an ITT approach addresses a treatment effect that may not be generalisable beyond the current pandemic.

The impact of disasters on contraception in OECD member countries: a scoping review

OBJECTIVES

Review evidence is lacking about how contraception is affected by severe social disruption, such as that caused by the COVID-19 pandemic. The purpose of this scoping review was to explore the impact of natural and man-made disasters on contraception in OECD member countries.

METHODS

Manual searches and systematic searches in six electronic databases were conducted with no language restrictions. All articles were screened by at least two researchers. The data were analysed thematically.

RESULTS

108 articles were included. Most focussed on the Zika virus outbreak (n = 50) and the COVID-19 pandemic (n = 28). Four key themes were identified: importance of contraception during disasters, impact of disasters on contraceptive behaviour, barriers to contraception during disasters and ways of improving use of contraception during disasters. Despite efforts to increase access to contraception including by transforming ways of delivery, barriers to use meant that unmet need persisted.

CONCLUSIONS

To prevent adverse health outcomes and reduce health costs as a result of failure to have access to contraception during disasters, there is a need to intensify efforts to remove barriers to use. This should include increasing access and information on methods of contraception and their side effects (e.g., menstrual suppression) and making contraception freely available.

The impact of COVID-19 on the cell and gene therapies industry: Disruptions, opportunities, and future prospects

Coronavirus 2019 (COVID-19) has caused significant disruption to the cell and gene therapy (CGT) industry, which has historically faced substantial complexities in supply of materials, and manufacturing and logistics processes. As decision-makers shifted their priorities to COVID-19-related issues, the challenges in market authorisation, and price and reimbursement of CGTs were amplified. Nevertheless, it is encouraging to see that some CGT developers are adapting their efforts toward the development of promising COVID-19-related therapeutics and vaccines. Manufacturing resilience, digitalisation, telemedicine, value-based pricing, and innovative payment mechanisms will be increasingly harnessed to ensure that market access of CGTs is not severely disrupted.

Systematic review of available software for multi-arm multi-stage and platform clinical trial design

BACKGROUND

In recent years, the popularity of multi-arm multi-stage, seamless adaptive, and platform trials has increased. However, many design-related questions and questions regarding which operating characteristics should be evaluated to determine the potential performance of a specific trial design remain and are often further complicated by the complexity of such trial designs.

METHODS

A systematic search was conducted to review existing software for the design of platform trials, whereby multi-arm multi-stage trials were also included. The results of this search are reported both on the literature level and the software level, highlighting the software judged to be particularly useful.

RESULTS

In recent years, many highly specialized software packages targeting single design elements on platform studies have been released. Only a few of the developed software packages provide extensive design flexibility, at the cost of limited access due to being commercial or not being usable as out-of-the-box solutions.

CONCLUSIONS

We believe that both an open-source modular software similar to OCTOPUS and a collaborative effort will be necessary to create software that takes advantage of and investigates the impact of all the flexibility that platform trials potentially provide.

Analysis of the COVID-19 Vaccine Development Process: an Exploratory Study of Accelerating Factors and Innovative Environments

Purpose

The pace of the COVID-19 vaccine development process is unprecedented and is challenging the traditional paradigm of vaccinology science. The main pressure comes from the pandemic situation, but what makes it possible is a complex set of factors and innovative environments built along the times, which this manuscript aims to study.

Methods

Through an exploratory study within the scope of innovation management, the present manuscript aims to identify and explore factors that are promoting this accelerated development scenario. The method comprises the monitoring of the strategies adopted by the developers and other stakeholders, as regulatory and humanitarian agencies, specific mechanisms from governments and non-governments bodies, and the background technology that has paved this pathway.

Results

Technology-based and R&D strategy factors are the two main factors identified and explored herein. The breakthrough in the field of biotechnology and molecular biology is considered the main base-science that enables the rapid development of new vaccines. Additionally, new technological platforms can also be pointed out. Relating to R&D strategies, the parallelism of phases and adaptive clinical trials in consonance with regulatory agencies are the most relevant.

Conclusions

The need to rapidly develop a vaccine against COVID-19 occurs at a time of great excitement in basic scientific understanding, as well as strategies learned in the past by industry and optimization of regulatory pathways. It is expected that these factors, arising from the global emergency, may redirect the R&D processes for new drugs, especially in times of pandemic.

Causal inference methods for small non-randomized studies: Methods and an application in COVID-19

The usual development cycles are too slow for the development of vaccines, diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2 pandemic. Given the pressure in such a situation, there is a risk that findings of early clinical trials are overinterpreted despite their limitations in terms of size and design. Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies. A widely used tool to reduce the impact of treatment-selection bias are so-called propensity score (PS) methods. Conditioning on the propensity score allows one to replicate the design of a randomized controlled trial, conditional on observed covariates. Extensions include the g-computation approach, which is less frequently applied, in particular in clinical studies. Moreover, doubly robust estimators provide additional advantages. Here, we investigate the properties of propensity score based methods including three variations of doubly robust estimators in small sample settings, typical for early trials, in a simulation study. R code for the simulations is provided.

Clinical Trial Drug Safety Assessment for Studies and Submissions Impacted by COVID-19

Abstract-In this article, we provide guidance on how safety analyses and reporting of clinical trial safety data may need to be modified, given potential impact from the COVID-19 pandemic. Impact could include missed visits, alternative methods for assessments (such as virtual visits), alternative locations for assessments (such as local labs), and study drug interruptions. Starting from the safety analyses typically included in Clinical Study Reports for Phase 2-4 clinical trials and integrated submission documents, we assess what modifications might be needed. If the impact from COVID-19 affects treatment arms equally, analyses of adverse events from controlled data can, to a large extent, remain unchanged. However, interpretation of summaries from uncontrolled data (summaries that include open-label extension data) will require even more caution than usual. Special consideration will be needed for safety topics of interest, especially events expected to have a higher incidence due to a COVID-19 infection or due to quarantine or travel restrictions (e.g., depression). Analyses of laboratory measurements may need to be modified to account for the combination of measurements from local and central laboratories.

Machine learning for clinical trials in the era of COVID-19

The world is in the midst of a pandemic. We still know little about the disease COVID-19 or about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside from managing symptoms). We do not know if recovery from COVID-19 produces immunity, and if so for how long, hence we do not know if "herd immunity" will eventually reduce the risk or if a successful vaccine can be developed - and this knowledge may be a long time coming. In the meantime, the COVID-19 pandemic is presenting enormous challenges to medical research, and to clinical trials in particular. This paper identifies some of those challenges and suggests ways in which machine learning can help in response to those challenges. We identify three areas of challenge: ongoing clinical trials for non-COVID-19 drugs; clinical trials for repurposing drugs to treat COVID-19, and clinical trials for new drugs to treat COVID-19. Within each of these areas, we identify aspects for which we believe machine learning can provide invaluable assistance.

Challenges in Assessing the Impact of the COVID-19 Pandemic on the Integrity and Interpretability of Clinical Trials

Abstract-The COVID-19 pandemic has a global impact on the conduct of clinical trials of medical products. This article discusses implications of the COVID-19 pandemic on clinical research methodology aspects and provides points to consider to assess and mitigate the risk of seriously compromising the integrity and interpretability of clinical trials. The information in this article will support discussions that need to occur cross-functionally on an ongoing basis to "integrate all available knowledge from the ethical, the medical, and the methodological perspective into decision making." This article aims at facilitating: (i) risk assessments of the impact of the pandemic on trial integrity and interpretability; (ii) identification of the relevant data and information related to the impact of the pandemic on the trial that needs to be collected; (iii) short-term decision making impacting ongoing trial operations; (iv) ongoing monitoring of the trial conduct until completion, including the possible involvement of data monitoring committees, and adequately documenting all measures taken to secure trial integrity throughout and after the pandemic, and (v) proper analysis and interpretation of the eventual interim or final trial data.