Statistical Issues and Challenges in Clinical Trials for COVID-19 Treatments, Vaccines, Medical Devices and Diagnostics

Since its breakout in late 2019, the pandemic caused by coronavirus disease (COVID-19) has impacted everyone’s life in different ways, from lifestyle and working environment to economics and politics. Researchers in all fields, especially in medicine, have rose to the occasion to understand the disease, investigating preventive measures, identifying possible treatments and vaccines, and exploring various approaches to collect and analyze the quickly accumulating data. However, the more we learn about the virus and the disease, the more we realize how much we do not know. First, the coronavirus constantly adapts and generates new variants. Throughout the COVID-19 pandemic, thousands of coronavirus variants have been identified and several of them are of great concern to public health. Second, long-term effects of COVID-19 have been recognized and suffered by many (so-called long COVID). In a similar manner, long-term safety issues from COVID-19 treatments and vaccines have surfaced that call for immediate attention. As expected, proper doses and formula for minority groups and pediatric populations are still being investigated. Last, medical resource shortages, including medical supplies and frontline health care providers, have caused a wide range of medical emergencies worldwide. Needless to say, all aspects of clinical trials in all fields and facilities are being affected, including protocol language updates, patient enrollment difficulties, treatment dispense challenges, and missing or delayed assessments.

Responding to popular demands after publishing our first Special Issue of Statistics in Biopharmaceutical Research on clinical trial disruptions and statistical issues due to COVID-19 pandemic (Hamasaki et al., 2020), we felt obliged to dedicate another Special Issue to cover additional topics not covered previously, including treatment identification as well as vaccine and diagnostic test development for COVID-19. This Special Issue is comprised of peer-reviewed articles and commentaries, drafted and finalized in a condensed time period, with topics both proactive and reactive in the COVID-19 pandemic.

Vaccine development has attracted unprecedented attention during the pandemic. This Special Issue includes three articles that discuss different approaches or perspectives in vaccine trials. Both Mukherjee et al. (2021) and Johnson et al. (2021) delved into adaptive trial designs that could be used for vaccine trials. Mukherjee et al. (2021) presents a Bayesian sequential design with multiple interim looks and predictive power. Johnson et al. (2021) integrated “ring” recruitment strategies, data weighing approaches and response-adaptive randomization in the clinical trial design and analysis. In addition, Patterson et al. (2021) provided a holistic view on vaccine clinical development with selected statistical considerations. Switching gears to COVID-19 treatment trials, McMillan et al. (2021) discussed various innovative trial designs that are called PIPELINES, Portfolio of Innovative Platform Engines, Longitudinal Investigations and Novel Effectiveness, introduced by Trusheim et al. (2016). Natanegara et al. (2021) expanded the discussion from innovative trial designs to endpoints standardization, data sharing and evidence generation, with detailed statistical considerations. Chakladar et al. (2021) employed a discrete-time multistate Markov process with regime switching in a COVID-19 disease progression model to gain insight in clinically meaningful endpoints associated with treatment effect sizes. Finally, Collins et al. (2020) provided constructive remarks on comprehensive regulatory guidance and references on both clinical trial conduct during the pandemic and on COVID-19 therapeutics development, all from a perspective of statisticians at the Center for Drug Evaluation and Research (CDER), Food and Drug Administration (FDA).

Although this Special Issue mainly focuses on statistical issues and challenges in clinical trials for COVID-19 treatments, vaccines, medical devices and diagnostics, several articles provide tools to resolve issues observed in ongoing clinical trials disrupted by the pandemic. Li et al. (2021) introduced an innovative usage of propensity score-integrated approaches based on the strategies proposed by Meyer et al. (2020) and Akacha et al. (2020) to salvage the power loss from COVID-interrupted clinical trials. Another novel mitigation strategy was discussed by Hua et al. (2021), who incorporate a modified graphical approach benefiting from two identical repeated clinical trials conducted to support one registrational drug application. Taking a more proactive approach for ongoing trials, Best et al. (2021) developed a visualization tool to forecast recruitment during the pandemic, using both internal and external historical data with a Bayesian hierarchical model.

We would like to express our gratitude towards all the authors of the articles included in this second Special Issue on COVID-19. Without reservation, they shared their critical views from both an operational and a statistical perspective on clinical trials during the pandemic, adding enormous values to COVID-19 research. We also wish to thank the anonymous referees, who dedicated many personal hours in reviewing the submitted articles and providing insightful comments; their contribution is much appreciated. It is worthy of note that the work from both the authors and the referees were accomplished under the stress and exhaustion caused by the pandemic. Last but not least, thanks go to SBR Editorial Coordinator Jina Lee and ASA Journals and Publications Manager Eric Sampson for their enthusiasm and tireless support for this Special Issue. We are also grateful to Taylor and Francis for yet another rapid processing and publication of this Special Issue. Following the general policy of Taylor and Francis, all COVID-19 related, peer-reviewed research published in this Special Issue is free to access and available for anyone to read. We sincerely hope that this Special Issue will offer more appreciation for COVID-19 research in particular and clinical development in general that could guide us through this pandemic and any future crises.

Funding

The author(s) reported there is no funding associated with the work featured in this article.