Target trial emulations to bridge the evidence gap between COVID-19 treatment trials and observational cohorts - from application to guidance

Background: The COVID-19 pandemic resulted in many deaths worldwide and remains an important cause of death in the unvaccinated, those with comorbid illness, compromised immune function, or older adults. Randomized clinical trials (RCTs) evaluated various treatments for COVID-19, such as the antiviral remdesivir or the janus kinase inhibitor baricitinib, and their results were subsequently synthesized in systematic reviews and meta-analyses. However, most of these RCTs were conducted before widespread circulation of the current virus strains, among unvaccinated patients, mostly patients with adequate immune function and studied short-term outcomes only. Current clinical guidelines lack evidence-based guidance for several important hospitalised populations. Target trial emulations (TTEs) offer a comprehensive causal inference framework to assess comparative effectiveness of treatments and can answer additional clinical questions in areas where RCTs are not available, not feasible, not ethical, or not timely. Objectives: The objectives of my research project are twofold. First, to provide updated treatment effect estimates for remdesivir and baricitinib among hospitalized COVID-19 patients, focusing on vulnerable populations and long-term outcomes (Objective 1). Second, to establish infrastructure (Ancillary Objective 1.1) and guidance (Objective 2) for future TTEs on hospitalised patients affected by emerging infectious diseases.Methods: For Objective 1, I will collaborate with the OpenSAFELY Trusted Research Environment (www.opensafely.org), which contains National Health Service data from 40% of the English population, linked to secondary care data and other national registries. I will conduct several TTEs to answer the clinical questions of this objective and benchmark the analyses to my own individual patient data meta-analyses. For Ancillary Objective 1.1, I will conduct a mapping review to identify other suitable secondary care cohorts for TTEs in infectious diseases. Regarding Objective 2, I will extend the Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tool to assess the risk of bias in causal inference studies.Output, Impact, Outlook: The findings will be published in peer-reviewed journals and presented at scientific conferences. The TTEs will provide lacking evidence for clinical guidelines. OpenSAFELY has been a major source of innovative and robust scientific output directly impacting policy in the field of COVID-19. The mapping review will provide a comprehensive overview of databases for future TTEs, and the ROBINS-I extension will provide researchers with guidance to assess risk of bias in TTEs and other causal inference studies. This project will enable me to complement my RCT and meta-analysis knowledge with advanced causal inference skills on an applied (Objective 1) and meta-research (Objective 2) level, collaborate with pioneers in the field of TTEs, expand my international scientific network, conceptualise future collaborative research ideas, and eventually build up my own research group on return, applying for an SNSF Ambizione grant.