15 General Introduction 1 exchangeable44. A common approach is to correct for known confounders that cause this lack of exchangeability between treated and untreated groups in observational data44. Correcting for a confounding variable produces conditional exchangeability within levels of the confounding variable, and can help with calculating a causal effect estimate, but only if we assume that there is no residual or unmeasured confounding44. In the second part of this thesis focuses on answering specific clinical questions using opportunistic, large datasets, using various epidemiological approaches to infer causal relations. In Chapter 4 the International Severe Acute Respiratory and emerging Infections Consortium Coronavirus Clinical Characterisation Consortium (ISARIC4C) Clinical Characterisation Protocol UK (CCP-UK) dataset was used to study the relationship between the need for invasive mechanical ventilation, mortality and co-infections in SARS-CoV-2. The ISARIC4C dataset consists of data from over 300.000 hospitalised COVID-19 patients from over 250 hospitals across the UK. A SARS-CoV-2 mono infection was compared with a SARS-CoV-2 co-infection with influenza viruses, adenovirus or respiratory syncytial virus (RSV). Inverse probability weighting was used to correct for the increased likelihood of testing in patients that were severely ill or admitted to the intensive care unit. Chapter 5 is a natural experiment, in which the dosing of IL-6 inhibitor for treatment of hospitalised COVID-19 patients was determined by the time of hospitalisation. Due to drug shortages, different doses of tocilizumab and sarilumab were recommended in different time periods in 2021 in the Netherlands. Using real world claims data, we compared the effectiveness of different doses of these IL6 inhibitors. For final chapter of the second part of this thesis, Chapter 6, data from the National Intensive Care Evaluation (NICE) registry was used, to study the relationship between cardiac surgery and influenza-like-illness (ILI) season. The duration of IMV was utilised as a proxy for viral respiratory disease, and was compared between patients who underwent elective cardiac surgery in ILI season compared to patients who underwent surgery in a season with low incidence of viral respiratory disease. Facilitating causal inference The third part of this thesis explores different methods and approaches that can increase the efficiency of causal inference. Apart from meta-analyses, RCTs provide the most reliable evidence for the effectiveness of interventions, and should ideally use patient relevant outcome measures, such as mortality47. A downside of many patient relevant outcomes is that follow-up time is long and a large sample size is needed, both leading to higher costs. Especially in therapeutic studies in COVID-19 (or other emerging viruses), in which answers are needed rapidly, it is vital to assess therapeutic options efficiently and accurately in early-stage clinical studies. A possible way to improve the efficiency of
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