16 Chapter 1 clinical trials -by decreasing sample size and follow-up time- is to use intermediate or surrogate outcomes. For example, HbA1c could be used as a surrogate for micro- and macrovascular complications in type II diabetes mellitus48. Continuous (numerical) endpoints also decrease sample size, further improving the efficiency of a trial49. A commonly used intermediate endpoint is the WHO ordinal scale, but this has several downsides. Developing a surrogate endpoint with a closer relation to the definitive endpoint can improve causal inference, by providing a better assessment of the definitive endpoint. In Chapter 7, an intermediate endpoint for clinical trials in COVID-19 was developed and evaluated. This intermediate endpoint is a measure for pulmonary oxygenation function (S/F94) and we compare this measure to other commonly used outcome measures in clinical trials for COVID-19. Another method to facilitate more efficient causal inference is by removing noise generated by including patients with divergent disease processes. There are many diseases that present as clinically heterogeneous syndromes, and identifying clinically relevant subgroups can be useful to provide better care for patients: subgroup specific treatments, accurately predicting risks or estimating prognosis are a few of the possible benefits50,51. Latent Class Analysis (LCA) is a frequently used unsupervised modelling approach to identify unobserved (“latent”) homogeneous groups (clusters) of people within a larger, heterogeneous population51. For example, within the population of Acute Respiratory Distress Syndrome (ARDS) patients, different subgroups -identified using LCA- responded differently to therapy52,53. This means that future clinical trials can be better aimed at patient groups that are most likely to benefit. Clusters are formed based on indicator variables, such as demographics and comorbidities. Within clusters, the distribution of these indicator variables is similar, but different from those in other clusters. In the final chapter of the third section of this thesis Chapter 8, LCA was utilised to identify different subgroups in Staphylococcus Aureus Bacteraemia (SAB) patients. A summary of the studies in part I, II and III, a reflection on the different approaches and contemplation of future uses of large datasets is given in the final chapter of this thesis: Chapter 9.
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