In Silico Trials of Surgical Interventions - Using Routinely Collected Data to Model Trial Feasibility and Design Efficiency In Vivo Randomised Controlled Trials

Routinely collected health data such as National Health Service Hospital Episode Statistics (HES) contains a wealth of real-world longitudinal patient health data including demographics, diagnoses, procedures and other clinical information. These data can be used to address many of the existing challenges in the design and conduct of clinical trials by optimising trial design and simplifying the assessment of adherence, safety, and outcomes. When a trial concept is initiated, researchers may use HES data to explore the hypothesis and assess trial feasibility. The large-volume patient data enables a detailed understanding of the characteristics of the target patient populations and the estimation of the real-world treatment effects across different patient groups thus enabling identification of targeted populations for specific interventions. By tapping into this resource and using advanced statistical and machine learning methods, the investigators can emulate the trial and thus obtain the key trial parameters required for designing a clinical trial, minimise the number of assumptions imputed and make the design and development of clinical trials quicker, simpler, and more reliable.