Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models' performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain.

As the scientific research community along with health care professionals and decision-makers around the world fight tirelessly against the COVID-19 pandemic, the need for comparative effectiveness research (CER) on preventive and therapeutic interventions for COVID-19 is immense. Randomized controlled trials markedly underrepresent the frail and complex patients seen in routine care, and they do not typically have data on long-term treatment effects. The increasing availability of electronic health records (EHRs) for clinical research offers the opportunity to generate timely real-world evidence reflective of routine care for optimal management of COVID-19. However, there are many potential threats to the validity of CER based on EHR data that are not originally generated for research purposes. To ensure unbiased and robust results, we need high-quality healthcare databases, rigorous study designs, and proper implementation of appropriate statistical methods. We aimed to describe opportunities and challenges in EHR-based CER for COVID-19-related questions and to introduce best practices in pharmacoepidemiology to minimize potential biases. We structured our discussion into the following topics: 1) Study population identification based on exposure status; 2) Ascertainment of outcomes; 3) Common biases and potential solutions; and 4) Data operational challenges specific to COVID-19 CER using EHR. We provide structured guidance for the proper conduct and appraisal of drug and vaccine effectiveness and safety research using EHR data for the pandemic. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE).