This section outlines the importance of framing an effective research question and how this enables research objectives and hypotheses in the field of RA to be generated. Factors to be considered when initially designing a research question are highlighted, as well as the process of expanding on the research question to develop clearly defined objectives. The concept of hypothesis testing is explored, and the use of an estimand framework to further refine the study question and objectives is explained.
This section introduces the key considerations to be taken when designing a study in RA to ensure it produces statistically sound and clinically meaningful results. The importance of minimising effects of inconsistency, variability and bias are discussed. The concepts of Type I and II errors, randomisation/stratification, blinding and propensity score matching are also explored.
This section explores the core elements of designing studies in RA. More specifically, the section introduces concepts of patient eligibility criteria, sample size, control groups, endpoints and outcome measures. It is important to carefully consider each of these elements when designing a study to ensure that the data generated are statistically robust. Basic principles of clinical data management are also discussed.
The first three sections in this module demonstrate how to design a clinical trial, develop a research question, and gather data to inform appropriate endpoints. In this section we consider how to analyse these study data. We introduce methods of data analysis, describe when each analysis type should be used, and explore how to overcome situations when data are missing.
This section will explain how a hierarchical model can be applied to rank scientific evidence at different levels based on robustness and discuss why randomised controlled trials (RCTs) are, in most scenarios, considered the ‘gold standard’ for research. The strengths of a randomised design will be reviewed and the need for further RCTs to address future questions in rheumatoid arthritis (RA) will be examined.
This section investigates how statistical analyses are currently used in RCTs within rheumatology and the challenges faced by both experts and investigators. We also elaborate on key topics in the statistical analysis of clinical trial data in RA, including alpha values, hierarchical testing, and statistical significance vs. clinical relevance.
This section will explore the limitations and interpretation of data from RCTs in RA. Assumptions underlying RCTs and how these dictate the validity of a study’s conclusions will be discussed. We will explore reasons why clinical trial outcomes are not always reflective of the outcomes that would be observed in a real-world clinical setting.
This section discusses the future use of RCTs in rheumatology, exploring examples of current and prospective ideas such as use of big data, machine learning and how the current COVID-19 pandemic may impact future clinical research.
This section will introduce RWD and how they are utilised as RWE within rheumatology and clinical practice. Key terminology and concepts regarding real-world studies are outlined and the advantages and limitations of RWE are discussed. This section also highlights examples of how RWE has addressed questions of academic interest and has provided clinical guidance to rheumatologists.
This section describes some of the major RA data currently available and held in electronic health records, registries, patient-generated databases and patient-reported outcomes, introducing retrospective or prospective observational studies, as well as how to find these data and the limitations of using this approach.
This section delves deeper into the methodological approaches to studying RWD once they have been collected. How should these data be studied? Who are the target audience? What risks and benefits are there for each approach?
This section will examine how RWD can be transformed into RWE, considering the statistical and analytical tools required to do so. We will also explore some of the uses of RWE, how it can impact patient care, and what is needed to optimise its use.
This introductory section will outline the most common types of clinical outcomes and endpoints used in rheumatology trials. Section 1 also addresses how these endpoints may be assessed during real-world practice, and discusses their clinical significance.
In Section 2, we suggest that traditional clinical endpoints may not always address all patient needs. Here, we introduce the importance of PROs in the management of patients with RMDs and how data from PRO endpoints can be incorporated into clinical decision-making.
Section 3 highlights current guidelines on the use of endpoints in rheumatology and discusses whether endpoints are always in alignment with patient needs, as introduced in Section 2. This section also explores issues involved in standardising endpoints across heterogeneous populations, as well as disparities in access to outcome assessment tools.
This final section will review data exploring clinical endpoints relevant to specific patient populations within rheumatology, which perhaps may not be assessed during clinical trials. We discuss what factors should be considered when assessing such populations and what challenges are faced. Section 4 will also explore the use of precision medicine in rheumatology, and how its application may impact outcome assessments.