Generating evidence involves three steps: asking the right research questions, finding or collecting fit-for-purpose data, and conducting the appropriate analyses. The first step in any research is to formulate the research question clearly and accurately. The research question should stem from the problem or gap in knowledge to be addressed and should be supported by a theoretical framework. The research question should be formulated in collaboration with the primary end-users of the study results and state who will be these end-users, e.g. patients, health care professionals, regulators or public health authorities, health technology assessment organisations, payers, pharmaceutical company or research community. It should state the ‘why’ (main justification for starting the research), the ‘what’ (exposure and endpoints), the ‘who’ (target population), the ‘how’ (main study design) and the ‘when’ (time period of the study) of the research in a way that helps understanding the choice of study objectives and methods. It should make it clear whether a hypothesis will be tested and, in this case, whether the hypothesis is pre-specified or data driven.
Previous findings are useful for the methodological planning of the current study and may support the background, research question, hypotheses and design of the proposed study. They may also serve to determine the expected effect size and, if available in the target population, to characterise risk factors for the event, to identify relevant outcomes and measures and to assess the feasibility of the proposed study. A critical and thorough review of the literature forms the basis for the theoretical framework of the research question and should usually be included in the background section of a protocol. Such a review aims to evaluate current evidence around the question at hand and identify gaps in knowledge that a study is intended to fill.
How to formulate research recommendations (BMJ 2006;333:804-6) proposes the EPICOT format with 5 core elements for research recommendations on the effects of treatments: Evidence (source of the current evidence), Population (population characterised by any diagnosis, disease stage, comorbidity, risk factor, sex, age, ethnic group, specific inclusion or exclusion criteria, clinical setting), Intervention (type, frequency, dose, duration, prognostic factor), Comparison (placebo, routine care, alternative treatment/management), Outcome (which clinical or patient related outcomes will the researcher need to measure, improve, influence or accomplish; which methods of measurement should be used), and Time stamp (date of literature search or recommendation). This format was adopted by the European network of Health Technology Assessment (EUnetHTA) in its Position paper on how to formulate research recommendations. Chapter 5.7 and Annex 1 present methods for reviewing and synthesising findings from the literature through the means of systematic review and meta-analysis.
Research questions relevant to regulatory authorities and health technology assessment bodies regarding the utilisation, safety, efficacy and impact of medicines are detailed the European Public Assessment Report (EPAR) available for each centrally authorised product on the EMA website, with general pharmacovigilance related aspects being described in Modules of the Good Pharmacovigilance Practices (GVP), and The criteria to select and prioritise health technologies for additional evidence generation document.
When the study data source is not well characterised or known, a feasibility study should be considered. The aim of a feasibility study is not to answer the research question directly but to determine whether the data source could answer it within the expected timelines and what is the required statistical power for the proposed study design. Feasibility studies can provide information on the number of people with a specific exposure or outcome, the availability of covariates and the follow up period needed. A feasibility study can also provide insights into the potential difficulties which may be encountered in the conduct of the study or which may introduce bias. Importance of feasibility assessments before implementing non‐interventional pharmacoepidemiologic studies of vaccines: lessons learned and recommendations for future studies (Pharmacoepidemiol Drug Saf. 2016;25(12):1397-406) illustrates a vaccine manufacturer’s pragmatic approach for conducting feasibility assessments for post-authorisation studies required to address regulatory requests. The ISPE Good pharmacoepidemiology practice (GPP) explains how a data collection method or data source can answer a research question with justifications coming from the feasibility study when relevant.