8.1.1. General considerations
8.1.3. Randomised controlled trials
The methodological aspects of studies using primary data collection (also sometimes referred to as field studies or prospective studies) are well covered in the textbooks and guidelines referred to in the Introduction. Annex 1 of Module VIII of the Good pharmacovigilance practice provides examples of study designs based on prospective/primary data collection, such as cross-sectional study, prospective cohort study, and active surveillance. For completeness, surveys and randomised controlled trials are also presented below as examples of primary data collection.
Studies using primary data collection in clinical care or community-based settings have allowed the evaluation of drug-disease associations for rare complex conditions that require very large source populations and in-depth case assessment by clinical experts. Classic historical examples are Appetite-Suppressant Drugs and the Risk of Primary Pulmonary Hypertension (N Engl J Med. 1996;335:609-16), The design of a study of the drug etiology of agranulocytosis and aplastic anemia (Eur J Clin Pharmacol. 1983;24:833-6) and Medication Use and the Risk of Stevens–Johnson Syndrome or Toxic Epidermal Necrolysis (N Engl J Med. 1995;333:1600-8). For some conditions, case-control surveillance networks have been developed and used for selected studies and for signal generation and evaluation, e.g., Signal generation and clarification: use of case-control data (Pharmacoepidemiol Drug Saf 2001;10:197-203).
Data can be collected using paper, electronic case report forms or, increasingly, study-specific smartphone or web applications provided to patients. This approach has been used during the COVID-19 pandemic, as illustrated, for example, in COVID-19 vaccine waning and effectiveness and side-effects of boosters: a prospective community study from the ZOE COVID Study (Lancet Infect Dis. 2022:S1473-3099(22)00146-3): in this longitudinal, prospective, community-based study, data on demographic characteristics, comorbidities, symptoms, SARS-CoV-2 tests and results, and vaccinations, were self-reported through an app, with participants prompted to daily reporting through app notifications. Possibilities, Problems, and Perspectives of Data Collection by Mobile Apps in Longitudinal Epidemiological Studies: Scoping Review (J Med Internet Res. 2021;23(1):e17691) concludes that using mobile technologies can help to overcome challenges linked to data collection in epidemiological research, but the applicability and acceptance of these mobile apps in various subpopulations vary and need to be further studied. In addition, self-reported data may introduce information bias or selection bias, and since participants are self-selected, they might not be fully representative of the general population.
The book Research Methods in Education (J. Check, RK. Schutt, Sage Publications, 2011) defines survey research as "the collection of information from a sample of individuals through their responses to questions" (p. 160). This type of research allows for a variety of methods to recruit participants, collects data and utilises various instruments.
A survey is the collection of data on specific health and quality of life aspects, knowledge, attitudes, behaviour, practices, opinions, beliefs, or feelings of selected groups of individuals from a specific sampling frame, by asking them questions in person or by post, phone or online. They generally have a cross-sectional design, but repeated measures over time may be performed for the assessment of trends.
Surveys have long been used in fields such as market research, social sciences and epidemiology. General guidance on constructing and testing the survey questionnaire, modes of data collection, sampling frames and ways to achieve representativeness can be found in general texts (Survey Sampling (L. Kish, Wiley, 1995) and Survey Methodology (R.M. Groves, F.J. Fowler, M.P. Couper et al., 2nd Edition, Wiley 2009). The book Quality of Life: the assessment, analysis and interpretation of patient-related outcomes (P.M. Fayers, D. Machin, 3rd Edition, Wiley, 2016) offers a comprehensive review of the theory and practice of developing, testing and analysing health-related quality of life questionnaires in different settings.
Surveys have an important role in the evaluation of the effectiveness of risk minimisation measures (RMM) or of a risk evaluation and mitigation strategy (REMS) (see Chapter 16.4). The application of methods described in the aforementioned textbooks needs adaptation for surveys to evaluate the effectiveness of RMM or REMS. For example, the extensive methods for questionnaire development of quality of life scales (construct, criterion and content validity, inter-rater and test-retest reliability, sensitivity and responsiveness) are not appropriate to questionnaires for RMM which are often used only once. The EMA and FDA issued guidance documents on the conduct of surveys for risk minimisation (RM) which, together, encompass the selection of risk minimisation measures, study design, instrument development, data collection, processing and data analysis and presentation of results. This guidance include the draft EMA Guideline on good pharmacovigilance practices (GVP) Module XVI – Risk minimisation measures: selection of tools and effectiveness indicators (Rev 3) (2021), the FDA draft guidance for industry REMS Assessment: Planning and Reporting on REMS (2019) and the FDA Guidance on Survey Methodologies to Assess REMS Goals That Relate to Knowledge (2019). A checklist to assess the quality of studies evaluating risk management programs is provided in The RIMES Statement: A Checklist to Assess the Quality of Studies Evaluating Risk Minimization Programs for Medicinal Products (Drug Saf. 2018;41(4): 389-401). The article Are Risk Minimization Measures for Approved Drugs in Europe Effective? A Systematic Review (Expert Opin Drug Saf. 2019;18(5):443-54) highlights the need for improvement in the methods and presentation of results and for more hybrid designs that link survey data with health and safety outcomes as requested by regulators. This article also reports on low response rates found in many studies, allowing for the possibility of important bias. The response rate should therefore be reported in a standardised way in surveys to allow comparisons. Standard Definitions. Final Dispositions of Case Codes and Outcome Rates for Surveys (2016) of the American Association for Public Opinion Research provides standard definitions which can be adapted to RM surveys and the FDA Guidance on Survey Methodologies to Assess REMS Goals That Relate to Knowledge (2019) provides guidance for RM surveys.
An important aspect of surveys is sampling, often using a clustered random sample. However, attention shall be paid to the selection of the original list of subjects in the target population. For example, if the evaluation of the awareness about an educational material is part of the objectives, the same lists which were used to distribute the educational material cannot be used for sampling the survey, otherwise a selection bias cannot be excluded.
The increasing use of online RMM require that survey methods adapt but should not sacrifice representativeness by accessing only populations which visit these websites. They should provide evidence that the results using these sampling methods are not biased. Similarly, the increasing use of healthcare professional and patient panels needs to ensure that survey methods do not sacrifice representativeness by accessing only self-selected participants in these panels and should provide evidence that the results are not biased by using these convenient sampling frames. The influence of information given to survey subjects about the survey prior to its completion should attempt to minimise the influence of this information to reduce bias.
The issue of thresholds to assess the effectiveness of RMM remains a topic of debate. This topic is discussed in the aforementioned EMA and FDA documents and the article Are Risk Minimization Measures for Approved Drugs in Europe Effective? A Systematic Review (Expert Opin Drug Saf. 2019;18(5):443-54). The thresholds need to be viewed in the context of their potential impact on the benefit-risk balance. Composite thresholds for all of three aspects (awareness, knowledge, and behaviour) of RM effectiveness are hardly achieved.
The draft EMA Guideline on good pharmacovigilance practices (GVP) Module XVI – Risk minimisation measures: selection of tools and effectiveness indicators (Rev 3) (2021) encourages the evaluation of process indicators being linked to health outcomes. A holistic evaluation of non-targeted effects as well as product-specific targeted effects has so far been performed in only a minority of studies, as shown in Risk Minimisation Evaluation with Process Indicators and Behavioural or Health Outcomes in Europe: Systematic Review (Expert Opin Drug Saf. 2019;18(5):443-54).
Randomised controlled trials are an experimental design that involves primary data collection. There are numerous textbooks and publications on methodological and operational aspects of clinical trials which are not covered here. An essential guideline on clinical trials is the European Medicines Agency (EMA) Guideline for good clinical practice E6(R2), which specifies obligations for the conduct of clinical trials to ensure that the data generated in the trial are valid. From a legal perspective, the Volume 10 of the Rules Governing Medicinal Products in the European Union contains all guidance and legislation relevant for conduct of clinical trials. A number of documents are under revision.
The way clinical trials are conducted in the European Union (EU) has undergone a major change when the Clinical Trial Regulation (Regulation (EU) No 536/2014) came into effect and replaced the existing Directive 2001/20/EC.
Hybrid data collection as used in pragmatic trials, large simple trials and randomised database studies are described in Chapter 4.2.7.