The methodological aspects of primary data collection studies are well covered in the textbooks and guidelines referred to in the Introduction chapter. Annex 1 of Module VIII of the Good pharmacovigilance practice provides examples of different study designs based on prospective primary data collection such as cross-sectional study, prospective cohort study, active surveillance. Surveys and randomised controlled trials are also presented below as examples of primary data collection.
Studies using hospital or community-based primary data collection 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 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 clarification, e.g. Signal generation and clarification: use of case-control data (Pharmacoepidemiol Drug Saf 2001;10:197-203).
A survey is the collection of data on knowledge, attitudes, behaviour, practices, opinions, beliefs or feelings of selected groups of individuals, by asking them in person, on paper, by phone or online from some sampling frame. They generally have a cross-sectional design, but repeated measures overtime may apply for trends assessment.
Surveys have been used for a long time in fields such as marketing, social science 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, 2nd Edition, Wiley, 2007) offers a comprehensive review of the theory and practice of developing, testing and analysing 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 5.9). The application of methods described in these 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 in RM which are often used only once. The EMA and FDA issued guidance documents on the conduct of surveys for 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 EMA Guideline on good pharmacovigilance practices (GVP) Module XVI (2017), 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 RM 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.
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 health care 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.
Randomised clinical trials is an experimental design that involves primary data collection. There are numerous textbooks and publications on methodological and operational aspects of clinical trials and they 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) will undergo a major change when the Clinical Trial Regulation (Regulation (EU) No 536/2014) will fully come into effect and will replace 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 5.6.