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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

4.1. Primary data collection

 

Collection of specific data for a study has played an important role in pharmacoepidemiology and methodological aspects for the conduct of primary data collection studies are well covered in the textbooks and guidelines referred to in the Introduction section. . Annex 1 of Module VIII of the Good pharmacovigilance practice provides includes examples of different study designs based on prospective primary data collection such as cross-sectional study, prospective cohort study, active surveillance, etc. Survey method and randomised controlled trials as examples of primary data collections are presented.

 

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).

 

4.1.1. Surveys

 

A survey is a data collection tool used to gather information about individuals. Surveys are commonly used to collect self-reported data, either on factual information about individuals, or their opinions. They generally have a cross-sectional design and represent a form primary data collection conducted through questionnaires administered by web, phone or paper.

 

Although used for a long time in other areas as social science or marketing, surveys are nowadays also increasingly used in pharmacoepidemiology, especially in the areas of epidemiology and evaluation of risk minimisation measure (RMM) effectiveness.

 

Questionnaires used in surveys should be validated based on accepted measures including construct, criterion and content validity, inter-rater and test-retest reliability, sensitivity and responsiveness.

Recommendations with regards to data collection, which medium to use, how to recruit a representative sample and how to formulate the questions in a non-directive way to avoid information bias, are described in the following textbooks: Survey Sampling (L. Kish, Wiley, 1995) and Survey Methodology (R.M. Groves, F.J. Fowler, M.P. Couper et al., 2nd Edition, Wiley 2009).

 

Although primarily focused on quality of life research, 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 questionnaires in different settings. Health Measurement Scales: a practical guide to their development and use (D. L. Streiner, G. R. Norman, 4th Edition, Oxford University Press, 2008) is a very helpful guide to those involved in measuring subjective states and learning style in patients and healthcare providers.

 

Representativeness is an important element for surveys; the included sample should be representative of the target population and must be defined with regards to the research question. For example, if the objective of the survey is to evaluate whether the RMM are distributed among the right target population, the lists which are used for the distribution of the RMM material cannot be used as the source population for sampling.

 

The response rate is also an important metric of survey and it should be reported for each survey based on a standard definition so that the comparison among different surveys is possible. Standard Definitions. Final Dispositions of Case Codes and Outcome Rates for Surveys of the American Association for Public Opinion Research provides standard definitions which can be adapted to the context of pharmacoepidemiological surveys. The overall response rate of participation remains low in telephone surveys (J.M. Lepkowski, N.C. Tucker, J.M Bricket al., Ed. Advances in telephone survey methodology Wiley 2007, Part V) and is important to counteract since it leads to lack of power and reduced representativeness. These measures include the use of short or personalised questionnaires approved by professional associations.

 

4.1.2. Randomised clinical trials

 

Randomised clinical trials are another form of 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) Note for Guidance on Good Clinical Practice, which specifies obligations for the conduct of clinical trials to ensure that the data generated in the trial are valid.

 

 

 

Individual Chapters:

 

1. Introduction

2. Formulating the research question

3. Development of the study protocol

4. Approaches to data collection

4.1. Primary data collection

4.1.1. Surveys

4.1.2. Randomised clinical trials

4.2. Secondary data collection

4.3. Patient registries

4.3.1. Definition

4.3.2. Conceptual differences between a registry and a study

4.3.3. Methodological guidance

4.3.4. Registries which capture special populations

4.3.5. Disease registries in regulatory practice and health technology assessment

4.4. Spontaneous report database

4.5. Social media and electronic devices

4.6. Research networks

4.6.1. General considerations

4.6.2. Models of studies using multiple data sources

4.6.3. Challenges of different models

5. Study design and methods

5.1. Definition and validation of drug exposure, outcomes and covariates

5.1.1. Assessment of exposure

5.1.2. Assessment of outcomes

5.1.3. Assessment of covariates

5.1.4. Validation

5.2. Bias and confounding

5.2.1. Selection bias

5.2.2. Information bias

5.2.3. Confounding

5.3. Methods to handle bias and confounding

5.3.1. New-user designs

5.3.2. Case-only designs

5.3.3. Disease risk scores

5.3.4. Propensity scores

5.3.5. Instrumental variables

5.3.6. Prior event rate ratios

5.3.7. Handling time-dependent confounding in the analysis

5.4. Effect measure modification and interaction

5.5. Ecological analyses and case-population studies

5.6. Pragmatic trials and large simple trials

5.6.1. Pragmatic trials

5.6.2. Large simple trials

5.6.3. Randomised database studies

5.7. Systematic reviews and meta-analysis

5.8. Signal detection methodology and application

6. The statistical analysis plan

6.1. General considerations

6.2. Statistical analysis plan structure

6.3. Handling of missing data

7. Quality management

8. Dissemination and reporting

8.1. Principles of communication

8.2. Communication of study results

9. Data protection and ethical aspects

9.1. Patient and data protection

9.2. Scientific integrity and ethical conduct

10. Specific topics

10.1. Comparative effectiveness research

10.1.1. Introduction

10.1.2. General aspects

10.1.3. Prominent issues in CER

10.2. Vaccine safety and effectiveness

10.2.1. Vaccine safety

10.2.2. Vaccine effectiveness

10.3. Design and analysis of pharmacogenetic studies

10.3.1. Introduction

10.3.2. Identification of generic variants

10.3.3. Study designs

10.3.4. Data collection

10.3.5. Data analysis

10.3.6. Reporting

10.3.7. Clinical practice guidelines

10.3.8. Resources

Annex 1. Guidance on conducting systematic revies and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes