Print page Resize text Change font-size Change font-size Change font-size High contrast


methodologicalGuide4_1_1.shtml
Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

 

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.

 

 

 

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