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


methodologicalGuide4_5.shtml
Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

4.5. Social media and electronic devices

 

Technological advances have dramatically increased the range of data sources that can be used to complement traditional ones and provide compelling insights into effectiveness and safety of interventions. Such data include digital social media that exist in a computer-readable format on websites, web pages, blogs, vlogs, social networking sites, internet forums, chat rooms, health portals, etc. A recent addition to this list is represented by the data collected through mobile and other device applications such as wearable technology.

 

There is a growing interest to use these data sources to generate patient-generated information relevant for medicines safety surveillance.

 

Social media is a source of potential reports of suspected ADRs. Marketing authorisation holders (MAHs) are legally obliged to screen web sites under their management and assess whether they qualify for reporting Spontaneous ADRs identified from social media should be handled as unsolicited reports and evaluated and reported in a similar way.

 

Social media is already being used to provide insights into the patient’s perception of the effectiveness of drugs and for the collection of patient reported outcomes (PROs) as discussed in Web-based patient-reported outcomes in drug safety and risk management: challenges and opportunities? (Drug Saf. 2012;35(6):437-46).

 

Another use of social media might be for identification of new safety issues (signal detection). It would have added value only if more issues would be identified or it would help faster identification, but there is currently no evidence this is the case. Using Social Media Data in Routine Pharmacovigilance: A Pilot Study to Identify Safety Signals and Patient Perspectives (Pharm Med (2017) 31: 167-174) explores whether analysis of social media data could identify new signals, known signals from routine pharmacovigilance, known signals sooner, and specific issues (i.e., quality issues and patient perspectives). Also of interest in this study was to determine the quantity of ‘posts with resemblance to AEs’ (proto-AEs) and the types and characteristics of products that would benefit from social media analysis. It concludes that social media data analysis cannot identify new safety signals but can provide unique insight into the patient perspective. Assessment was limited by numerous factors, such as data acquisition, language, and demographics. Further research is deemed necessary to determine the best uses of social media data to augment traditional pharmacovigilance surveillance.

 

There is one ongoing EU project investigating the potential for publicly available social media data for identifying drug safety issues (WEB-RADR). The results of the WEB-RADR project will inform regulatory policy on the use of social media for pharmacovigilance, initial results show there may be utility for specific niche areas such as misuse/abuse or off-label use.

 

While offering the promise of new research models and approaches, the rapidly evolving social media environment presents many challenges including the need for strong and systematic processes for selection, validation and study implementation. Articles which detail associated challenges are: Evaluating Social Media Networks in Medicines Safety Surveillance: Two Case Studies (Drug Saf. 2015; 38(10): 921–930.) and Social media and pharmacovigilance: A review of the opportunities and challenges (Br J Clin Pharmacol. 2015 Oct; 80(4): 910–920).

 

There is currently no defined strategy or framework in place in order to meet the standards around data validity, generalisability for this type of data. Therefore regulatory acceptance of this type of data might be lower than for traditional sources.

 

More tools and solutions for analysing unstructured data, especially for pharmacoepidemiology and drug safety research, are becoming available, as in Deep learning for pharmacovigilance: recurrent neural network architectures for labelling adverse drug reactions in Twitter posts (J Am Med Inform Assoc. 2017 Feb 22) and Social Media Listening for Routine Post-Marketing Safety Surveillance (Drug Saf. 2016;39(5):443-54).

 

Before an informed strategy is put in place, the following factors may be considered when using social media and electronic data sources and devices using social media:

  • Completeness of data capture.
  • Validation processes defined for the devices, including accuracy
  • Reliability and reproducibility of outputs/inputs from the device
  • Data warehousing requirements for secure storage of the volume of data potentially received from wearable devices.

Data from social media and electronic devices can be both structured and unstructured. When analysing unstructured data, the following factors may be considered:

  • Tools used for crawling the web and the methods used for handling unstructured data should be well defined along with their potential limitations e.g. the type of natural language processing (NLP) approach and software used.
  • How exposure and outcomes were defined within unstructured data and whether these have been derived and validated.

 

 

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