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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

 

Section 3.5. Using data from social media and electronic devices as a data source

Technological advances have dramatically increased the range of tools that can be used to identify, generate and manage electronic data with the potential of providing compelling insights into effectiveness and safety of interventions. Such data include digital social media (e.g. audio, video and images) that exist in a computer-readable format on websites, web pages, blogs, vlogs, social networking sites, internet forums, chat rooms, health portals, etc. To this list, can be added the data collected through mobile and other device applications such as wearable technology. By 2020, it is estimated that the amount of data recorded on digital media will reach 44 zettabytes (http://wikibon.org/blog/big-data-statistics/) 9% of which will be related to healthcare, of which half will be related to drugs.

 

3.5.1. General considerations

 

Marketing Authorisation Holders (MAHs) are legally obliged to screen web sites under their management for potential reports of suspected ADRs and assessed whether they qualify for reporting and MAHs are encouraged to use their websites to facilitate ADR data collection. Cases from the Internet should also 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).

 

While offering the promise of new research models and approaches, this rapidly evolving marketplace presents equal challenges.  Without strong and systemic processes to manage new devices, simply keeping up to date and evaluating their suitability for a study will be challenging. There is currently no defined strategy or framework in place in order to meet the standards around data validity, generalizability, and likely regulatory acceptance for using this type of data.  Current tools and solutions for analyzing unstructured data, especially for pharmacoepidemiology and drug safety research, are becoming available. A framework for use is evolving and proven models will emerge that ensure  data content, rigor, and quality matched to intended use, as well as accompanying methods and solutions to validate these data. In the meantime, the following factors should be considered for data source and devices using this media:

  • The technology should be evaluated for reliability and “sameness” of outputs/inputs from the device.

  • Processes should be defined through which data can be validated e.g. originating from the individual as opposed to someone else using the device, and the accuracy of the device reading.

  • The completeness of data capture.

  • Data privacy and accessibility for longitudinal datasets in large populations.

  • Data warehousing requirements to securely store the volume of data potentially received from wearable devices.

When analysing unstructured data for pharmacoepidemiology and drug safety research, the following factors should be considered:

  • Tools used for trawling 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. General aspects of study protocol

2. Research question

3. Approaches to data collection

3.1. Primary data collection

3.2. Secondary use of data

3.3. Research networks

3.4. Spontaneous report database

3.5. Using data from social media and electronic devices as a data source

3.5.1. General considerations

4. Study design and methods

4.1. General considerations

4.2. Challenges and lessons learned

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

4.2.1.1. Assessment of exposure

4.2.1.2. Assessment of outcomes

4.2.1.3. Assessment of covariates

4.2.1.4. Validation

4.2.2. Bias and confounding

4.2.2.1. Choice of exposure risk windows

4.2.2.2. Time-related bias

4.2.2.2.1. Immortal time bias

4.2.2.2.2. Other forms of time-related bias

4.2.2.3. Confounding by indication

4.2.2.4. Protopathic bias

4.2.2.5. Surveillance bias

4.2.2.6. Unmeasured confounding

4.2.3. Methods to handle bias and confounding

4.2.3.1. New-user designs

4.2.3.2. Case-only designs

4.2.3.3. Disease risk scores

4.2.3.4. Propensity scores

4.2.3.5. Instrumental variables

4.2.3.6. Prior event rate ratios

4.2.3.7. Handling time-dependent confounding in the analysis

4.2.4. Effect modification

4.3. Ecological analyses and case-population studies

4.4. Hybrid studies

4.4.1. Pragmatic trials

4.4.2. Large simple trials

4.4.3. Randomised database studies

4.5. Systematic review and meta-analysis

4.6. Signal detection methodology and application

5. The statistical analysis plan

5.1. General considerations

5.2. Statistical plan

5.3. Handling of missing data

6. Quality management

7. Communication

7.1. Principles of communication

7.2. Guidelines on communication of studies

8. Legal context

8.1. Ethical conduct, patient and data protection

8.2. Pharmacovigilance legislation

8.3. Reporting of adverse events/reactions

9. Specific topics

9.1. Comparative effectiveness research

9.1.1. Introduction

9.1.2. General aspects

9.1.3. Prominent issues in CER

9.1.3.1. Randomised clinical trials vs. observational studies

9.1.3.2. Use of electronic healthcare databases

9.1.3.3. Bias and confounding in observational CER

9.2. Vaccine safety and effectiveness

9.2.1. Vaccine safety

9.2.1.1. General aspects

9.2.1.2. Signal detection

9.2.1.3. Signal refinement

9.2.1.4. Hypothesis testing studies

9.2.1.5. Meta-analyses

9.2.1.6. Studies on vaccine safety in special populations

9.2.2. Vaccine effectiveness

9.2.2.1. Definitions

9.2.2.2. Traditional cohort and case-control studies

9.2.2.3. Screening method

9.2.2.4. Indirect cohort (Broome) method

9.2.2.5. Density case-control design

9.2.2.6. Test negative design

9.2.2.7. Case coverage design

9.2.2.8. Impact assessment

9.2.2.9. Methods to study waning immunity

9.3. Design and analysis of pharmacogenetic studies

9.3.1. Introduction

9.3.2. Identification of genetic variants

9.3.3. Study designs

9.3.4. Data collection

9.3.5. Data analysis

9.3.6. Reporting

9.3.7. Clinical practice guidelines

9.3.8. Resources

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