var _paq = window._paq = window._paq || []; /* tracker methods like "setCustomDimension" should be called before "trackPageView" */ _paq.push(['trackPageView']); _paq.push(['enableLinkTracking']); (function() { var u="https://rwrregs.matomo.cloud/"; _paq.push(['setTrackerUrl', u+'matomo.php']); _paq.push(['setSiteId', '1']); var d=document, g=d.createElement('script'), s=d.getElementsByTagName('script')[0]; g.async=true; g.data-privacy-src='//cdn.matomo.cloud/rwrregs.matomo.cloud/matomo.js'; s.parentNode.insertBefore(g,s); })();

Real World Evidence (RWE) 101 – ISPE GPP

RWE 101 – ISPE GPP

The International Society of Pharmacoepidemiology (ISPE) Good Pharmacoepidemiology Practices (GPP) provides guidance for the conduct and reporting of pharmacoepidemiologic studies. The key points of ISPE GPP include:

Study Design and Conduct: ISPE GPP emphasizes the importance of study design and conduct to ensure the validity and reliability of study results. The guide provides guidance on study design, sample selection, data collection, and analysis.

Data Sources and Quality: ISPE GPP provides guidance on the selection and use of data sources, such as administrative databases, electronic health records, and patient registries. The guide emphasizes the importance of data quality and the need to validate data sources and ensure data completeness.

Ethical Considerations: ISPE GPP emphasizes the importance of ethical considerations in pharmacoepidemiologic studies, including informed consent, confidentiality, and protection of human subjects.

Reporting and Dissemination of Results: ISPE GPP provides guidance on the reporting and dissemination of study results, including the need to provide clear and transparent reporting of study methods, results, and limitations.

Collaboration and Communication: ISPE GPP emphasizes the importance of collaboration and communication among researchers, stakeholders, and the public to ensure the appropriate use and interpretation of study results.

Share this story...

Real World Evidence (RWE) 101 – ISPE GPP2023-08-07T22:59:38+00:00

Real World Evidence (RWE) 101 – EMA Good Pharmacovigilance Practices (GVPs)

RWE 101 – EMA Good Pharmacovigilance Practices (GVPs)

The European Medicines Agency’s (EMA) Good Pharmacovigilance Practices (GVPs) provide a framework for the monitoring and reporting of adverse drug reactions (ADRs) to ensure the safety and efficacy of medicines. In the context of real-world evidence, GVPs play an important role in ensuring the quality and reliability of data collected from real-world studies.

Real-world evidence refers to data collected from sources outside of traditional clinical trials, such as electronic health records, patient registries, and observational studies. This type of data is becoming increasingly important in drug development and regulatory decision-making, as it provides valuable insights into how medicines perform in real-world settings.

To ensure the quality and reliability of real-world evidence, GVPs require that data collection methods are standardized and that the data is collected in a manner that minimizes bias and confounding factors. GVPs also require that adverse events are reported in a timely and accurate manner, and that data is regularly monitored for safety signals.

In addition, GVPs require that all stakeholders involved in the collection and use of real-world evidence are trained (as appropriate) in pharmacovigilance principles and are aware of their responsibilities in ensuring the safety and efficacy of medicines.

By adhering to GVPs in the context of real-world evidence, researchers and regulatory agencies can ensure that the data collected is of high quality and can be used to inform decision-making related to the safety and efficacy of (approved) medicines.

Share this story...

Real World Evidence (RWE) 101 – EMA Good Pharmacovigilance Practices (GVPs)2023-08-07T22:58:35+00:00

Real World Evidence (RWE) 101 – Is ICH GCP Applicable to Non-Interventional Studies?

RWE 101 – Is ICH GCP Applicable to Non-Interventional Studies?

No, the International Council for Harmonisation (ICH) Good Clinical Practice (GCP) guidelines are not applicable to non-interventional studies (NIS) in the context of real-world evidence (RWE).

ICH GCP guidelines are designed to ensure the protection of human subjects and the quality and integrity of data generated in clinical trials of investigational medicinal products (IMPs). In contrast, NIS are observational studies that do not involve the administration of IMPs, and instead rely on the collection of data from routine clinical practice or other non-experimental settings.

However, there are other guidelines and frameworks that apply to non-interventional studies in the context of real-world evidence, such as the International Society for Pharmacoepidemiology’s (ISPE) “Guidelines for Good Pharmacoepidemiology Practices” (GPP).

It is important to note that while NIS may not be subject to the same regulatory requirements as clinical trials, they still need to adhere to the applicable local regulations, ethical and scientific standards, and to ensure the protection of patient privacy and confidentiality.

Share this story...

Real World Evidence (RWE) 101 – Is ICH GCP Applicable to Non-Interventional Studies?2023-08-07T22:57:28+00:00

Real World Evidence (RWE) 101 – Ethical Principles and Safeguards for Medical AI in the Context of Real World Evidence

RWE 101 – Real World Evidence (RWE) 101 – Ethical Principles and Safeguards for Medical AI in the Context of Real World Evidence

Medical AI applications hold great promise for improving healthcare outcomes, but they also raise ethical concerns related to patient privacy, algorithmic bias, and the reliability of the underlying data. When deploying medical AI in the context of real-world evidence, there are several ethical principles and safeguards that should be considered:

Transparency: Medical AI algorithms should be transparent about how they make decisions, what data they use, and the potential limitations of their predictions. This allows patients and clinicians to better understand the reasoning behind the AI’s recommendations and assess its accuracy.

Data privacy: Medical AI algorithms should comply with data privacy regulations, such as HIPAA in the United States, and should ensure that patient data is protected from unauthorized access, use, or disclosure.

Informed consent: Patients should be informed about how their data will be used by medical AI algorithms and should provide explicit consent for its use. They should also have the right to withdraw their consent at any time.

Fairness and bias: Medical AI algorithms should be designed to minimize bias and ensure that their predictions are fair across different patient populations. This requires careful attention to the selection of training data and the use of appropriate validation methods.

Human oversight: Medical AI algorithms should be designed to augment, not replace, human decision-making. Clinicians should have the ability to review and modify the AI’s recommendations, and patients should have access to human experts to address any concerns or questions they may have.

Accountability: Developers and providers of medical AI applications should be accountable for the accuracy and reliability of their algorithms, and should be transparent about any limitations or uncertainties associated with their predictions.

By following these ethical principles and safeguards, medical AI can be deployed in a responsible and effective manner, enabling healthcare providers to make better-informed decisions and improve patient outcomes.

Share this story...

Real World Evidence (RWE) 101 – Ethical Principles and Safeguards for Medical AI in the Context of Real World Evidence2023-08-07T22:56:13+00:00

Real World Evidence (RWE) 101 – The Declaration of Helsinki

RWE 101 – The Declaration of Helsinki

The Declaration of Helsinki is a set of ethical principles that govern the conduct of medical research involving human subjects. It was first adopted in 1964 and has been revised several times since then, most recently in 2013.

In the context of real-world evidence (RWE), the Declaration of Helsinki is particularly relevant because RWE often involves the collection and analysis of data from sources that were not originally intended for research purposes, such as electronic health records or claims data. This raises important ethical considerations, such as privacy and confidentiality concerns, and the need to obtain informed consent from study participants.

The Declaration of Helsinki provides guidance on these and other ethical issues related to medical research involving human subjects. For example, it states that research involving human subjects must be conducted in accordance with ethical principles, including respect for persons, beneficence, and justice. It also emphasizes the importance of obtaining informed consent from study participants, protecting their privacy and confidentiality, ensuring that the risks and benefits of the research are appropriately balanced, and ensuring research transparency by publishing the results (both positive and negative).

In the context of RWE, these ethical principles can help guide the development and implementation of research studies that use real-world data. For example, researchers can use the Declaration of Helsinki as a framework for designing studies that protect the privacy and confidentiality of study participants, obtain informed consent, and balance the risks and benefits of the research.

Overall, the Declaration of Helsinki provides important ethical guidance for medical research involving human subjects, including research that uses real-world data. Adhering to these principles can help ensure that RWE studies are conducted in an ethical and responsible manner.

Share this story...

Real World Evidence (RWE) 101 – The Declaration of Helsinki2023-08-07T22:55:06+00:00

Real World Evidence (RWE) 101 – Ethical Foundation of RWE Research

RWE 101 – Real World Evidence (RWE) 101 – Ethical Foundation of RWE Research

Real-world evidence (RWE) research, which is the study of data from real-world settings, is founded on a number of ethical principles, including:

Respect for autonomy: This principle recognizes the importance of individuals’ ability to make their own decisions regarding their healthcare. In RWE research, this means obtaining informed consent from individuals before using their data.

Beneficence: This principle requires that researchers seek to maximize the benefits of their research while minimizing any potential harm. In RWE research, this means ensuring that the research is designed to answer important questions that will improve health outcomes for individuals.

Non-maleficence: This principle requires that researchers avoid causing harm to study participants. In RWE research, this means minimizing any risks associated with data collection and use, such as breaches of confidentiality.

Justice: This principle requires that researchers treat individuals fairly and equitably, ensuring that the benefits and burdens of the research are distributed fairly. In RWE research, this means ensuring that the use of data is transparent and that individuals are not unfairly excluded from research opportunities.

In addition to these principles, RWE research is also guided by ethical standards established by international bodies, such as the World Medical Association’s Declaration of Helsinki. These standards emphasize the importance of obtaining informed consent, protecting privacy and confidentiality, and ensuring that research is conducted in a manner that respects the dignity and rights of study participants.

Share this story...

Real World Evidence (RWE) 101 – Ethical Foundation of RWE Research2023-08-07T22:52:57+00:00

Real World Evidence (RWE) 101 – The Impact of GDPR on RWE Research

RWE 101 – Real World Evidence (RWE) 101 – The Impact of GDPR on RWE Research

The General Data Protection Regulation (GDPR) is a regulation in EU law on data protection and privacy for all individuals within the European Union (EU) and the European Economic Area (EEA). Its implementation in May 2018 has had a significant impact on research, particularly in the context of real-world evidence (RWE).

RWE refers to data collected outside of the traditional clinical trial setting, such as electronic health records (EHRs), claims data, and patient-generated data. RWE is increasingly being used to support regulatory decisions and to inform clinical practice. However, the use of RWE must comply with GDPR, which has implications for the collection, processing, and use of personal data in research.

Under GDPR, personal data must be collected and processed lawfully, fairly, and transparently, and individuals have the right to be informed about how their data is being used. This means that researchers must obtain explicit and informed consent from individuals to use their personal data for research purposes. In addition, the data must be pseudonymized or anonymized to protect individuals’ privacy.

GDPR has also increased the administrative burden for researchers, who must ensure that their data management practices are compliant with GDPR. This includes developing and implementing policies and procedures for data protection, privacy, and security, as well as appointing a Data Protection Officer to oversee data management activities.

Overall, GDPR has had a positive impact on research by increasing transparency and protecting the privacy of individuals whose data is used in research. However, compliance with GDPR can be challenging, particularly in the context of RWE, where large volumes of data are collected from multiple sources. It is essential for researchers to work closely with data protection and privacy experts to ensure that their research practices are compliant with GDPR.

Share this story...

Real World Evidence (RWE) 101 – The Impact of GDPR on RWE Research2023-08-07T22:51:18+00:00

Real World Evidence (RWE) 101 – De-Identification versus Pseudo-Anonymisation

RWE 101 – Real World Evidence (RWE) 101 – De-Identification versus Pseudo-Anonymisation

De-identification and pseudo-anonymization are two commonly used techniques for protecting personal information in real world evidence (RWE) studies.

De-identification involves removing or obscuring any personal identifiers, such as names, addresses, and social security numbers, from a dataset. The goal is to make it impossible to identify individuals in the dataset. However, de-identified data can still potentially be re-identified if combined with other data sources or through statistical analysis.

Pseudo-anonymization involves replacing personal identifiers with a unique identifier, or code, that cannot be traced back to the individual without access to a separate database. This technique provides an additional layer of protection as the original personal identifiers are not included in the dataset. However, there is still a risk that individuals can be re-identified if the codes are compromised or if the separate database is breached.

In the context of RWE, both de-identification and pseudo-anonymization can be effective in protecting personal information. The choice of technique will depend on the level of risk associated with re-identification and the specific requirements of the study. For example, if the dataset contains sensitive information or the risk of re-identification is high, pseudo-anonymization may be preferred. If the risk of re-identification is low and the dataset does not contain sensitive information, de-identification may be sufficient.

It is important to note that neither de-identification nor pseudo-anonymization can guarantee complete protection of personal information. Additional measures, such as access controls and data use agreements, may be necessary to further reduce the risk of re-identification and protect the privacy of individuals in RWE studies.

Share this story...

Real World Evidence (RWE) 101 – De-Identification versus Pseudo-Anonymisation2023-08-07T22:50:09+00:00

Real World Evidence (RWE) 101 – Data Tokenization

RWE 101 – 4 Potential Uses for Improving Drug Development

Real world evidence (RWE) refers to data collected outside the context of traditional clinical trials, such as observational studies, registries, and electronic health records. RWE can provide valuable insights into the safety, effectiveness, and real-world use of drugs, and has the potential to transform drug development.

Some of the opportunities of real world evidence in drug development include:

1. Improved patient recruitment: RWE can help identify patient populations that are typically underrepresented in clinical trials, such as elderly patients, patients with multiple comorbidities, and those with rare diseases. This can help improve patient recruitment and enable more representative clinical trials.
2. Enhance clinical trial design: RWE can help inform the design of clinical trials, for example, by identifying appropriate endpoints, understanding patient demographics, and identifying potential confounding factors that need to be accounted for.
Identify safety concerns: RWE can help identify safety concerns that may not have been detected in clinical trials, especially those related to long-term use or rare adverse events. This can help improve post-marketing surveillance and ensure that drugs are used safely in the real world.
3. Better understanding of effectiveness: RWE can provide insights into the effectiveness of drugs in the real world, including how drugs are used in combination with other treatments, and how patient outcomes vary across different subpopulations.
4. Accelerate drug development: By leveraging RWE, drug development timelines can be accelerated as fewer resources are required for clinical trials, making it easier to conduct larger and more complex studies. Additionally, RWE can help optimize the design of clinical trials, reducing the likelihood of failed trials and resulting in faster regulatory approvals.

In summary, real world evidence has the potential to improve drug development in a number of ways, including patient recruitment, clinical trial design, safety monitoring, and accelerating drug development timelines. By leveraging RWE, drug developers can gain a better understanding of how drugs work in the real world, which can ultimately improve patient outcomes.

Share this story...

Real World Evidence (RWE) 101 – Data Tokenization2023-08-07T22:48:59+00:00

Real World Evidence (RWE) 101 – Primary Data versus Secondary Data

RWE 101 – Primary Data versus Secondary Data

Primary data and secondary data are two types of data used in research. The main difference between the two is that primary data is collected directly from the source, while secondary data is collected from sources that have already collected the data.

Primary data is original data that is collected for a specific research project. This type of data can be collected through various methods, including surveys, interviews, observations, and experiments. Primary data is collected with a specific research objective in mind, and the data is usually more focused and targeted than secondary data.

On the other hand, secondary data is data that has already been collected by someone else for a different purpose. This type of data can be collected from a wide variety of sources, including healthcare organisations, government agencies, academic institutions, and commercial organizations. Secondary data can be used to supplement primary data or to answer research questions that are not directly related to the original research objective.

There are advantages and disadvantages to both types of data. Primary data is more likely to be accurate and relevant to the specific research question being studied, but it can also be more time-consuming and expensive to collect. Secondary data is generally less expensive and easier to access, but it may not be as accurate or relevant to the specific research question being studied.

In general, researchers will use a combination of primary and secondary data to address their research questions and achieve their research objectives

Share this story...

Real World Evidence (RWE) 101 – Primary Data versus Secondary Data2023-08-07T22:47:38+00:00
Help

About Us

Contact Us

Our Values

SSO Login

Follow Us

Newsletter

LinkedIn

[…]

Legal Info

Privacy Policy

Terms and Conditions

Copyright 2026 | Real-World Research Limited | All Rights Reserved |

This website uses cookies and third party services. Settings OK Reject

Cookies

We use analytical tools (Google Analytics) to help us improve the user experience. We DO NOT sell or use any of this data for any purpose beyond improving the design and usability of our own website (rwr-regs.com).
Go to Top