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Real World Evidence (RWE) 101 – Standard Operating Procedures

RWE 101 – Standard Operating Procedures

Standard Operating Procedures (SOPs) are critical for the operation of any business, particularly in regulated industries like pharmaceuticals where precision, safety, and compliance with regulatory requirements are paramount. For a pharmaceutical company, SOPs for observational studies and clinical trials should be separated due to the distinct nature and objectives of each type of study. Here are the main reasons:
[1] Different objectives: Clinical trials are experiments designed to evaluate the safety and efficacy of a new drug or intervention. On the other hand, observational studies are non-interventional; they involve the collection of data without altering the patients’ usual treatment or environment, aiming to observe and analyse patterns, effects, or outcomes.
[2] Regulatory requirements: Clinical trials are highly regulated, with strict GxP requirements from bodies like the FDA or EMA. Observational studies, while still regulated, do not fall under the same stringent GxP requirements. Having separate SOPs ensures compliance with the correct set of regulations for each type of study.

[3] Study design and conduct: The design and conduct of clinical trials and observational studies are significantly different. For instance, clinical trials are generally randomized, controlled, and blinded studies. In contrast, observational studies may involve case-control studies, cohort studies, or cross-sectional studies. Different SOPs can specify the appropriate designs, methods and checks for data collection, storage, and analysis in each case.

[4] Patient consent and confidentiality: In clinical trials, patients provide informed consent for the specific interventional treatment they will receive. In observational studies, while consent is still required, it’s often related to access and use of their medical data rather than a specific treatment. Thus, separate SOPs can address these differing requirements.
[5] Risk Management: Risks and safety considerations also vary between clinical trials and observational studies. The risks of clinical trials often relate to the new drug or intervention being tested, while observational studies’ risks typically concern data protection and patient privacy.
[6] Reporting Results: Different types of studies may also have different reporting requirements, including timelines, formats, and recipients of the information (e.g., regulatory authorities, ethics committees, sponsors).
By having separate SOPs, companies ensure that all these aspects are appropriately addressed, leading to better quality, more ethical research, and improved compliance with regulatory requirements. It also helps in training and guiding staff effectively in their roles for different studies.

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Real World Evidence (RWE) 101 – Standard Operating Procedures2023-08-07T19:53:39+00:00

Real World Evidence (RWE) 101 – Principles for Ensuring that Research Results are Reliable, Valid, and Objective

RWE 101 – Principles for Ensuring that Research Results are Reliable, Valid, and Objective

Ensuring that research results are reliable, valid, and objective requires careful planning, execution, and evaluation. Here are several key factors to consider:

1. Research Design: The study design should be robust and suitable for answering the research questions. It should incorporate procedures for controlling variables, randomizing assignments (if applicable), and testing hypotheses. The design should also minimize the potential for bias.

2. Sampling: The selection of participants or samples should be appropriate and unbiased. Larger sample sizes generally increase reliability and can improve validity. The sample should be representative of the population to which you want to generalize your findings.

3. Data Collection: The methods for collecting data should be consistent and standardized. Any measurement tools used should be reliable (provide consistent results over time) and valid (accurately measure what they’re supposed to measure).

4. Data Analysis: Statistical analysis should be appropriate for the type of data collected and the research questions. This includes correctly handling missing data and making proper inferences. Be cautious of multiple testing issues that can lead to false positives.

5. Replication: A study’s results are more reliable if they can be replicated by other researchers. To facilitate replication, provide a clear and thorough description of your methods (e.g., use the STaRT-RWE structured template for planning and reporting of real world evidence studies).

6. Peer Review: Have your research reviewed by others in your field before publishing. They can provide valuable feedback and catch any errors or inconsistencies you may have missed.

7. Transparency and Openness: Be open and honest about your methodology, data, results, and any potential conflicts of interest. This can include sharing your raw data and analysis scripts if possible.

8. Addressing Confounding Factors: Identify and control for potential confounding factors – variables that could cause both the independent and dependent variables to change, thereby creating a false impression of a cause-effect relationship.

9. Interpretation: Be careful not to overstate your findings. Make sure your conclusions are supported by your data, and acknowledge any limitations of your study.

10. Ethical Considerations: Ensure that your study complies with ethical guidelines. This includes respecting participants’ rights and privacy, obtaining informed consent, and avoiding harm.

By adhering to these principles, researchers can increase the chances that their findings will be reliable, valid, and objective. However, it’s also important to recognize that no study is perfect, and all research comes with some degree of uncertainty. The goal is to minimize this uncertainty as much as possible.

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Real World Evidence (RWE) 101 – Principles for Ensuring that Research Results are Reliable, Valid, and Objective2023-08-07T14:20:58+00:00

USA – NESTcc publishes Data Quality Maturity Model Self-Assessment Tools to complement initial Data Quality Framework

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USA – NESTcc publishes Data Quality Maturity Model Self-Assessment Tools to complement initial Data Quality Framework2022-08-07T16:51:09+00:00
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