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Related Concept Videos

Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Observational Studies01:11

Observational Studies

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
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Naturalistic Observations02:30

Naturalistic Observations

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Observational data: Understanding the real MS world.

Tomas Kalincik1, Helmut Butzkueven2

  • 1Department of Medicine, University of Melbourne, Melbourne, VIC, Australia/Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia tomas.kalincik@unimelb.edu.au.

Multiple Sclerosis (Houndmills, Basingstoke, England)
|June 9, 2016
PubMed
Summary
This summary is machine-generated.

Randomised clinical trials are limited in scope. Observational studies offer broader insights into treatment effectiveness for multiple sclerosis but require careful bias mitigation for valid results.

Keywords:
Observational datadisabilityprogressionrelapsestherapy

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Area of Science:

  • Neurology
  • Epidemiology
  • Clinical Research Methodology

Background:

  • Randomised clinical trials (RCTs) are the gold standard for disease-modifying drug evidence in multiple sclerosis (MS).
  • RCTs have limitations in addressing a broad range of clinical questions.
  • 'Real-world' observational data is increasingly vital for understanding epidemiology, aetiology, prognostics, and treatment effectiveness.

Purpose of the Study:

  • To review the inherent biases in observational data research.
  • To outline strategies for mitigating bias in observational studies.
  • To discuss the current state and future directions of treatment outcomes research using real-world data.

Main Methods:

  • This is a review article, synthesizing existing literature on observational data research in multiple sclerosis.
  • The review focuses on identifying biases, mitigation techniques, and contemporary treatment outcomes research.
  • It aims to provide a critical appraisal framework for clinicians.

Main Results:

  • Observational data offers advantages in power, generalizability, and follow-up duration compared to RCTs.
  • However, observational studies are susceptible to various biases.
  • Effective bias mitigation is crucial for ensuring the robustness and validity of findings.

Conclusions:

  • Clinicians must critically appraise observational studies, understanding their potential biases.
  • Mitigation strategies are essential for leveraging the strengths of real-world data in multiple sclerosis research.
  • This review equips clinicians to better interpret and apply findings from observational studies.