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Related Concept Videos

Clinical Trials01:16

Clinical Trials

10.8K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
10.8K
Clinical Trials: Overview01:11

Clinical Trials: Overview

5.0K
Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Trial and Error and Algorithm01:12

Trial and Error and Algorithm

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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Multiple Allele Traits01:49

Multiple Allele Traits

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The Concept of Multiple Allelism
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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
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Clinical trials in multiple sclerosis: milestones.

Yinan Zhang1, Amber Salter2, Gary Cutter3

  • 1Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Therapeutic Advances in Neurological Disorders
|July 24, 2018
PubMed
Summary

Clinical trials for multiple sclerosis (MS) have evolved significantly, improving diagnosis and monitoring. Future MS therapeutic research requires efficient trial designs to maintain progress in developing new treatments.

Keywords:
clinical trialsdiagnostic criteriamagnetic resonance imagingmultiple sclerosisoutcome measure

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Area of Science:

  • Neurology
  • Clinical Trials Methodology
  • Pharmacology

Background:

  • Multiple sclerosis (MS) therapeutics have advanced based on clinical trial outcomes.
  • Early MS trials relied on subjective patient reports, unlike modern quantitative methods.
  • Disease diagnosis and progression monitoring have seen significant improvements.

Purpose of the Study:

  • To review the evolution of multiple sclerosis clinical trials.
  • To highlight advancements in MS diagnosis and outcome measurement.
  • To address challenges in future MS clinical trial design for sustained therapeutic progress.

Main Methods:

  • Historical review of multiple sclerosis clinical trial methodologies.
  • Analysis of advancements in diagnostic and monitoring techniques.
  • Examination of collaborative efforts and data resources in MS research.

Main Results:

  • MS clinical trials have transitioned from subjective to quantitative outcome measures.
  • Improved diagnostic tools and paraclinical studies enhance disease monitoring.
  • Collaborative initiatives and patient databases have provided crucial epidemiologic data.

Conclusions:

  • Innovations have enhanced MS clinical trials but necessitate more efficient designs.
  • The increasing complexity and duration of trials pose a challenge to the pace of progress.
  • Future trials must adopt new strategies to identify novel MS therapies as treatment options expand.