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Related Concept Videos

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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Clinical Trials01:16

Clinical Trials

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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...
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Clinical Trials: Overview01:11

Clinical Trials: Overview

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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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Data: Types and Distribution01:19

Data: Types and Distribution

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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
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Drug Distribution: Tissue Binding01:21

Drug Distribution: Tissue Binding

4.0K
Upon entering the systemic circulation, drugs can distribute into the interstitial and intracellular fluid of various tissue cells. This distribution is facilitated by the binding of drugs to different cellular components within tissues, which may lead to drug accumulation in specific areas. Drugs bound to tissue components serve as reservoirs that release free drugs back into the system, prolonging the drug's overall action. However, this accumulation can also result in local toxicity.
For...
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Drug Distribution: Plasma Protein Binding01:29

Drug Distribution: Plasma Protein Binding

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Drugs predominantly attach to plasma proteins, with only a small percentage remaining unbound. The unbound portion can be calculated as one minus the bound fraction. Acidic drugs form large, inactive complexes by reversibly binding to plasma albumin, which prevents them from diffusing across biological barriers. These drug-protein complexes act as reservoirs for the drugs. As the concentration of unbound drugs decreases, these complexes quickly dissociate to release the free drug, maintaining...
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Characterizing Exon Skipping Efficiency in DMD Patient Samples in Clinical Trials of Antisense Oligonucleotides
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A late-binding, distributed, NoSQL warehouse for integrating patient data from clinical trials.

Eric Yang1, Jeremy D Scheff1, Shih C Shen1

  • 1Covance, the Drug Development Division of LabCorp Carnegie Center, Princeton, NJ, USA.

Database : the Journal of Biological Databases and Curation
|March 12, 2019
PubMed
Summary
This summary is machine-generated.

We developed a data warehouse to integrate and standardize diverse clinical trial data. This flexible, scalable solution efficiently supports various analytic tasks for study teams.

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Area of Science:

  • Biomedical Informatics
  • Clinical Data Management
  • Health Data Science

Background:

  • Clinical trial data are fragmented across multiple vendor systems with varying standards.
  • Integrating and standardizing this data is crucial for monitoring and reporting but presents technical challenges.
  • Existing systems struggle with large volumes of inconsistent data and evolving requirements.

Purpose of the Study:

  • To develop a data warehouse solution for a comprehensive clinical data repository.
  • To integrate, standardize, and provide timely access to patient data from any source.
  • To support diverse analytic tasks for clinical studies.

Main Methods:

  • Implemented a data warehouse using Apache HBase (NoSQL column store) and Apache Phoenix (relational query engine).
  • Utilized an extract-load-transform (ELT) pattern, deferring data mapping until query time.
  • Separately versioned data and transformations to enable dynamic data representation and historical analysis.

Main Results:

  • The solution efficiently loads large data volumes under ambiguous specifications.
  • Maintains a single data copy, transforming it dynamically without extra storage.
  • Allows concurrent multiple data representations and simplifies data cut maintenance for adaptive trials.

Conclusions:

  • The developed data warehouse offers a scalable, secure, and redundant solution.
  • Combines NoSQL flexibility with relational query engine robustness.
  • Effectively supports a wide range of applications including data management, medical review, and safety signal detection.