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Related Concept Videos

Standard Deviation01:10

Standard Deviation

27.8K
The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more variation.
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Mean Absolute Deviation01:13

Mean Absolute Deviation

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The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
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Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

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Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...
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Calculating Standard Deviation01:08

Calculating Standard Deviation

13.1K
The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high...
13.1K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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A Toolkit for the Management of Protocol Deviations.

Sandy Mohan1, Munish Mehra2, Maryrose Petrizzo3

  • 11 Biotie Therapies Inc, South San Francisco, CA, USA.

Therapeutic Innovation & Regulatory Science
|September 21, 2018
PubMed
Summary
This summary is machine-generated.

This article presents templates for a protocol deviation standard operating procedure (SOP) and handling plan (PDHP) to improve clinical trial quality. These tools aid in managing deviations to ensure subject safety and data integrity.

Keywords:
CAPAGCPSOPcorrective actiongood clinical practicepreventive actionprotocol deviationsstandard operating procedure

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Area of Science:

  • Clinical trial management
  • Regulatory compliance in pharmaceuticals
  • Quality assurance in healthcare

Background:

  • The Drug Information Association's (DIA) Good Clinical Practice and Quality Assurance Community (GCP/QA) formed a working group.
  • The group's objective was to create templates for a protocol deviation standard operating procedure (SOP) and a protocol deviation handling plan (PDHP).

Purpose of the Study:

  • To provide practical tools for managing protocol deviations in clinical trials.
  • To establish standardized procedures for handling deviations to ensure data integrity and patient safety.

Main Methods:

  • A working group comprising QA auditors, data managers, statisticians, and clinical monitors was assembled.
  • Existing SOPs, data handling plans, and auditing plans were reviewed to extract core elements for the templates.
  • Draft templates were refined based on feedback from a workshop at the DIA 51st Annual Meeting.

Main Results:

  • The article presents the developed templates for a protocol deviation SOP and PDHP.
  • These templates serve as a foundation, requiring company-specific modifications for implementation.

Conclusions:

  • The article offers concrete tools for protocol deviation management, building upon a previous position paper.
  • It emphasizes best practices for detecting, classifying, mitigating, and managing deviations.
  • The ultimate goal is to minimize the impact of protocol deviations on subject safety and data integrity.