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Related Concept Videos

Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Data Collection III01:05

Data Collection III

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Levels of Use of a GIS01:29

Levels of Use of a GIS

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Run Charts01:12

Run Charts

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Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Computerized Adaptive Testing System of Functional Assessment of Stroke
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The Structured Process to Identify Fit-For-Purpose Data: A Data Feasibility Assessment Framework.

Nicolle M Gatto1,2,3, Ulka B Campbell2,4, Emily Rubinstein1

  • 1Aetion, Inc., New York, New York, USA.

Clinical Pharmacology and Therapeutics
|October 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the Structured Process to Identify Fit-For-Purpose Data (SPIFD) framework. SPIFD guides feasibility assessments for real-world data (RWD) to ensure its suitability for regulatory decision-making.

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Area of Science:

  • Pharmacoepidemiology
  • Health Data Science
  • Regulatory Science

Background:

  • Existing real-world evidence (RWE) guidelines require robust study design frameworks.
  • The 2019 Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real-world Evidence (SPACE) framework addressed study design.
  • A gap existed in structured guidance for assessing data feasibility for RWE studies.

Purpose of the Study:

  • To present the Structured Process to Identify Fit-For-Purpose Data (SPIFD) framework.
  • To provide a systematic, step-by-step guide for conducting feasibility assessments of data sources.
  • To complement existing RWE guidelines and the FDA's framework for RWE programs.

Main Methods:

  • The SPIFD framework was developed based on collective experience in systematic feasibility assessments.
  • It outlines a process for identifying decision-grade, fit-for-purpose data.
  • The framework integrates with the SPACE framework for comprehensive study development.

Main Results:

  • SPIFD offers a structured approach to feasibility assessments for real-world data (RWD).
  • It ensures that identified data sources are suitable for regulatory decision-making.
  • The process enhances justification and transparency from research question to study design.

Conclusions:

  • The SPIFD framework provides a systematic method for evaluating RWD feasibility.
  • It supports the generation of valid and transparent RWE for regulatory submissions.
  • SPIFD enhances the reliability and utility of RWD in pharmacoepidemiology and regulatory science.