What is Design of Experiments?
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Design of Experiments, or DOE as it is commonly known in the industry, is defined as a “branch of applied statistics that deals with planning, conducting, analyzing, and interpreting controlled tests to evaluate the factors that control the value of a parameter or group of parameters” by ASQ. At a basic level, DOE is one of the most powerful data collection and analysis tools available, regarded as one of the best ways to predict process variability. Although DOE can be applied to experimental situations in any industry, it is especially useful in the medical device and pharmaceutical spaces...
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Tsqr16 17-en
The document is the 2016-17 Turkey Software Quality Report published by the Turkish Testing Board. It provides an executive summary and analysis of survey results on topics related to test data management in the Turkish software industry. Some of the key findings from the surveys include:
- Over 70% of organizations report that software testers are responsible for test data generation.
- Over half of organizations spend between 11-50% of their testing efforts on test data management activities.
- The top challenges in creating test data are having up-to-date and consistent data that resembles real production data.
- Most organizations manually generate test data or use subsets of production data for testing. Very few use full database virtualization.
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A Roadmap for SAS Programmers to Clinical Statistical Programming
The purpose of this presentation is to describe step by step the transition of a SAS Programmer into a Clinical Statistical Programmer. It can be used as guidelines for SAS Programmers who wants to put their programming and technical expertise into industries.
A SAS Programmer is someone who uses SAS software for different scenarios. The person who uses it for different purposes is known as a SAS Programmer.
On the other hand, a Clinical Statistical Programmer performs all the procedures to generate future outputs and makes advanced and real-world developments to face further challenges. A primary role of Clinical Statistical Programmers is to use their technical and programming skills in order to enable clinical trial statisticians to perform their statistical analysis duties more efficiently.
This presentation will briefly discuss about the smooth transition that a SAS Programmer needs to go through in order to become a Clinical Statistical Programmer.
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Clinical Data Management
The document provides information on several clinical data management systems and software, including Oracle Clinical, SAS Clinical Software, TCS Clin-E2E Software, Cognos 8 Business Intelligence Software, Symetric Software, Akaza's OpenClinica Software, SigmaSoft's DMSys Software, and Progeny Clinical Software. It discusses their key features for managing clinical trials data such as electronic data capture, reporting, security, compliance with industry standards, and integration with other systems.
Quality management system in pharmaceutical industry
The document discusses quality management systems in the pharmaceutical industry. It provides an overview of the key components of an effective quality management system, including quality management, quality assurance, evaluation analysis, quality risk management tools, preventive action, and risk management. It describes how a quality management system should function as the central hub connecting six subsystems: quality system, production system, facilities and equipment system, laboratory controls system, materials system, and packaging and labeling system. The document also lists and provides brief descriptions of several common quality management tools used in pharmaceutical quality systems, such as check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms.
Pharmaceutical quality management
This document provides an overview of pharmaceutical quality management. It discusses risk management in the pharmaceutical industry and the importance of a robust quality system. An effective quality system should focus on quality management, quality assurance, evaluation analysis, risk management, preventive action, and continuous improvement. It also describes the key subsystems of a modern pharmaceutical quality system: quality system, production system, facilities/equipment system, laboratory controls system, materials system, and packaging/labeling system. The document provides examples of quality management tools including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and others.
Holistic quality management
This document discusses holistic quality management. It provides an overview of the Holistic Quality Management (HQM) Suite, which is a quality management system that takes a holistic approach. The HQM Suite includes several modules that integrate quality data across systems for specification management, data integration, online specifications, statistical process control, material tracking, business analytics, and data security. It also discusses common quality management tools like check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and provides links to additional quality management resources.
Test performance indicators
One of the objectives of Idexcel’s Test Policy is to
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Saama Technologies Award Write Up
Saama Technologies provides a clinical data integration and analytics platform that leverages machine learning to help life sciences companies optimize clinical trials and other processes. The platform can handle large amounts of structured and unstructured data. It has applications across the pharmaceutical value chain including supporting clinical trials through patient recruitment, site selection, and feasibility assessment using real-world data. Saama's focus on the life sciences industry helps clients apply real-world evidence to functions like drug discovery and regulatory support.
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