Clinical Trials Data Management and Quality Assurance

所在平台: Coursera

课程主页: https://www.coursera.org/learn/clinical-trials-data-collection-management-quality-assurance

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课程简介

课程名称：临床试验数据管理与质量保证 课程概述：在本课程中，您将学习如何收集和管理试验期间获得的数据，并通过质量保证实践防止错误和失误。临床试验会产生大量数据，因此您和您的团队必须仔细规划，选择合适的数据收集工具、系统和措施，以保护试验数据的完整性。您将学习如何整理、清洗和去标识化数据集。最后，您将学习如何发现并纠正数据缺陷。 课程大纲： 1. **数据收集工具** - 描述：本模块涵盖用于临床试验的数据收集工具的设计与组织。设计良好的数据收集工具对于试验的成功至关重要，因为它决定了数据的定义、收集和组织方式。缺乏良好设计的数据收集工具的研究可能会面临本可避免的问题。 2. **数据管理** - 描述：在本模块中，您将学习临床试验中的数据管理，包括定义和核心概念，并探讨几种常用的数据管理系统。我们将详细讲解Excel及其他电子表格程序，因为它们被广泛使用，并帮助阐明更广泛的数据管理原则。同时，您还将了解数据完整性，包括数据安全、冗余和保存的特点。 3. **数据汇编与分发** - 描述：数据汇编涉及准备数据以便分享。在本模块中，您将学习创建可分享数据集的必要步骤。我们将涵盖数据冻结和数据锁定，以及清洗、去标识化、分享和标准的相关内容，以便使您的数据更有用。 4. **性能监测** - 描述：在本模块中，您将学习如何在临床试验中进行性能监测。具体来说，我们将讨论一个监测临床中心表现和协议遵循的框架，从试验启动到后续的各个阶段。模块的最后部分将简要概述现场访问，这是性能监测工具包的重要组成部分。 5. **干预管理** - 描述：本模块将介绍干预管理的原则。临床试验中存在相当大的异质性，因此多种因素会影响您如何处理干预。这些因素包括假设、试验设计、是否为优化干预，以及采用的是许可药物还是实验药物。您还将学习不同类型的药物制剂以及它们在盲法协议中的作用。 6. **质量保证** - 描述：在本模块中，您将学习质量保证，指的是您和您的团队可以采取的各种措施，以帮助防止临床试验中的错误或问题。这些措施在试验的各个阶段可能会有所不同，因此我们将讨论这些措施应在特定背景下使用的情况。 通过该课程，您将具备在临床试验中有效管理和维护数据质量的能力，为确保试验的成功做好准备。

课程大纲

Name:Data Collection Instruments

Description:This module covers the design and organization of data collection instruments to be used in a clinical trial. A well-designed data collection instrument is critically important to the success of a trial because it determines the way that the data are defined, collected, and organized. A study without a well-designed data collection instrument is likely to encounter otherwise avoidable problems.

Name:Data Management

Description:In this module, you’ll learn about data management in the context of clinical trials. You’ll learn definitions and core concepts and explore a few different frequently used data management systems. We’ll look closely at Excel and other spreadsheet programs because they are widely used and help illustrate broader data management principles. You'll also learn about data integrity, which incorporates features of data security, redundancy, and preservation.

Name:Data Assembly and Distribution

Description:Data assembly involves preparing data for distribution to others. In this module, you’ll learn the necessary steps for creating datasets for sharing. We’ll cover data freezes and data locking as well as cleaning, de-identification, sharing, and standards that you and your team can use to make your data more useful.

Name:Performance Monitoring

Description:In this module, you’ll learn how to conduct performance monitoring in clinical trials. Specifically, we’ll discuss a framework for monitoring clinical center performance and protocol adherence through all phases of the trial from start-up through follow-up. The module will conclude with a brief overview of site visits, an important part of a performance monitoring toolkit.

Name:Intervention Management

Description:In this module, you’ll learn about the principles of managing treatment interventions. There’s a considerable amount of heterogeneity in clinical trials, so a number of factors can influence how you deal with the intervention. Factors include the hypothesis, the design, whether it is an improved intervention, and whether it is licensed or experimental. You’ll also learn about different types of drug formulations and how they factor into masking protocols.

Name:Quality Assurance

Description:In this module, you’ll learn about quality assurance, which refers to the various measures that you and your team can take to help prevent mistakes or problems in your clinical trial. These measures can differ throughout the stages of the trial, so we’ll discuss the specific context in which these measures should be used.

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