Introduction to Clinical Data Science

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

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About Course

This course will prepare you to complete all parts of the Clinical Data Science Specialization. In this course you will learn how clinical data are generated, the format of these data, and the ethical and legal restrictions on these data. You will also learn enough SQL and R programming skills to be able to complete the entire Specialization - even if you are a beginner programmer. While you are taking this course you will have access to an actual clinical data set and a free, online computational environment for data science hosted by our Industry Partner Google Cloud. At the end of this course you will be prepared to embark on your clinical data science education journey, learning how to take data created by the healthcare system and improve the health of tomorrow's patients.

Course Syllabus

Welcome to the Clinical Data Science Specialization
Introduction: Clinical Data
Tools: SQL
Tools: R and the Tidyverse

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Course Introduction

Introduction to Clinical Data Science There are many different types of clinical data scientists, and it is important for you to be able to identify the data that is relevant to your clinical research. This course will take you from data analyst to data scientist, and we will use an easy-to-follow format to cover topics such as data quality, data analysis, and exploratory data analysis. We will cover how data scientists identify and analyze data, how they should be prepared for the job, and how they can assist in clinical data analysis. There are two parts to this course, and each will be useful to clinical data scientists. The first part is a basic course overview of the field, including topics such as data analysis, and the second part is a practical course description of the data science work that data scientists do. In order to get the most out of this course, you will want to first complete the other parts of the Data Science for Business specialization ( After completing this course, you will be able to: - Exploratory Data Analysis - Quality Data Analysis - Quality Control - Coordinate with other data scientists - Quality Coordination - Interpret and/or present data You can also follow us on Twitter! #EdiMBiCOIntroduction to Clinical Data Science Data Quality Data Analysis Quality

Course Tag

Related Wiki Topic

Article Example
Clinical data management system A clinical data management system or CDMS is a tool used in clinical research to manage the data of a clinical trial. The clinical trial data gathered at the investigator site in the case report form are stored in the CDMS. To reduce the possibility of errors due to human entry, the systems employ various means to verify the data. Systems for clinical data management can be self-contained or part of the functionality of a CTMS. A CTMS with clinical data management functionality can help with the validation of clinical data as well as helps the site employ for other important activities like building patient registries and assist in patient recruitment efforts.
Clinical data repository A Clinical Data Repository (CDR) or Clinical Data Warehouse (CDW) is a real time database that consolidates data from a variety of clinical sources to present a unified view of a single patient. It is optimized to allow clinicians to retrieve data for a single patient rather than to identify a population of patients with common characteristics or to facilitate the management of a specific clinical department. Typical data types which are often found within a CDR include: clinical laboratory test results, patient demographics, pharmacy information, radiology reports and images, pathology reports, hospital admission, discharge and transfer dates, ICD-9 codes, discharge summaries, and progress notes.
Clinical Data, Inc Once the clinical data has been harmonized into a unified subject centric data model, it can be transformed to CDISC compliant data sets. This means that the clinical operations teams at the Bio-Pharma companies can work with standardized data all through the life of the clinical trial than at the very end. Hence a reduced change of FDA rejection and faulty analysis.
Clinical data management system At the end of the clinical trial the data set in the CDMS is extracted and provided to statisticians for further analysis. The analysed data are compiled into clinical study report and sent to the regulatory authorities for approval.
Clinical Data, Inc EDI and related data exchange standards such as X12, EDIFACT, ODETTE have enabled organizations around the world to conduct eCommerce for decades. The Bio-Pharma and Medical Devices industry and the regulatory agencies have agreed to finally adopt global standards to exchange clinical data. Beginning October 2016, FDA mandates that all pre-clinical (Animal studies) and clinical data submissions must conform to CDISC data standards.
Data science In 2013, the IEEE Task Force on Data Science and Advanced Analytics was launched, and the first international conference: IEEE International Conference on Data Science and Advanced Analytics was launched in 2014. In 2014, the American Statistical Association section on Statistical Learning and Data Mining renamed its journal to "Statistical Analysis and Data Mining: The ASA Data Science Journal" and in 2016 changed its section name to "Statistical Learning and Data Science". In 2015, the International Journal on Data Science and Analytics was launched by Springer to publish original work on data science and big data analytics. 2013 the first "European Conference on Data Analysis (ECDA)" was organised in Luxembourg establishing the European Association for Data Science (EuADS) in August 2015. In September 2015 the Gesellschaft für Klassifikation (GfKl) added to the name of the Society "Data Science Society" at the third ECDA conference at the University of Essex, Colchester, UK.
Clinical Data, Inc Large amount of subject centric clinical data is generated throughout the life of a clinical trial / study. However there is no binding mandatory requirement to submit this clinical data in a standardized format, leading to divergent data submission formats to regulatory agencies such as FDA / EMA / PMDA. This lack of uniformity of submitted data, results in longer review time of NDAs and hence more operational costs and longer duration of the trials.
Clinical data management Clinical data management (CDM) is a critical phase in clinical research, which leads to generation of high-quality, reliable, and statistically sound data from clinical trials. Clinical data management assures collection, integration and availability of data at appropriate quality and cost. It also supports the conduct, management and analysis of studies across the spectrum of clinical research as defined by the National Institutes of Health (NIH). The ultimate goal of CDM is to assure that data support conclusions drawn from research. Achieving this goal protects public health and confidence in marketed therapeutics.
Clinical data management The clinical data manager plays a key role in the setup and conduct of a clinical trial. The data collected during a clinical trial forms the basis of subsequent safety and efficacy analysis which in turn drive decision making on product development in the pharmaceutical industry. The clinical data manager is involved in early discussions about data collection options and then oversees development of data collection tools based on the clinical trial protocol. Once subject enrollment begins, the data manager ensures that data is collected, validated, complete, and consistent. The clinical data manager liaises with other data providers (e.g. a central laboratory processing blood samples collected) and ensures that such data is transmitted securely and is consistent with other data collected in the clinical trial. At the completion of the clinical trial, the clinical data manager ensures that all data expected to be captured has been accounted for and that all data management activities are complete. At this stage, the data is declared final (terminology varies, but common descriptions are "Database Lock" and "Database Freeze"), and the clinical data manager transfers data for statistical analysis.
Clinical Data, Inc It is possible to mine clinical data to recognize risk patterns that could cause minor or serious adverse events (SEA) in subjects. However it is critical that mined data represent all clinical data across all sites. Housing standardized clinical data in a CDW is a pre-requisite for accurate predictive analytics. This may greatly help clinicians identify subjects at risk of developing SAEs and mitigate that risk, thus improving safety among participating subjects.
Clinical data management Analysis of clinical trial data may be carried out by laboratories, image processing specialists or other third parties. The clinical data manager liaises with such data providers and agree data formats and transfer schedules. Data may be reconciled against the CRF to ensure consistency.
Data science Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD).
Clinical Data Interchange Standards Consortium The CDISC Operational Data Model (ODM) is designed to facilitate the regulatory-compliant acquisition, archive and interchange of metadata and data for clinical research studies. ODM is a vendor neutral, platform independent format for interchange and archive of clinical study data. The model includes the clinical data along with its associated metadata, administrative data, reference data and audit information.
Data science Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data.
Clinical data management Samples collected during a clinical trial may be sent to a single central laboratory for analysis. The clinical data manager liaises with the central laboratory and agrees data formats and transfer schedules in Data Transfer Agreement. The sample collection date and time may be reconciled against the CRF to ensure that all samples collected have been analysed.
Clinical Data, Inc Many Bio-Pharma companies are realizing that clinical data across multiple sites in a study, cannot exist in data silos and need a mechanism to harmonize and consolidate it to provide a unified data model and view. One way they are trying to address this is by requiring their CROs to build Clinical Data Warehouse for them. Other more IT savvy companies are building their own in-house.
Clinical data management The case report form (CRF) is the data collection tool for the clinical trial and can be paper or electronic. Paper CRFs will be printed, often using No Carbon Required paper, and shipped to the investigative sites conducting the clinical trial for completion after which they are couriered back to Data Management. Electronic CRFs enable data to be typed directly into fields using a computer and transmitted electronically to Data Management.
Clinical data management The data management plan describes the activities to be conducted in the course of processing data. Key topics to cover include the SOPs to be followed, clinical data management system to be used, description of data sources, data handling processes, data transfer formats and process, and quality control procedures to be applied.
Data science he initiated the modern, non-computer science, usage of the term "data science" and advocated that statistics be renamed data science and statisticians data scientists.
Oracle Clinical Oracle Clinical or OC is a database management system designed by Oracle to provide data management, data entry and data validation functionalities to support Clinical Trial operations.