Biostatistics

Biostatistics Course Overview Statistics provides the basic framework for thinking about data in a rigorous fashion. Biostatistics applies this thorough examination of data to information gathered in the biological sciences, including clinical trials, public health, environmental health, genetics, and genomics. Biostatisticians help researchers design experiments and studies, collect and analyze data, and draw meaningful conclusions communicated to the public. It seeks to differentiate between correlation and causation to uncover the proper interpretation of data. This course is intended as...

This course is designed to provide a solid foundation in regression analysis and ANOVA, with a special focus on applications in the biomedical field. Whether you’re looking to enhance your research skills or apply statistical techniques in your profession, this course will equip you with the knowledge and tools you need. Course Highlights: · Comprehensive Coverage: Dive deep into simple and multiple linear regression, analysis of variance (ANOVA), and analysis of covariance (ANCOVA). · Real-World Applications: Apply what you learn to real-world biomedical data, enhancing your ability to...

The most common types of analysis in the healthcare or pharmaceutical industries are logistic regression models and survival analysis. To analyze data with dichotomous outcomes, such as having (or not having) a certain disease, one often needs to use a logistic regression model. Survival analysis focuses on time to event data. The event of interest can be death (most commonly encountered) or occurrence of a disease. In this course, students learn to identify situations when it is best to utilize logistic regression and survival analysis and how to run these types of analysis by using SAS...

Biostatistics in Healthcare is a field that applies statistical methods to data gathered in the biological sciences, such as clinical trials, public health, genetics, and genomics. It aims to distinguish between correlation and causation to gain a proper understanding of the data. This course serves as both a refresher and an introductory course in statistical thinking and methods. It covers both descriptive and inferential statistics, and students will complete a real-life project to demonstrate their understanding. By the end of the course, students will: Organize, summarize, and present...

The Clinical Data Interchange Standards Consortium (CDISC) is a non-profit organization that develops and supports data standards to improve medical research and ensure research data is easily interpreted and understood by regulators. The CDSIC established standards support the acquisition, exchange, submission and archive of clinical research data. In this course, students will learn the skills and tools necessary to apply the CDISC standards when processing clinical data used for FDA submissions. Specifically, this course will focus mapping raw datasets to CDISC’s Study Data Tabulation...

Introduction to R Programming Statistical computing is employed within a diverse range of industries. In recent years, an open source project, R, has emerged as the preeminent statistical computing platform. With its unsurpassed library of freely available packages, R is capable of addressing almost every statistical inference problem. In this course, you will learn the most commonly-used (roughly 100) functions and operators from the R Base Package, which serves as the fundamental tools for accessing data from multiple sources, manipulating different types of R objects, performing...

Learn how to analyze data using SPSS Unravel the mysteries within data with SPSS software! SPSS is one of the most popular software packages used for statistical analysis and understanding data. Big data, prediction, and data mining all use SPSS. Academic courses that use SPSS include psychology, education, research methods, nursing, social research, engineering management, and, naturally, statistics. SPSS appears in a wide range of applications in the business world, too, such as, automotive, pharmaceutical, medical device, outsourcing, and project management. Data is generated from...

Learn to Create Regulatory Compliance Deliverables for Pharmaceutical Industry: SDTMs, ADaMs, Tables, Lists and Graphs Participants will gain comprehensive knowledge on common R packages (Tidyverse, DPLYR and Piping) and the R programming language. Attendees will learn how to access, create and process R data frames in data management, reporting and analysis. In addition, this course shows best practices in how to select, filter, derive, append and join data frames using SQL code. Ideal for the pharma industry, examples of both SDTM and ADaM datasets will be created, which are important in...

SAS Programming: DATA Step and PROC Fundamentals The SAS system is a software suite for data analysis and management, widely used in business, government, and academia. Because the pharmaceutical, biotech, and clinical research industries use SAS to analyze clinical trial data, SAS programmers are in especially high demand in these industries. In this course, students will learn the tools necessary to write SAS programs to perform elementary data management, analysis, and reporting. Students will also gain the skills necessary to create and document data sets, manage and reshape data,...