Clinical Trials - R

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Biostatistics in R: Clinical Trial Applications

taught by Din Chen
and Karl Peace

Aim of Course:

This online course, "Biostatistics in R: Clinical Trial Applications" covers the implementation in R of statistical procedures important for the clinical trial statistician.  Students completing the course will learn how to use R to compare treatments, incorporate covariates into the analysis, analyze survival (time-to-event) trials, model longitudinal data, and analysis of bioequivalence trials.

Course Program:

WEEK 1: Treatment Comparisons

  • R fundamentals associated with clinical trials
  • A simple simulated clinical trial
  • Statistical models for treatment comparisons
  • Incorporating covariates

WEEK 2: Survival Analysis

  • Time-to-event data structure
  • Statistical models for survival data
  • Right-censored data analysis
  • Interval-censored data analysis

WEEK 3: Analysis of Data from Longitudinal Clinical Trials

  • Trial designs and data structure
  • Statistical models and analysis

WEEK 4: Analysis of Bioequivalence Clinical Trials

  • Data from bioequivalence clinical trials
  • Bioequivalence clinical trial endpoints
  • Statistical methods to analyze bioequivalence


HOMEWORK:

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and guided data modeling problems using software.

In addition to assigned readings, this course also has an end of course data modeling project, example software files, and supplemental readings available online.


Biostatistics in R: Clinical Trial Applications

Who Should Take This Course:

Analysts and statisticians at pharmaceutical companies and other health research organizations who need or want to become involved in the design, monitoring or analysis of clinical trials and who are familiar with R software and considering its use in clinical trials.

Level:

Intermediate

Prerequisite:
You should be familiar with introductory statistics.  Try these self tests to check your knowledge.


Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:
About 15 hours per week, at times of  your choosing.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

  1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.


Course Text:

The course text is Clinical Trial Data Analysis Using R by Ding-Geng (Din) Chen and Karl E. Peace.

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Software:

You must have a copy of R for the course. Click Here for information on obtaining a free copy.


Biostatistics in R: Clinical Trial Applications

Instructor(s):
Dates:
To be scheduled.

Course Fee: $589