Wednesday 28 May 2014

Introduction to Statistical Issues in Clinical Trials

Streptomycin has a storied history, and many uses - it is the treatment of choice for plague, and is also used to control algae in ponds.  Its place in statistics history, though, dates to 1946, when a study demonstrated its effectiveness in cases of tuberculosis.  This study - controlled, double blind with a placebo - is generally regarded as the first randomized controlled trial (RCT).  Learn the principles of designing and analyzing RCT's in our online course, "Introduction to Statistical Issues in Clinical Trials," with Dr. Vidyadhar Phadke at Statistics.com. For more details please visit http://www.statistics.com/ClinicalStats/.

This course covers the basic statistical principles in the design and analysis of randomized controlled trials. Participants will learn the basic principles for the design of trials, and sample size determination. They will also learn the appropriate statistical techniques associated with the major types of end points for trials (categorical, count, Normal, non-Normal). Participants will also be introduced to pharmacokinetics and the study of drug concentration data.

Who can 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.

Course Program:

Course outline: The course is structured as follows
SESSION 1: The Domain
  • What is a clinical trial?
  • Historical remarks, some diseases and discoveries
  • Steps(phases)in drug development
  • Scope of clinical trials: new drugs, generics, devices, psychiatric therapy, alternative medicine.
  • Role of statistics

SESSION 2: Planning a Clinical Trial - Statistican's Inputs
  • Principles of design of experiments (replication, local control, randomization)
  • Power and sample size
  • Bias reduction (blinding)
  • Commonly used designs

SESSION 3: Statistical Analysis Plan (SAP) of Clinical Trial
  • Trial objectives, hypotheses, choice of techniques, nature of endpoints (illustrated with live cases)
  • End point Binary: A Randomizated Evaluation of First-Dollar Coverage for Post-MI Secondary Preventive Therapies (Post-MI FREEE)
  • End point - Normal: accuracy study of SoftTouch (a non-invasive device for measurement of peripheral blood biomarkers)
  • End point - count data: Pediatric Asthma Alert Intervention for Minority Children With Asthma (PAAL)
  • End point - non-Normal: TBTC Study 27/28 PK: Moxifloxacin Pharmacokinetics During TB Treatment

SESSION 4: Illustrative Statistical Analysis of Clinical Trial Data
  • One sample problem - reduction in blood pressure
  • Two sample problem - anorexia
  • K-sample problem - drowsiness due to antihistamines
  • Cochrane's Q test - allergic response
  • Analysis of Time-concentration data in pharmacokinetic study

HOMEWORK:
Homework in this course consists of short answer questions to test concepts.
In addition to assigned readings, this course also has supplemental readings available online, and an end of course data modeling project.

Dr. Vidyadhar Phadke is Senior Statistician at Cytel Statistical Software and Services, the leading contract research organization for advanced design of clinical trials.  Dr. Phadke has responsibility for Cytel's industry-leading software for adaptive trials (EaSt) and dose-ranging trials (Compass).

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

We, the Center for eLearning and Training (C-eLT), Pune, partner with Statistics.com and offer these courses to Indian participants at special prices payable in INR.

For India Registration and pricing, please visit us at www.india.statistics.com.

Call: 020 6680 0300 / 322

Websites:

Friday 2 May 2014

Introduction to Statistical Modeling

If you've had some statistics and feel that mathematical notation and formulas are not your friend, consider Dr. Vittorio Addona's online course Introduction to Statistical Modeling at statistics.com.

This course provides a solid introduction to the ideas and techniques of statistical modeling. Once you have completed this course, you will be able to construct and interpret linear statistical models involving multiple variables and co-variates, you will understand the implications of including or excluding explanatory variables, you will be able to conduct and interpret analysis of variance (ANOVA) and of covariance (ANCOVA), and you will have a solid theoretical foundation for understanding linear regression and experimental design. For more details please visit at http://www.statistics.com/ModelingIntro/.

Who Should Take This Course:
Students who are planning to take regression and other modeling courses at statistics.com. Analysts or educators who need to work with multiple variables but are not comfortable with the standard formula- and linear-algebra based approach generally taken.

Course Program:
Course outline: The course is structured as follows

SESSION 1: What is a Statistical Model?
  • Explanatory and response variables
  • Model terms
  • Reading model formulas
  • Fitting models to data
  • Introduction to R software

SESSION 2: The Logic Behind Models
  • Statistical adjustment
  • Introduction to the geometry of model fitting:
    • case space vs variable space
    • variables as vectors
    • simple projection and least squares
    • the model triangle
  • Correlation as a measure of alignment

SESSION 3: Randomness and Models
  • Geometry of multiple explanatory variables
  • Random walks and random directions
  • Confidence intervals
  • Collinearity

SESSION 4: Inference and Models
  • The F statistic
  • Decomposing variance into parts
  • Ambiguities introduced by collinearity
  • The virtues of orthogonality

Homework:
The 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 supplemental readings available online, and example software codes.

The Instructor, Dr. Vittorio Addona, teaches statistics at Macalaster College in Minnesota.  His research falls into three broad categories: survival analysis, sports statistics, and election audits.  His doctoral work was in survival analysis, a statistical field often employed in medical studies of time-to-event data. Vittorio teaches across the statistics curriculum: introductory modeling, probability, mathematical statistics, and an applied survival analysis 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.

The course typically requires 15 hours per week. 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.

We, the Center for eLearning and Training (C-eLT), Pune, partner with Statistics.com and offer these courses to Indian participants at special prices payable in INR.

For India Registration and pricing, please visit us at www.india.statistics.com.

Call: 020 6680 0300 / 322

Websites: