Wednesday 17 October 2012

Cluster Analysis


Identifying distinct criminal personalities, separating customers into different types, discovering the characteristics of different kinds of stars -- all involve the use of statistical clustering methods.  Learn more about the statistical underpinnings of cluster analysis and its implementation in Anthony Babinec's online course "Cluster Analysis" at Statistics.com. For more details please visit at http://www.statistics.com/clustering/.

This course will teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. In marketing applications, clusters of customer records are called market segments (and the process is called market segmentation).  Clustering is also used in computational biology, environmental science, web and internet analytics, and in military and security applications.  Methods discussed include hierarchical clustering (in which smaller clusters are nested inside larger clusters), k-means clustering; two-step clustering; and normal mixture models for continuous variables.

Who Should Take This Course:
·         Marketing analysts who need to cluster customer data as part of a market segmentation strategy;
·         Computational biologists (e.g. for taxonomy);
·         Environmental scientists (e.g. for habitat studies);
·         IT specialists (e.g. in modeling web traffic patterns);
·         Military and national security analysts (e.g. in automated analysis of intercepted communications).

Course Program:

Course outline: The course is structured as follows

SESSION 1: Hierarchical Clustering

  • Hierarchical clustering - dendrograms
  • Divisive vs. agglomerative methods
  • Different linkage methods

SESSION 2: K-means Clustering


SESSION 3: Normal Mixture Model

  • Finite mixture model
  • K-means cluster as a special case

SESSION 4: Other Approaches


Before forming AB Analytics, Anthony Babinec was Director of Advanced Products Marketing at SPSS; he worked on the marketing of Clementine and introduced CHAID, neural nets and other advanced technologies to SPSS users. He has presented at the AMA's Applied Research Methods Conference and Advanced Research Techniques Forum, the Sawtooth Software Conference, Statistical Innovation's Statistical Modeling Week, and numerous professional meetings. He is on the Board of Directors of the Chicago Chapter of the American Statistical Association, where he has held various offices including President. He is on the Editorial Board of the "Journal of Targeting, Measurement and Analysis for Marketing."  Participants can ask questions and exchange comments directly with Dr. Babinec via a private discussion board throughout the period.

You will be able to ask questions and exchange comments with Mr. Anthony Babinec 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.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

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

Call: 020 66009116

Websites:

Monday 15 October 2012

Avoiding Selection Bias in Randomized Clinical Trials


Do you like to learn the essential concepts required to design rigorous randomized trials so as to ensure valid treatment comparisons, primarily by avoiding selection bias and other biases. The nature and objectives of randomization are discussed, as are those of masking, allocation concealment, blocking, stratification, dynamic randomization, and various types of bias that can arise. In addition, we cover analysis techniques that can be used to salvage reliable treatment comparisons even if some of these biases are detected. These methods are more advanced, and involve adaptations of the propensity score. We round out the course with consideration of crossover designs and self-controlled studies. Learn with Dr. Vance W. Berger in his online course "Avoiding Selection Bias in Randomized Clinical Trials" at Statistics.com. For more details please visit at http://www.statistics.com/ClinBias/.

Who Should Take This Course:
Anyone who designs, conducts, analyzes, or reviews randomized clinical trials. This includes research staff in pharmaceutical and biotechnology firms and CROs, regulators, journal editors, and students of biostatistics and epidemiology.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Clinical Trial Designs - A Hierarchy
  • The need for comparison groups.
  • Historical vs. parallel comparison groups.
  • Self-selection vs. physician treatment decision vs. randomization.
  • Masking and allocation concealment.

SESSION 2: Detecting Violations of the Randomization Procedure
  • Baseline comparisons by treatment groups.
  • Baseline comparisons by P groups.
  • The Berger-Exner test and graph.

SESSION 3: Preventing Subversion of the Trial by Violations of Randomization
  • Permuted blocks with fixed or varied block size.
  • The maximal procedure.
  • Unrestricted randomization and chronological bias.

SESSION 4: Salvaging Trials Affected by Violations of Randomization
  • Excluding data from contaminated centers.
  • Excluding data from patients with predictable allocations.
  • Using the RPS as a covariate.

SESSION 5: Crossover Designs
  • Washout period.

The instructor, Dr. Vance W. Berger, is an Adjunct Professor at University of Maryland Baltimore County. He has written or co-written dozens of papers in peer reviewed journals in medicine and statistics and regularly reviews manuscripts for many of the top scholarly journals. He has also taught in the past at Rutgers University and the Johns Hopkins University School of Public Health, lectured all over the country on his research, and served as an FDA reviewer for over four years.

You will be able to ask questions and exchange comments with Dr. Vance W. Berger via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 5 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.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

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

Call: 020 66009116

Websites:

Thursday 11 October 2012

Matrix Algebra Review


Statistics deals with collections of data organized in 1, 2, 3 or more dimensions. Compactly representing such data is best accomplished by the use of matrix notation, particularly when solutions to optimization (e.g., regression) or estimating (i.e, models) are involved. This course will provide the basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods, including the notions of the matrix inverse, generalized inverse and eigenvalues and eigenvectors. After successfully completing this course, you will be able to use and understand vector and matrix operations and equations, find and use a matrix inverse, and use and understand the eigenset of a symmetric matrix. Learn with Dr. Robert LaBudde in his online course "Matrix Algebra Review" at Statistics.com. For more details please visit at http://www.statistics.com/matrixalgebra/.

Who Should Take This Course:
Matrix algebra is used heavily in multivariate statistics, and the theory behind many statistical modeling procedures. Matrix notation is used even more widely. If you are interested in taking courses in multivariate statistics, modeling, design of experiments, data mining or other topics involving multivariate data and need a refresher in, or introduction to matrix methods, you should take this course.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Introduction to Vectors and Matrices
  • Notation
  • Definitions of scalars, vectors, matrices and arrays
  • Vector and matrix operations and the transpose
  • Inner and outer products
  • Zero and Identity matricesMatrix multiplication
  • Order and rank of a matrix
  • Length, norm and distance
  • Angle between two vectors, orthogonally

SESSION 2: Matrix Inverse & Linear Equations
  • Order and rank of a matrix
  • Elementary row and column operations
  • Row and column echelon forms
  • Inverse of a square matrix
  • Applications to statistics
  • Linear combinations, dependence and independence
  • More on the rank of a matrix
  • The generalized inverse
  • Homogeneous equations
  • Solving a system of linear equations and the generalized inverse
  • Determinant of a square matrix
  • Applications of determinants in statistics

SESSION 3: Eigenvalues and Eigenvectors
  • The characteristic equation and eigenvalues and eigenvectors of a real, square matrix
  • Finding eigenvalues and eigenvectors of a matrix
  • Geometric interpretation

SESSION 4: Symmetric Matrices
  • Symmetric matrices
  • Positive definite, semi-definite and non-negative definite matrices
  • Eigenvalues and eigenvectors of a real symmetric matrix
  • The spectral decomposition of a symmetric matrix
  • Principal components analysis
  • Quadratic forms
  • Applications to statistics

The instructor, Dr. Robert LaBudde, is president and founder of Least Cost Formulations, Ltd., a mathematical software development company specializing in optimization and process control software for manufacturing companies. He has served on the faculties of the University of Wisconsin, Massachusetts Institute of Technology, Old Dominion University and North Carolina State University. Dr. LaBudde is currently Adjunct Professor of Statistics at Old Dominion University.

You will be able to ask questions and exchange comments with Dr. Robert LaBudde 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.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

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

Call: 020 66009116

Websites:

Tuesday 9 October 2012

Safety Monitoring Committees in Clinical Trials


In 1989, a drug was found to be effective in controlling heart arrhythmias, but it paradoxically increased the rate of arrhythmic death.  Who watches over clinical trials as they happen, looking out for unusual outcomes, protecting the patients, and assuring integrity of the study?  This is the job of the Data and Safety Monitoring Committee, a specialty of Dr. Jay Herson (a pioneer in this area), who will teach his online course "Safety Monitoring Committees in Clinical Trials" at Statistics.com. For more details please visit at http://www.statistics.com/ClinicalMonitoring/.

"Safety Monitoring Committees in Clinical Trials" describes how the safety of clinical trials in the pharmaceutical industry is assured through Data and Safety Monitoring Committees (DMC's).  It covers the statistical display and analysis methods that are appropriate for the monitoring procedures, as well as the biases and pitfalls inherent in safety review. Emerging issues in the pharmaceutical industry and their effect on the activities of a safety monitoring committee are discussed. The course is illustrated throughout with real-life examples.

Who Should Take This Course:
Anyone involved with the design, implementation or analysis of clinical trials.

Course Program:

Course outline: The course is structured as follows
SESSION 1: Introduction and Organization of a Safety Monitoring Program
  • Objectives of a Data Monitoring Committee (DMC)
    • Differences in DMC's between the NIH-sponsored trials and industry-sponsored trials
    • Issues related to the size of the sponsoring company (Big Pharma, Middle Pharma, Infant Pharma)
  • Creation of a DMC
  • Selecting members
  • Conflicts of interest
  • Role of the Data Analysis Center (DAC)

SESSION 2: Meetings and Clinical Issues
  • The DMC Charter
  • Types and structure of meetings
  • Open and closed sessions
  • Adverse event definitions and coding schemes
  • Format for meeting agendas
  • Impact of multinational trials

SESSION 3: Statistical Issues, Biases, and Pitfalls
  • Goals of statistical analysis for DMC's
  • Useful data displays
  • Frequentist, likelihood, and Bayesian analysis methods
    • Incidence
    • Rate/patient year
    • Time-to-event
  • Power
  • Multiplicity
  • Sources of bias by sponsor
  • Investigator
  • Granularity bias
  • Competing risks

SESSION 4:  DMC Decisions and Emerging Issues
  • Types of DMC decisions and the environment in which they are made
  • Risk vs. benefit analysis
  • Steps taken when a safety issue arises
  • Meta-analysis
  • Problems particular to Infant Pharma companies
  • DMC operations for safety when adaptive designs are employed for efficacy
  • Real-time SAE reporting via the internet
  • Causal inference
  • Biomarkers
  • Training of DMC members
  • Cost control, DMC audits
  • Working with internal safety review committees
  • Effect of company mergers and licensing agreements on independent safety review

Dr. Jay Herson is currently serving on the adjunct faculty in Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His previous positions included Senior Biostatistician at the University of Texas MD Anderson Cancer Center and as president of the contract research organization he founded known as Applied Logic Associates. Under his leadership ALA grew from a solo consultancy to a 50-person full-service CRO with global capabilities. Dr. Herson started the first Data Monitoring Committee (DMC) in the pharmaceutical industry and has Chaired or served on 25 DMCs.

You will be able to ask questions and exchange comments with Dr. Sudha Purohit 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.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

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

Call: 020 66009116

Websites:

Friday 5 October 2012

Sample Size and Power-Analysis for Cluster-Randomized and Multi-Site Studies (power-analysis for cluster-randomized and multi-site studies)

Cluster-randomized (or multi-site) studies are studies with a multi-level structure, such as students within classes within schools, or patients within doctors within hospitals.

Unlike simple-randomized trials, where you need to consider only the number of subjects, in cluster-randomized and multi-site trials you need to consider the number of units at each level of the study design. If you ignore these, you might compute power as 90% when the actual value is 50% or even lower.

Cost-effective design.

Your real challenge, however, is not only to compute the correct power, but to design the most cost-effective study.

In simple randomized trials, the only sample size you can manipulate is the number of subjects. By contrast, in a cluster-randomized or multi-site trial you can manipulate (for example) the number of school districts, schools, classes, and students. Or, the number of hospitals, doctors and patients.

Typically, there are many combinations of these numbers that will yield the same power but (in common scenarios) some may cost twice as much as others.  In this course you will learn how to quickly and confidently find the best options, taking into account both cost and practical considerations.

For more details about this course, Sample Size and Power-Analysis for Cluster-Randomized and Multi-Site Studies, please visit at http://www.statistics.com/CT-Cluster/.

In this course Dr. Borenstein will explain how cluster-randomization and/or the use of multiple sites affect power. Then, he will show how to take account of these issues when planning a study.

The course will cover the following topics
·         Basic issues and goals in a power analysis
·         Cluster-randomized (hierarchical) designs
·         Multi-site (matched or randomized block) designs
·         Two-level designs
·         Three-level designs
·         Four-level designs
·         Working with continuous data
·         Working with binary data
·         Working with covariates
·         Finding the most cost-effective design
·         Using the software to create reports, tables and graphs

How the course works
·         Participants can ask questions and exchange comments with Dr. Borenstein via a private discussion board throughout the period.
·         As with all courses at Statistics.com, participate at your own convenience; there are no set hours when you must be online.  We  estimate you will need about 15 hours per week.
·         This course will include lessons posted online, video lectures, PowerPoint presentations, and video demonstrations of the software.

About the software

Dr. Borenstein is the co-author (with Larry Hedges) of CRT-Power, a program developed with funding from the National Institutes of Health (see below for details).

·         Participants will have free access to this program for the duration of the course.

More info about the online course:
http://www.statistics.com/CT-Cluster/

More info about the software:
http://crt-power.com/

About the instructor:
Michael Borenstein is the co-author of Power And Precision, the best-selling program for power analysis, and of Comprehensive Meta-Analysis, the best-selling program for meta-analysis.  He is also the co-author (with Larry Hedges) of the newly released program CRT-Power, the first program ever developed to compute power for a full array of three-level and four-level study designs.

Dr. Borenstein has taught numerous workshops and online courses on power analysis and meta-analysis, and is widely recognized for his ability to communicate difficult concepts in ways that are accessible to researchers as well as statisticians.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

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

Call: 020 66009116

Websites: