Wednesday 26 June 2013

Clinical Trials - Pharmacokinetics and Bioequivalence

How quickly does the human body absorb a drug?  What are the effects of different doses?  Pharmaceutical companies study these issues in small, early-phase studies that, before they undertake a large randomized study of a drug's safety and efficacy.  Learn more in "Clinical Trials - Pharmacokinetics & Bioequivalence," which will be taught online by Dr. Vidyadhar Phadke at statistics.com. For more details please visit at http://www.statistics.com/bioequivalence.

This course covers the statistical measurement and analysis methods relevant to the study of pharmacokinetics (the absorption, distribution and secretion of drugs), dose-response modeling and bioequivalence. In this course, you will apply the principles of designing and analyzing clinical trials to the circumstances of several actual trials. This course is primarily case oriented and will give you the "hands-on" practice required in this demanding field.

After taking this course, participants will be able to specify the design of a new drug or new device study, with the goal of establishing whether the new drug or device is statistically equivalent to an existing therapy. This includes designing the study in accordance with regulatory requirements, as well as appropriate methods for analyzing data. Participants will also be able to fit statistical models to dose-response data, with the goal of quantifying a reliable relationship between drug dosage and average patient response.

Who Should Take This Course:
Analysts responsible for designing, implementing or analyzing clinical trials.

Course Program:

Course outline: The course is structured as follows

 

PK DOSE –

SESSION 1: Clinical Trials for Drugs and Devices
  • Clinical trials review
  • Four trials of drugs and devices, behavioral therapy and chiropractic therapy (two examined in the lesson, two for homework)
o    end point
o    question of interest
o    choice of statistical technique
o    interpretation
  • Illustrative analysis of two cases

SESSION 2: Pharmacokinetics (PK) and Bioavailability
  • Basic concepts of PK
  • PK analysis of time-concentration data (bioavailability assessment)
o    Oral administration
o    Estimation of Cmax, Tmax, AUC, Ke, Ka
o    Intravenous administration
  • Dose-response modeling
o    Types of 
      • Michaelis-Menton model for saturating relationship
      • Power model: A model that includes three shapes

 

BIOEQUIVALENCE –

SESSION 3: Inference for Pharmocokinetic (PK) data

  • Normality testing of PK parameters (AUC, Cmax)
  • Transformations for achieving normality (AUC, Cmax)
  • Parametric (AUC, Cmax) and Non-parametric tests (Tmax)
  • Bootstrap confidence interval for t1/2

 

Analysis of Dose-Response Data
  • Estimation of median effective dose
  • Testing of dose proportionality in power model

SESSION 4: Bioequivalence Studies-Parallel Design
  • Statistical equality vs. clinical equivalence
  • Testing bioequivalence (AUC)
  • CI approach (AUC)
  • Testing bioequivalence (Cmax)
  • CI approach (Cmax)

 

Bioequivalence Studies 2 x 2 (Crossover Design)
  • What is crossover design?
  • Analysis of illustrative data using two sample tests
o    Test for carry over effect
o    Test for period effect
    • Test for treatment difference
  • Testing equivalence using CI
o    Parallel vs. crossover design

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.

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:

Introduction to R – Statistical Analysis

Now learn R via your existing knowledge of basic statistics and does not treat statistical concepts in depth. After completing this course, students will be able to use R to summarize and graph data, calculate confidence intervals, test hypotheses, assess goodness-of-fit, and perform linear regression. This is covered in Dr. John Verzani online course “Introduction to R – Statistical Analysis” at statistics.com. For more detail please visit at http://www.statistics.com/Rstatistics.

Who Should Take This Course:
Anyone who wants to gain a familiarity with R to facilitate its use in more advanced courses. Also, teachers who wish to use R in teaching introductory statistics.

Course Program:

Course outline: The course is structured as follows

SESSION 1: The One-Sample T-Test in R
  • A manual computation
    • A data vector
    • The functions: mean(), sd(), (pqrd)qnorm()
    • Finding confidence intervals
    • Finding p-values
    • Issues with data
      • Using data stored in data frames (attach()/detach(), with())
      • Missing values
      • Cleaning up data
  • EDA graphs
    • Histogram()
    • Boxplot()
    • Densityplot() and qqnorm()
  • The t.test() function
  • P-values
  • Confidence intervals
  • The power of a t test

SESSION 2: The Two-Sample T-Tests, the Chi-Square GOF test in R
  • GUI's
    • Rcmdr
    • PMG
  • Tests with two data vectors x, and y
    • Two independed samples no equal variance assumption
    • Two independed samples assuming equal variance
    • Matched samples
    • Data stored using a factor to label one of two groups; x ~ f;
    • Boxplots for displaying more than two samples
    • The chisq.tests
      • Goodness of fit
      • Test of homogeneity or independence

SESSION 3: The Simple Linear Regression Model in R
  • The basics of the Wilkinson-Rogers notation: y ~ x
  • * y ~ x linear regression
    • Scatterplots with regression lines
    • Reading the output of lm()
    • Confidence intervals for beta_0, beta_1
    • Tests on beta_0, beta_1
  • Identifying points in a plot
  • Diagnostic plots

SESSION 4: Bootstrapping in R, Permutation Tests
  • An introduction to boostrapping
  • The sample() function
  • A bootstrap sample
  • Forming several bootstrap samples
    • Aside for loops vs. matrices and speed
      • Using the bootstrap
      • An introduction to permuation tests
      • A permutation test simulation

Instructor, Dr. John Verzani is a Professor and Chair of the Mathematics Department at the College of Staten Island of the City University of New York. His research interests and publications are in the area of superprocesses.

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.

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 21 June 2013

Natural Language Processing (NLP)

Sophisticated machine learning foundations lie behind it, one of which is natural language processing (NLP). Nitin Indurkhya, a leading authority on NLP, will present his online course on NLP at Statistics.com. For more details please visit at http://www.statistics.com/language.

"Natural Language Processing" will introduce you to the algorithms, techniques and software used in the rapidly growing field of natural language processing (NLP).  You will learn about existing applications, particularly speech understanding, information retrieval, machine translation and information extraction.

NLP draws heavily on work in computational linguistics and artificial intelligence. The course textbook will provide the necessary background in linguistics and computer science for those students who need it.  In this course only a portion of the textbook will be covered, however anyone going on to do further studies in NLP will find the textbook a very useful reference.

At the completion of the course, you should be able to read the description of an NLP application and have an idea of how it is done, what the likely weaknesses are, and often which claims are probably exaggerated. The course also prepares students to do further work in NLP by giving them a good grasp of the basic concepts.

Who can take this course:
Analysts, researchers and managers who deal with, or might need to deal with, NLP systems at a variety of levels - needs assessment, design, deployment and operation.

Course Program:

Course outline: The course is structured as follows
SESSION 1: Introduction of NLP and Word-level Analysis
  • Overview of NLP
  • Text Preprocessing
  • Corpus Creation
  • Fundamental Statistical Techniques in NLP (review)
  • Lexical Analysis

SESSION 2: Sentence-level Processing
  • Part-of-Speech Tagging
  • Context-Free Grammars (CFG)
  • Parsing of sentences with CFG
  • Statistical parsing methods

SESSION 3: Semantics
  • Representation of Meaning
  • Semantic Analysis
  • Word Sense Disambiguation

SESSION 4: Applications of NLP
  • Information Retrieval
  • Information Extraction
  • Speech Recognition Systems
  • Natural Language Generation

Nitin Indurkhya, co-author of "Text Mining" (Springer) and co-editor of the "Handbook of Natural Language Processing" (CRC), was also Principal Research Scientist at eBay and Professor at the School of Computer Science and Engineering, University of New South Wales (Australia), as well as the founder and president of Data-Miner Pty Ltd, an Australian company engaged in data-mining consulting and education.

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.

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 20 June 2013

Modeling Longitudinal and Panel Data: GEE

IID:  "Independent, identically-distributed" is the assumption behind basic modeling methods.  The real world is rarely so conveniently arranged.  Data within a school may be correlated (when compared to observations from other schools in the study).  Likewise for patient treatment centers in multi-center clinical trials.  Learn how to deal with data like this in "Modeling Longitudinal and Panel Data: GEE" with Dr. Joseph Hilbe and Dr. James Hardin, in their online course at statistics.com. For more details please visit at http://www.statistics.com/longitudinal.

This course covers the extension of Generalized Linear Models (GLM) to model varieties of longitudinal and clustered data, called panel data. Specifically, the course treats generalized estimating equations (GEE), a population averaging method that models panel data in which the response is a member of the exponential family of distributions; e.g., continuous, binary, grouped, and count. GEE is one of several methods used to model panel data - the most noted alternative being random effect models. 

The course will discuss GEE theory, relevant correlation structures, and differences in both theory and application between population averaging GEE (PA-GEE) and random effects or subject specific panel models (SS-GEE).

Who can take this course:
Social scientists, medical and psychological researchers who need to analyze and model longitudinal or panel data.

Course Program:

Course outline: The course is structured as follows
SESSION 1
  • Theory and history of GLM
  • Development of methods to analyze panel data
  • Software used for GEE and related models
SESSION 2
  • Model Construction and Estimating Equations for Panel data in general and PA-GEE specifically
  • Parameterization of the working correlation matrix
  • Scale variance estimation
  • Alternating logistic regression models
SESSION 3
  • SS-GEE models (random effect)
  • GEE2 models
  • Generalized and cumulative logistic regression
  • Problems with missing data
SESSION 4
  • Residual analysis
  • Goodness-of-fit
  • Comparative testing of models
  • MCAR assumption for PA-GEE models

Dr. Hilbe and Dr. Hardin are the authors of "Generalized Estimating Equations" (Chapman & Hall/CRC). Dr. Joseph Hilbe is Emeritus Professor at the University of Hawaii and Solar System Ambassador with NASA's Jet Propulsion Laboratory at California Institute of Technology.  He is an elected Fellow of both the American Statistical Association and Royal Statistical Society.  Dr. James Hardin is on the faculty at the University of S. Carolina, and also on the board of the "Stata Journal."

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.

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:

Wednesday 19 June 2013

Multivariate Statistics

Multivariate Statistics covers the theoretical foundations of multivariate statistics. Multivariate data typically consist of many records, each with readings on two or more variables, with or without an "outcome" variable of interest. Procedures covered in the course include multivariate analysis of variance (MANOVA), principal components, factor analysis and classification. Dr. Robert LaBudde, president and founder of Least Cost Formulations, Ltd., will present his online course “Multivariate Statistics” at statistics.com. For more details please visit at http://www.statistics.com/multivariate.

Course Program:

Course outline: The course is structured as follows
SESSION 1: Multivariate Data
  • Descriptive Statistics
  • Rows (Subjects) vs. Columns (Variables)
  • Covariances, Correlations and Distances
  • The Multivariate Normal Distribution
  • Scatterplots
  • More than 2 Variable Plots
  • Assessing Normality

SESSION 2: Multivariate Normal Distribution, MANOVA, & Inference
  • Details of the Multivariate Normal Distribution
  • Wishart Distribution
  • Hotelling T2 Distribution
  • Multivariate Analysis of Variance (MANOVA)
  • Hypothesis Tests on Covariances
  • Joint Confidence Intervals

SESSION 3: Multidimensional Scaling & Correspondence Analysis
  • Principal Components
  • Correspondence Analysis
  • Multidimensional Scaling

SESSION 4: Discriminant Analysis
  • Classification Problem
  • Population Covariances Known
  • Population Covariances Estimated
  • Fisher’s Linear Discriminant Function
  • Validation

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 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.

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: