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Introduction to Statistical Analysis of Laboratory Data

 
  January 23, 2018  
     
 
CfPIE - The Center for Professional Innovation & Education, Boston, MA USA
October 11 & 12, 2018


Course Description - Course runs 9:00 to 5:00 both days (Breakfast & Lunch Included)

Basic Methods (Day One). This section of the course will detail the basic and intermediate statistical concepts that are essential for professionals in the field. The first day emphasizes the principles of descriptive and inferential statistical applications and focuses on actual study examples, problem solving and interpretation of results. Throughout the course the participants are encouraged to ask questions and discuss examples relevant to their own work. Topic areas to be discussed include, but are not limited to:

  • Basic statistical terminology including simple statistics as well as geometric ( e.g. means, standard deviations) transformations needed to effectively communicate and understand your data results
  • The statistical testing (one sided, two sided, non parametric, sample size, and power considerations) essentials required to initiate a research investigation (i.e., research questions in statistical terms)
  • Concepts of accuracy and precision in measurement analysis to ensure appropriate conclusions in experimental results including between and within laboratory variation results
  • Discussion of statistical techniques to compare experimental approaches with respect to specificity, sensitivity and linearity
  • The instructor gives a detailed description of topics discussed in the his latest publication, "Introduction to Statistical Analysis of Laboratory Data" by  Alfred  A. Bartolucci, Karan Singh and Sejong Bae (2015).

Advanced Topics (Day Two). This section of the course will go beyond the basics and cover more complex issues in laboratory investigations with examples. Topics will include:

 

  • Association studies including correlation and regression analysis with laboratory applications
  • Analysis of robustness and ruggedness
  • Method comparison using more accurate alternatives to correlate analysis and other pair-wise comparisons
  • Outliers, limit of detection and limit of quantitation
  • Statistical quality control for process stability and capability

Who Should Attend

This course is designed as an introduction to the statistical principles of laboratory data analysis and quality control that form the basis for the design and analysis of laboratory investigations. The course curriculum will benefit R&D managers, analytical laboratory supervisors and staff, manufacturing and production professionals, scientists, technicians and others who wish to comprehend and interpret methods of data analysis relevant to laboratory experimentation. Where applicable, topics are presented with relevant regulatory requirements.

This training will concentrate on the philosophy and understanding of the statistical principles required in conducting sound scientific investigations of laboratory processes and validation, including design and sample size issues. It will not simply present statistical formulae and the lectures are oriented toward professionals having minimal formal training in statistics or mathematics beyond basic algebra. However, for those with more formal training in statistics wishing to actually apply the techniques, appropriate time and references will be given for the procedures involved.

 
 
Organized by: Center for Professional Innovation & Education
Invited Speakers:

Al Bartolucci, Ph.D.

Specialties: Management

Dr. Al Bartolucci is Emeritus Professor of Biostatistics at the University of Alabama where he also serves as a Senior Scientist at the Center for Metabolic Bone Diseases, AIDS Research Center and Cancer Center.

He previously served as Chairman of the Department from 1984 through 1997. He has also taught Statistical Software courses involving Data Exploration, ANOVA/Regression and Design of Experiments. His teaching experience includes areas such as, Clinical Trials, Survival Analysis, Multivariate Analysis, Regression Techniques and Environmental/Industrial Hygiene Sampling and Analysis, Bayesian Statistics, and Longitudinal Data Analysis.

Dr. Bartolucci received his PhD in Statistics from the State University of New York at Buffalo and his MA in Mathematics from Catholic University, Washington DC, and his BA in Mathematics from Holy Cross.

Other Courses:

Publications:

He is widely published with over 300 publications and some of his recent works include:

  • Bartolucci, Al: Bayesian modeling of pharmaceutical data addressing the average effect of bivariate parameters of interest in a bioequivalence framework, page 166, December 2011, Journal of International Modeling and Simulation, Vol
  • Bartolucci, Al: An application of EM algorithm in prostate carcinoma data, page 525, Epidemiology, Health and Medical Research
  • Bartolucci, Al: Meta-analysis of multiple primary prevention trials of cardiovascular events using aspirin, page, American Journal of Cardiology
 
Deadline for Abstracts: N/A
 
Registration: https://www.cfpie.com/ProductDetails.aspx?ProductID=240
E-mail: info@cfpie.com
 
 
   
 
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