SFKKA9041U  Statistical Design and Analysis of Experiments

Volume 2017/2018
Education

MSc Programme in Pharmacy or Pharmaceutical Sciences (Danish programmes cand.pharm and cand.scient.pharm) - elective

MSc Programme in Medicinal Chemistry - elective

MSc Programme in Pharmaceutical Sciences (English programme) - elective

Content
  • Principles of confounding, randomization, blocking and balancing.
  • Choice of necessary sample size.
  • Estimation and testing of treatment effects and components of variance.
  • Experiments with many factors.
  • Restrictions on randomization and elementary split plot and repeated measures designs.
  • Some insight into the design and analysis of experiments with a dichotomous outcome (binomial trial).

 

The syllabus is presented by three parallel activities. The theoretical and methodological content is covered in about 12 double lectures based on the textbook and lecture notes. Pharmaceutical applications and case studies related to the most important activities involving discovery and development of pharmaceutical products are presented in a series of about six double lectures. Training in the practical design and analysis of experiments is provided in a series of nine double classroom sessions, in which the different methods are discussed and practised using manual calculations and statistical software.

The purpose of the course is to introduce a general statistical approach to the design of laboratory and similar experiments and to analyse the resulting data. Along with the statistical and methodological content of the course, a number of concrete and frequently used pharmaceutical applications (designed experiments) are presented. Examples are clinical trials (including e.g. crossover and repeated measures designs), toxicity testing, bio-equivalence analyses, assay validation, design and analysis of epidemiological surveys, etc. Regarding the statistical design of experiments, the main objective is to assure the accuracy and precision of the data so that reliable and reproducible conclusions can be drawn concerning the relations being studied.

Learning Outcome

At the end of the course, students are expected to be able to:

Knowledge

  • use and understand output from modern statistical computer software.

 

Skills

  • carry out statistical analysis of data obtained from a given experimental design, most often using variance and regression analysis techniques.
  • present and interpret the results obtained from a experimental design.

 

Competences

  • design relevant experimental work to support the lifecycle phases of a new pharmaceutical product: discovery, development, production and quality control.
  • judge the necessary amount of experimentation, take into account practical restrictions and anticipate and prevent/mitigate sources of the types of bias often encountered during these phases.

Design and Analysis of Experiments, D. C. Montgomery, 7. ed., 2009.

Lecture notes are available from the course homepage.

Necessary statistical prerequisite for the course is good knowledge of elementary statistics.
Lectures/seminars: 33 hours
Classroom exercises: 18 hours
Credit
7,5 ECTS
Type of assessment
Written examination, 4 timer under invigilation
The written examination is a two-part questionnaire. The first part is an essay-type, practical question asking the student to partially design an experiment and/or do a statistical analysis. The second part is a multiple-choice test with questions generally related to important issues discussed in the course.
Aid
Written aids allowed

There is access to the following at the exam on Peter Bangs Vej:

  • Office (Word, Excel, Onenote and Powerpoint)
  • IO2 – the digital pen
  • Panoramic Viewer
  • Paint
  • Calculator – Windows' own
  • R – Statistical programme
  • ITX MC – multiple choice programme
  • Adobe reader
  • MathType formel programme
  • Latex
  • LYX (Latex formel editor)
  • USB access – for usb stick with notes etc.
  • Programmes for assisting with dyslexia
Marking scale
7-point grading scale
Censorship form
External censorship
Criteria for exam assesment

To achieve the grade 12 the student must be able to:

Knowledge

  • use and understand output from modern statistical computer software.

 

Skills

  • carry out statistical analysis of data obtained from a given experimental design, most often using variance and regression analysis techniques.
  • present and interpret the results obtained from a experimental design.

 

Competences

  • design relevant experimental work to support the lifecycle phases of a new pharmaceutical product: discovery, development, production and quality control.
  • judge the necessary amount of experimentation, take into account practical restrictions and anticipate and prevent/mitigate sources of the types of bias often encountered during these phases.
  • Category
  • Hours
  • Lectures
  • 33
  • Preparation
  • 151
  • Colloquia
  • 18
  • Exam
  • 4
  • Total
  • 206