Module Details

Module Code: ZDAT C4100
Module Title: Advanced Data Analysis and Modelling
Title: Advanced Data Analysis and Modelling
Module Level:: 8
Credits:: 5
Module Coordinator: Paula Rankin
Module Author:: Rachael Carroll
Domains:  
Module Description: To introduce the students to a wide variety of Data-Analysis, Statistical and Modelling Techniques. (The emphasis will be on description and usefulness of the techniques studied rather than with routine calculations).
To analyse a number of practical problems using computer facilities. Understanding issues to consider when designing a trial. Understanding the key statistical components involved in the planning and conduct of clinical trials.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Describe the key elements for the importance of good experimental design and apply the appropriate data-analytic techniques.
LO2 Describe and discuss key issues to consider when designing a clinical trial and the key statistical components involved in the planning and conduct of clinical trials.
LO3 Apply and recognise practical situations where a statistical or a deterministic model is appropriate.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed
 
Indicative Content
Data managment
Priciples of good data managment. Key elements of a good graph and data visualisation. Review of types of data, confidence intervals and P values. Understanding and interpreting treatment effects, statistical significance, effect size, The principle of parsimony.
Statistical tests
Parametric vs nonparametric tests. Review of key statistical tests: Tests for differences in means. Student’s T-Test, Analysis-of-Variance (ANOVA). Correlations and Significance of regression. Linear, Polynomial and Multiple Regression.
Experimental Design
Fundamental Principles of Good Design. Awareness of different types of outcomes and be able to select the appropriate statistical technique for the type of outcome and study design.
Biostatistics
Bioassays, bioavailability and bioequivalence. Prevalence and incidence. Study designs: cross-sectional, cohort, case-control, experimental, randomised control trials. Efficacy, dose response relationship, placebos. Understanding different types of trial designs and be able to choose the relevant design for a given question.
Clinical Trials
The role of the statistics in drug development. Understanding the key statistical components involved in the planning and conduct of clinical trials. Design configurations and issues, Parallel Group Design, Crossover Design, Factorial Designs. Design Techniques to Avoid Bias, Blinding, Randomization. Control groups, confounding factors. Statistical versus clinical significance. Study protocol.
Deterministic models and pharmacokinetics
The application of differential equations including pharmacokinetics such as variation of drug and metabolic levels in various fluids and tissues of the body, compartment models for mixtures, rates of drug absorption and elimination, elimination half-life and dose determination for anesthetic drugs.
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment30.00%
Practical30.00%
End of Module Formal Examination40.00%

Assessments

Full Time

Continuous Assessment
Assessment Type Practical/Skills Evaluation % of Total Mark 30
Timing n/a Learning Outcomes 1,2,3
Non-marked No
Assessment Description
Typically may include assignments, quizzes, analysis of data sets or examination.
No Project
Practical
Assessment Type Practical/Skills Evaluation % of Total Mark 30
Timing n/a Learning Outcomes 1,2,3
Non-marked No
Assessment Description
Practical exam and assignments
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 40
Timing End-of-Semester Learning Outcomes 1,2,3
Non-marked No
Assessment Description
Exam questions
Reassessment Requirement
Exam Board
It is at the discretion of the Examination Board as to what the qualifying criteria are.

SETU Carlow Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Category Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact No Description 12 Weeks per Stage 2.00 24
Practicals Contact No Description 12 Weeks per Stage 2.00 24
Independent Learning Time Non Contact No Description 15 Weeks per Stage 5.13 77
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Douglas C. Montgomery. (2020), Design and Analysis of Experiments, John Wiley & Sons, p.688, [ISBN: 1119722101].
  • Karl E Peace. (2020), Statistical Issues in Drug Research and Development, CRC Press, p.384, [ISBN: 9780367580179].
  • P.Millard A. Krause. (2012), Applied statistics in the pharmaceutical industry, 1. Springer, [ISBN: 0387988149].
  • S. Senn. (2010), Statistical issues in drug development, 1. [ISBN: 9780470018774].
  • D. G Zill. (2018), First course in differential equations with modelling applications, 11. Cengage Learning, [ISBN: 9781305965720].
  • A. Pocock. (2015), Clinical Trials A practical approach, Wiley, [ISBN: 0471901555].
  • A.J Cohen. (2008), Statistics and data with R, Wiley, [ISBN: 9780470758052].
  • Arthur J. Atkinson. (2012), Principles of Clinical Pharmacology, Academic Press, p.626, [ISBN: 9780123854711].
Supplementary Book Resources
  • C. Ralph Buncher,Jia-Yeong Tsay. (2005), Statistics In the Pharmaceutical Industry, 3rd Edition, CRC Press, p.504, [ISBN: 9780824754693].
  • D. A. Berry. (1989), Statistical Methodology in the Pharmaceutical Sciences, CRC Press, p.592, [ISBN: 9780824781170].
  • David J. Finney. (1987), Statistical Method in Biological Assay, Charles Griffin Book, p.522, [ISBN: 9780195205671].
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: