ANALYSIS OF MEDICAL DATA

STA5AMD

2016

Credit points: 15

Subject outline

This course introduces statistical concepts and analytical methods as applied to data encountered in the biomedical and clinical sciences. It emphasizes how statistical inferences are applied in two common study designs, namely case control and cohort. Topics include likelihood and profile likelihood functions, the Fisher exact test, chi-square tests for association, the Mantel Haenszel test and Wollf estimation, matching and sample size determination, logistic regression, Poisson regression and related models (zero inflated and negative binomial), and receiver operating characteristic curves. The course provides students a foundation to evaluate information critically to support research objectives.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorAgus Salim

Available to Study Abroad StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites STA3BS

Co-requisitesN/A

Incompatible subjects STA4AMD

Equivalent subjectsN/A

Special conditionsN/A

Learning resources

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsPractical Statistics for Medical ResearchPrescribedAltman, D.G. (1991)CHAPMAN AND HALL: LONDON.
ReadingsStatistical Methods in Cancer Research, Vol.1, The Analysis of Case Control Studies.PrescribedBreslow, N.E. and Day, N.E. (1980).LYON: IARC SCIENTIFIC PUBLICATION NO.32.
ReadingsStatistics for EpidemiologyPrescribedJewell, N.P.(2003).CHAPMAN & HALL
ReadingsThe Statistical Evaluation of Medical Tests for Classification and Prediction.PrescribedPepe, M.S. (2004).OXFORD UNIVERSITY PRESS.

Graduate capabilities & intended learning outcomes

01. Apply the appropriate study design for biomedical research.

Activities:
Discussed and demonstrated in lectures (which incoporates the practice class). Related problems solved by students in practice class. Assignment questions, with feedback.
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Writing (Writing)
Creative Problem-solving (Creative Problem-solving)
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)

02. Derive the moment and characteristic functions (binomial, hypergeometric, Poisson, beta, chi-square, normal, etc.) for several underlying distrbutions relevant to medical data analysis.

Activities:
Discussed and demonstrated in lectures. Related problems solved by students in class. Assignment questions, with feedback.
Related graduate capabilities and elements:
Creative Problem-solving (Creative Problem-solving)
Critical Thinking (Critical Thinking)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Discipline-specific GCs (Discipline-specific GCs)
Inquiry/ Research (Inquiry/ Research)
Writing (Writing)

03. Construct likelihood and profile likelhood functions for models relevant for biomedical data analysis.

Activities:
Discussed and demonstrated in lectures. Related problems solved by students in practice class. Assignment questions, with feedback.
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Discipline-specific GCs (Discipline-specific GCs)
Critical Thinking (Critical Thinking)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Writing (Writing)
Creative Problem-solving (Creative Problem-solving)

04. Formulate appropriate hypothesis testing and construct relevant statistical models (logistic regression and Poisson regression) for analyzing biomedical problems; draw and explain the conclusions that follow from a rigorous and systematic approach.

Activities:
Discussed and demonstrated in lectures. Related problems solved by students in practice class. Assignment questions, with feedback.
Related graduate capabilities and elements:
Writing (Writing)
Critical Thinking (Critical Thinking)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Creative Problem-solving (Creative Problem-solving)
Discipline-specific GCs (Discipline-specific GCs)
Inquiry/ Research (Inquiry/ Research)

05. Perform sample size calculation for case-control (matched and unmatched) and cohort studies.

Activities:
Discussed and demonstrated in lectures. Related problems solved by students in practice class. Assignment questions, with feedback.
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Discipline-specific GCs (Discipline-specific GCs)
Critical Thinking (Critical Thinking)
Creative Problem-solving (Creative Problem-solving)
Writing (Writing)

Subject options

Select to view your study options…

Start date between: and    Key dates

Melbourne, 2016, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorAgus Salim

Class requirements

Lecture Week: 10 - 22
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Practical Week: 10 - 22
One 1.0 hours practical per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
Applied Projects20 01, 03, 04, 05
Four take-home assignments20 02, 03, 01
Two-hour examination60 04, 05, 01, 02, 03

Melbourne, 2016, Semester 2, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorAgus Salim

Class requirements

Lecture Week: 31 - 43
One 2.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Practical Week: 31 - 43
One 1.0 hours practical per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
Applied Projects20 01, 03, 04, 05
Four take-home assignments20 02, 03, 01
Two-hour examination60 04, 05, 01, 02, 03