HLSC409 - Statistics for Biomedical and Health Science Research

Year

Credit points

Campus offering

Unit rationale, description and aim

Understanding, using and interpreting statistics is crucial to biomedical and health sciences research and practice, particularly in monitoring health outcomes and decision-making processes about interventions. This unit will develop students’ knowledge of fundamental statistical concepts, such as descriptive and inferential statistics, common statistical tests and statistical methods frequently used in biomedical and health sciences research. This will include hypothesis testing, estimation, associations, modelling relationships and prediction using different methods such as regression analyses. Throughout the unit, students will consolidate their understanding of statistical theory through its application to practice. While there are some formulae and computational elements to the unit, the emphasis is on interpretation and concepts. Besides the theoretical material, this unit will also enable students to run basic analyses using common statistical software. Using this software, students will analyse simulated health science data sets and then interpret the results obtained. This unit aims to extend students’ statistical understanding and analytical expertise, which can then be applied to practice through critical appraisal of the statistical methods used in biomedical and health sciences research.

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

On successful completion of this unit, students should be able to:

LO1 - Identify appropriate statistical techniques and their application to biomedical and health science practice (GA5) 

LO2 - Distinguish between different statistical tests, especially in terms of application and interpretation (GA4, GA5, GA6)

LO3 - Perform appropriate statistical analyses using common statistical software and interpret the results (GA6, GA8, GA10)

LO4 - Develop a sound statistical approach to the analysis and interpretation of biomedical and health science data and communicate findings in an academic-standard output (GA4, GA8, GA9)

LO5 - Critique biomedical and health science research on the basis of its statistical methods, analysis and interpretation (GA4, GA5, GA8, GA9)  

Graduate attributes

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA6 - solve problems in a variety of settings taking local and international perspectives into account

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA9 - demonstrate effective communication in oral and written English language and visual media 

GA10 - utilise information and communication and other relevant technologies effectively.

Content

Topics will include:

 Fundamental statistical concepts and methods

• Types and levels of measurement of quantitative data and measures of central tendency and variability
• Probability distributions
• Hypothesis testing
• Statistical confidence: confidence intervals, p-values, statistical significance, effect sizes
• Common statistical tests: comparison of means (between two or more dependent or independent groups), proportions
• Variability and statistical inference; power and sample size; bias, confounding and adjustment

 Application to biomedical and health science practice

• Key measures of association in biomedical and health sciences: relative risk, odds ratios
• Inferential statistics: correlation, linear regression, logistic regression, analysis of variance
• Use of statistical software to analyse quantitative data sets: common statistical tests
• Writing up statistical analyses: interpretation, requirements for expressing statistical results
• Critical appraisal of statistical methods in biomedical and health sciences research: common tools and approaches

Learning and teaching strategy and rationale

Multi-mode

The learning and teaching strategy adopted in the multi-mode delivery of this unit aligns with the learning outcomes and uses a flipped classroom approach to engage and support students in an active learning experience in which they gain knowledge of biostatistics and learn about the applications of biostatistics in biomedical and health science practice. Students will acquire essential biostatistical knowledge via a series of asynchronous online lectures that include recorded online content; online readings and self-directed learning activities.  These learning activities, which allow students to progress at their own pace, will be supplemented with weekly face to face tutorials that guide students in translating the theoretical knowledge delivered online into practical applications and outcomes.  Activities focus on analysis of simulated biomedical and health science data sets to provide an authentic learning experience to students in data analysis and interpretation. Students are encouraged to attend tutorial classes facilitated by an expert to develop their skills and knowledge in biostatistics with other students, and deepen their level of understanding.

Online mode

The learning and teaching strategy adopted in the online delivery of this unit mirrors that of the multimode version. In online mode, students acquire essential theoretical knowledge in biostatistics via a series of synchronous or asynchronous online lessons which include: recorded lecture content, online readings, online discussion forums and self-directed learning modules. Facilitated synchronous or asynchronous online tutorial classes (virtual classroom) will be used to aid students in the construction and synthesis of this knowledge using expert led, and peer to peer strategies to develop students’ abilities to apply biostatistics principles and approaches to contemporary biomedical and health science issues.

Learning activities are designed to be suitable for both multimode and online deliveries to ensure students are achieving learning outcomes equitably. Learning content will be adapted between modes of delivery to account for current information and communication technologies.

Assessment strategy and rationale

A range of assessment procedures will be used to meet the unit learning outcomes and develop graduate attributes consistent with University assessment requirements. In order to successfully complete this unit, students need to complete and submit three graded assessment tasks and obtain an aggregate mark of greater than 50%.

HLSC409 involves assessment tasks designed to introduce students to the broad range of activity involved in biostatistics. In Assessment Task 1, students are required to demonstrate their understanding by analysing a health science data set. In Assessment Task 2, students are required to apply their biostatistical skills by preparing statistical methods and analysis for a peer reviewed journal article. Finally, in Assessment Task 3 students will critique and interpret the statistics in current published journal articles and reflect on how biostatistical knowledge will shape their professional practice.

All assessment tasks will be submitted electronically. 

Overview of assessments

Representative texts and references

Rowntree, D. (2018). Statistics Without Tears: An Introduction For Non-Mathematicians (1st ed.) Penguin Press.

Kirkwook, B.R., & Sterne, J.A.C. (2003). Essential medical statistics 2nd ed. Malden, Massachusetts: Blackwell Science [ACU ebook]

Bush, H.M. (2012). Biostatistics: An applied introduction for the public health practitioner. Clifton Park, NY: Delmar Cengage Learning.

Cook, A., Netuveli, G., & Sheikh, A. (2004). Basic skills in statistics: A guide for healthcare professionals. London: Class Publishing.

Gordis, L. (2014). Epidemiology (5th ed.). Philadelphia, PA: Elsevier/Saunders.

Munro, B. (2005). Statistical methods for health care research (5th ed.). Philadelphia, Pa: Lippincott.

Newell, R., & Burnard, R. (2011). Research for evidence-based practice in health care (2nd ed.). Chichester, England: Wiley-Blackwell.

Pallant, J. F. (2016). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (6th ed.). Crows Nest: Allen & Unwin.

Rugg, G. (2007). Using statistics: a gentle introduction. Maidenhead, Berks: Open University Press.

Pagano, M., & Gauvreau, K. (2018). Principles of biostatistics. CRC Press.