Undergraduate Course: Research Skills in Health Sciences (BIME10052)
Course Outline
| School | Deanery of Biomedical Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course provides an introduction to the range of research methodologies including data analysis employed in health science investigation from developing research questions through planning, conducting and analysing research data and its reporting and publication. This course will equip you with the core research and key transferrable skills to assist you during the Health Sciences programme. |
| Course description |
This course is made up of lectures and small group teaching and will provide up to date information covering the range of research methodologies including data analysis employed in health science investigation from developing research questions through planning, conducting and analysing research data using appropriate statistical methods and its reporting, communication and publication. This course will equip you with the core research and key transferrable skills to assist you during the Health Sciences programme. The course is taken in conjunction with an associated 20-credit course comprising Scientific Frontiers of Medicine, Surgical Sciences or Primary Care and will enable students to gain a broad and selectively deeper understanding of the application of biomedical and clinical science in the practice of medicine.
Outline Content
This course is made up of lectures and small group teaching and will provide up to date information covering the range of research methodologies including data analysis employed in health science investigation from developing research questions through planning, conducting and analysing research data using appropriate statistical methods and its reporting, communication and publication. This course will equip you with the core research and key transferrable skills to assist you during the Health Sciences programme. The course is taken in conjunction with an associated 20-credit course comprising Scientific Frontiers of Medicine, Surgical Sciences or Primary Care and will enable students to gain a broad and selectively deeper understanding of the application of biomedical and clinical science in the practice of medicine.
Outline Content
The scientific method and the nature of scientific certainty
Hypothesis testing vs discovery science (or fishing)
Retrospective vs prospective studies
Association vs causation
Observational vs intervention
Importance of appropriate controls and quality control
Quantitative vs qualitative vs mixed research methods
Interpretation of results
Why we need statistics
Lessons from bad research - Importance of pre-defining end points and analysis approach
Dangers (and insights) from sub-group analysis
Confounding factors
Over interpreting results
Scientific fraud and the drive to publish
Formulating and answering research questions
Formulating a research question - can it be answered from the literature?
Searching databases of publication knowledge, quality of journals and articles
Critical appraisal
Rationale and outline approach to combining published data
Can it be answered from existing data?
Awareness of potentially accessible datasets such as census, British social attitudes, ISD, various registries, gene phenotype datasets, novel data sets (big data).
Potential for powerful linkages between datasets
Examples insights from re-analysis / linkage old data
Would a study help?
What would it take to investigate?
Importance of readout and consideration of the importance of the question versus effort, cost and potential for harm
Patient data & data governance
Data ownership, confidentiality, permission to access
Patient consent for processing, audit vs research
Data husbandry and record keeping
Patient identifiable data
DPA, protection, processing, processing, legal requirements
Organisational processes and mechanisms
Data safe havens, census data
Creating anonymous datasets
Research Ethics
Procedures for gaining access to patient identifiable data sets
Ethical review
Public Benefit and Privacy Panel Review
Caldicott review
Finding and interpreting association and correlation
Association and correlation
Assessing association: Strength and significance
Cautions: data completeness, unmeasured and correlated variables
Interpretation
Extension to higher dimensional and large data sets
Issues arising from multiple tests and approaches to interpretation
Practical class - Analyse a data set beginning with an exploration of the data (types, completeness, correlation), chi square tests of detected associations, discuss significance and interpretation
Introduction to multiple regression and logistic regression
Assessing complex data sets for association and correlation Example application to data set
Dealing with data completeness and correlated variables
Review of results and their interpretation
Introduction to Qualitative Research
Philosophical underpinnings of health services research
Epistemology (the nature of knowledge)
Research paradigms
When is a qualitative approach useful?
Qualitative research methods
Interviews, focus groups, textual analysis, action research
Data collection and analysis
Communication of findings (written and verbal)
Combining with RCTs/other quantitative methods
Clinical trials
Clinical trial design - types of design
Randomisation, blinding, sub grouping, matching subjects, primary, secondary and composite outcomes, patient reported outcomes
Planning for robustness: design, size, power calculations
Role of pilot and feasibility studies
Sources of variation and bias
Planning for robust conclusions
Design of studies involving an intervention
Trial framework
Ethical and scientific approval processes
Registries of trials
How trials are funded
Audit and Quality control
Approaches to combining the results of distinct studies and investigative types
Why this is not straightforward
Combining results from published studies
Robust meta-analysis
Standard presentation of results
Pitfalls
Combining results from inter-related qualitative studies (Metanalysis)
Combining quantitative and qualitative methods - mixed model advantages
Understanding tests commonly employed in clinical and laboratory research
Techniques employed in hospital laboratories e.g. FACS, ELISA, Agar plates, PCR, EPS,
How are they done, implications for interpretation
Collection and preservation of clinical specimens (blood, urine, tissue) including importance for subsequent interpretation examples (biobanks)
Variation in test results
Sources and types of variation
Normal ranges, laboratory variation, error.
Measurement controls and standardisation
Examples of core science research technologies: Cell culture, Electrophoresis, Flow cytometry and FACS, Transgenic animals including CRISPR technology, Genomics, Proteomics
Drawing conclusions from data
The initial assessment
Types of data: continuous, categorical, counts etc.
Determining and improving data quality / completeness
Describing data
Type, range, variation, bounding
Testing approximation to convenient distributions
Role of transformation to more convenient distribution
Making comparisons
When to use and selection of parametric technique
Non-parametric techniques
Interpreting and reporting significance and the null hypothesis.
Examples
Comparing numeric continuous data that is normally distributed (e.g. Blood pressure)
Comparing numeric data that is not normally distributed - use of transformations
Comparing count data
Evaluating data for associations
Correlation and regression on numeric data
Associations in categorical data: role of chi square and logistic regression approaches.
Machine learning techniques.
Importance of distinct data sets for model development and testing
Extension to massive and higher order data
Seeking vs confirming
How to get help with more sophisticated analysis
Reporting data
Clarity in describing numeric data and statistics
Use of tables and graphs: labelling, statistics, colour
Recognise and admitting sources of uncertainty in conclusions
Importance of repeat testing positives and publishing negative data.
Presenting research data
Common presentation formats: what is expected and how to prepare
Design of tables and charts for slides and posters
Suggestions on production of posters and slides.
Seeking publication
Awareness of predatory journal and conference meetings.
Research and society
Influences on research agenda of societal, financial, political and commercial considerations
Research funding and prioritisation
Communication and engagement with patients and public
Accountability: research governance and record keeping
Ownership, protection, profit
This is a core component of the Intercalated BMedSci in Health Sciences. The course will cover the range of research methodologies employed in health science investigation from developing research questions through planning, conducting and analysing research data using appropriate statistical methods and its reporting, communication and publication. The course will equip students with core transferrable research skills and understanding for the Health Sciences programme. The course will develop the ability of students to design, and conduct research in the area of Health Sciences and help them develop the skills and attributes required to plan and conduct a high quality research project that will be expected of them in semester 2.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2018/19, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Full Year |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 28,
Supervised Practical/Workshop/Studio Hours 16,
Formative Assessment Hours 4,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
148 )
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| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
2 hour written exam (100%) |
| Feedback |
Students will be given formative feedback in relation to the small group work. A formative assessment with feedback will be available prior to the May examination.
A structured questionnaire will be used to gather student feedback on the components of the course.
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| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Acquire a broad appreciation of how health-related issues are identified, investigated and analysed including the regulatory frameworks appropriate to laboratory and patient data and the hierarchy of levels of evidence.
- Apply knowledge and understanding to choose with critical justification the most appropriate research and statistical methodologies in varied circumstances.
- Make effective use of knowledge and understanding to plan research investigations (e.g. clinical trials or laboratory studies) identifying and managing issues that impact the conduct of a study or interpretation of the results generated.
- Make effective use of knowledge and understanding to analyze research data and draw justified measured conclusions.
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Learning Resources
University library
Review articles
Original relevant research journal articles
On-line resources
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Additional Information
| Graduate Attributes and Skills |
After completion of this course, students will be able to:
- Describe the generic investigative techniques and methodological tools that are broadly applicable to many areas of research in Health Sciences.
- Discuss the interpretation, evaluation and integration of results across a range of these methodologies.
- Apply knowledge and critical understanding of the theories, concepts and principles of research methodologies to a specific research question in Health Sciences.
- Apply knowledge and critical understanding to make an independent judgement of the robustness of published data in research papers as well as make judgements where data is limited or comes from various sources.
- Interpret, use and evaluate numerical and graphical research data
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| Keywords | Research skills,scientific methodology,laboratory,research,Healthcare,Patient,Project,Clinic |
Contacts
| Course organiser | Dr Richard Phelps
Tel: (0131 6)51 1654
Email: Richard.Phelps@ed.ac.uk |
Course secretary | Miss Morag Wilson
Tel: (0131 6) 509 414
Email: morag.wilson@ed.ac.uk |
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