PHVIT9550 Systematiske oversikter og metaanalyser Emneplan

Healthcare decisions for individual patients and for public health policies should be informed by the best available research evidence. The evidence comes from good reviews which is a state-of-the-art synthesis of current evidence on a given research question. Systematic reviews, meta-analyses and qualitative evidence synthesis (QES) play an important role in clinical guidelines, patient information, in clinical and political decision-making and in evidence-based practice. A systematic review or qualitative evidence synthesis (QES) can also play an important role in a PhD Thesis by providing an overview of the thesis topic.

This course offers an overview of the use of systematic reviews, the method of systematic reviews, qualitative evidence synthesis (QES) and meta-analysis and how to critically appraise a systematic review or QES. The course will focus on systematic reviews of qualitative and quantitative studies and will be partly organized as parallel sessions. The course will be run as a five-day course over a period of three months.

This course is primarily aimed at PhD candidates admitted to the PhD Programme in Health Sciences but is also open to other applicants. Admission requirements are a completed major, master's degree (120 ECTS credits) or equivalent qualification.

The course can also be offered to students who have been admitted to the "Health Science Research Programme, 60 ECTS", by prior approval from the supervisor and based on given guidelines for the research programme.

On completion of the course, the PhD candidate has achieved the following learning outcomes, defined in terms of knowledge, skills, and general competence:

Knowledge

The PhD candidate:

• is at the forefront of knowledge in how to write a protocol of a systematic review or qualitative evidence synthesis
• understands the role of best evidence research in clinical and political decision-making

Skills

The PhD candidate can:

• plan and write a protocol of a systematic review or qualitative evidence synthesis
• formulate a focused research question
• plan a literature search
• conduct a systematic and explicit selection process of the available articles
• assess the methodological quality of included articles
• extract data from included studies and plan analyses
• critically assess a systematic review

General competence

The PhD candidate can:

• the principles of how to search for research literature
• the principles of how to run a meta-analysis or a synthesis of qualitative studies
• the principles of how to assess the quality of evidence by Grading of Recommendations, Assessment, Development and Evaluation (GRADE) or GRADE CERQual
• the principles of how to conduct a systematic review

Work and teaching methods consist of lectures, small group work and discussions, self-study, and practical exercises. The outcomes of the small group sessions are presented and discussed in plenary. Parallel sessions offer, teaching and small group work in critical assessment of included studies, analysis, and the quality of evidence within systematic reviews of qualitative or quantitative studies

None

This course covers the use of scripting as a programming paradigm to solve challenges like automation, integration, data manipulation and analysis. The focus is on understanding how scripting combined with utility libraries can be helpful in solving a task. Scripts can vary in length and complexity, but are normally written in a high-level language that focuses on ease of expression and readability as well as a powerful set of libraries for complex operations. Scripts can be written as a means to create tools that eases scientific work or automates tasks. They can also be used to make systems interact that would normally not. The course will use the Python programming language.

No formal requirements over and above the admission requirements.

Grades are awarded on the basis of pass or fail.

The student should have the following outcomes upon completing the course:

Knowledge

Upon successful completion of the course, the student:

• has a deep understanding of how scripting with Python is utilized to automate common tasks
• has advanced knowledge of scripting strategies that allow scripts to be robust against unforeseen failures and erroneous user input
• has advanced knowledge of how a code-base can be maintained through version control systems
• understands how scripting languages can be expanded through libraries
• knows how to use standardized packages for mathematics and statistics

Skills

Upon successful completion of the course, the student can:

• design and implement script-based tools
• evaluate and discuss how scripting may or may not facilitate automation
• use standard mathematics and statistics packages to visualize and solve relevant problems
• utilize a version control system for their code-base

General competence

Upon successful completion of the course, the student can:

• analyze automation approaches with regard to robustness and in relation to the intended tasks
• develop solution strategies for and participate in discussions about mathematical and statistical problems using scripting tools
• explain how automation and scripting can be used to automate workflows to experts and non-experts alike