Programme
Monday July 3rd: Big Data
Learning Objectives
- Describe existing big data sources for musculoskeletal research
- Evaluate the opportunities and challenges of data linkage
- Recognise emerging types of big data from patients
- Design studies employing big data
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Time |
Session |
Presenter |
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09.00 |
Welcome and overview of course |
John McBeth |
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09.10 |
Overview of day |
Will Dixon |
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09.30 |
Big datasets |
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National registers, including linkage between registers |
Kimme Hyrich |
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UK Biobank: Overview and opportunities for MSk research |
Suzan Verstappen |
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Drug utilization databases, anonymised electronic medical records, and their use in MSk research |
Dani Prieto-Alhambra |
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11.00 |
Coffee |
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11.30 |
Practical exercise |
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1300 |
Lunch and Course photograph |
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13.45 |
Big data from patients |
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Smartphones for collecting patient data |
Will Dixon |
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U.S. experience of patient registries and ePROs |
Jeff Curtis |
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Narrative data including social media |
Goran Nenedic |
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Sensors, wearables and artificial intelligence |
Niels Peek |
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15.45 |
Coffee & Practical exercise |
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17.10 |
Groups present back to panel |
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17.30 |
End |
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20.00 |
Welcome reception |
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Tuesday July 4th: Longitudinal studies
Learning Objectives
- To apply the right model for longitudinal data analysis
- To understand the impact of missing data in longitudinal studies
- To gain an understanding of multi-state models
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Time |
Session |
Presenter |
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09.00 |
Overview of day |
Suzan Verstappen |
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09.05 |
Norfolk Arthritis Register |
Suzan Verstappen |
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09.30 |
Introduction to longitudinal data analysis |
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Dealing with missing data/attrition in longitudinal analysis |
Mark Lunt |
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Repeated measurement analysis |
Mark Lunt |
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10.30 |
Practical |
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11.30 |
Coffee |
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12.00 |
Latent Class Growth Models |
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Introduction in LCGM and applied examples |
Sam Norton |
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13.00 |
Lunch |
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14.00 |
Time to event analysis |
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Introduction to time to event analysis |
Brian Tom |
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Multi-state models |
Brian Tom |
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15.30 |
Coffee |
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16.00 |
Feedback on practical |
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17.30 |
End |
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Wednesday July 5th: Pharmacoepidemiology
Learning Objectives
- Give examples of common pitfalls in pharmacoepidemiological studies
- Recognise a range of analytic tools and understand their practical application
- Propose and justify study designs for future pharmacoepidemiological research
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Time |
Session |
Presenter |
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09.00 |
Overview of day |
Will Dixon |
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09.10 |
Mind the gap: Setting up data for analysis |
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Data preparation: the unreported step |
Rebecca Joseph |
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Handling missing data |
Jamie Sergeant |
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10.15 |
Coffee |
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10.45 |
Practical exercise: Data preparation |
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12.00 |
Watch the time: Considering time in PhE analyses |
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Segmented regression and joinpoint regression |
Kelvin Jordan |
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Rik attribution, including weighted cumulative dose |
Will Dixon |
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13.00 |
Lunch |
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14.00 |
Dealing with confounding and effect modification |
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Propensity scores and PS analytics |
Mark Lunt |
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Case only designs in Pharmacoepidemiology |
Ian Douglas |
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Effect modification for stratified medicine |
TBC |
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15.30 |
Coffee |
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16.00 |
Practical exercise: Critical appraisal |
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17.30 |
End |
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Thursday July 6th: Clinical Trials
Learning objectives
Students will develop
- A theoretical and practical understanding of the issues involved in the design, conduct, analysis and interpretation of randomised controlled trials of MSK interventions
- Skills to critically appraise randomized clinical trials
- An understanding of the scope and rationale for different trial designs in determining effectiveness of MSK interventions
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Time |
Session |
Presenter |
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09.00 |
Overview of day |
Terry O’Neill |
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09.05 |
Clinical trials - Introduction |
David Felson |
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10.00 |
Practical exercise |
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Trial Design |
David Felson |
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11.00 |
Coffee |
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11.30 |
Critical appraisal of randomised trials |
Terry O’Neill, David Felson |
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12.00 |
Practical exercise |
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Critical appraisal |
David Felson |
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13.00 |
Lunch |
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14.00 |
Alternate trial designs |
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Trials of complex interventions |
Peter Bower |
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Adaptive trial designs |
Thomas Jaki |
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Pragmatic trials/N of 1 trials |
Tjeerd van Staa |
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15.30 |
Coffee |
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16.00 |
Practical exercise |
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Study design |
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17.00 |
End |
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1800 |
Summer BBQ |
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Friday July 7th: Causal inference
Learning objectives
- Understand different concepts and descriptions of causation in epidemiological research
- Understand graph theory that relates to causal models
- Understand the construction, application and analysis of Directed Acyclic Graphs
- Describe the similarities and differences in causal inference between observational and randomised studies
- Design a study to test a question around causal inference
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Time |
Session |
Presenter |
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09.00 |
Overview of day |
John McBeth |
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09.10 |
Practical session |
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Designing a study to test causal hypotheses |
John McBeth |
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10.00 |
Causal inference theory |
Mark Lunt |
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11.00 |
Coffee |
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11.30 |
Graph theory and Directed Acyclic Graphs |
Robin Evans |
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12.30 |
Lunch |
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13.30 |
Causal inference in observational and randomised studies |
Richard Emsley |
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14.30 |
Coffee |
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15.00 |
Practical session |
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Re-visiting your study |
John McBeth |
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16.00 |
Group presentations and faculty feedback |
John McBeth, Mark Lunt, Robin Evans, Richard Emsley |
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17.00 |
Close of Summer School |
John McBeth |