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Multilevel and Longitudinal Modelling

Key Details

Date: 18th February 2019 – 22nd February 2019
Venue: Seminar Room 1 & 2, Main Building, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London. View Map.

Description

Course Aim

As health care data becomes more complex and multimodal, the structure of the data becomes increasingly complicated. There could be an increasing number of repeated measurements on the same individuals over time, longer duration of follow-up for time-to-event outcomes or nested hierarchies leading to non-independence of individuals. The use of simpler statistical approaches to analyse these data is invalid because the key assumptions of the those approaches do not hold. In this module, we introduce the concept of multilevel and longitudinal modelling, including time-to-event or survival analysis. The aim is for the student to understand the challenges of longitudinal and

clustered data, and the concept and implementation of multilevel models. Students will also become familiar with the most common models for time-to-event data (inc. Cox proportional hazard models, additive hazard models), and finally link all these concepts together through joint modelling of survival and longitudinal data. Students will discover Stata commands that can fit all of these models and become familiar with the resulting Stata output, whilst applying them to real data structures.

Requirements

This workshop will assume that participants have a good knowledge of basic regression and statistical modelling (as can be gained in the BHI Introduction to Statistical Modelling course running in January) any syntax based statistical software, such as STATA (such as can be obtained from the BHI Introduction to Programming course running in October or the Introduction to STATA course running in January).

Participants will need to bring their own laptop computer with STATA installed.

Learning Outcomes

Subject Specific: Knowledge, Understanding and Skills

On successful completion of this module the student should be able to:

  • Properly interpret a multilevel model, and its parameter estimates
  • Specify multilevel models, appropriate to an example study data setting
  • Implement multilevel models in Stata and be able to interpret the output
  • Achieve the above for continuous and binary responses
  • Properly interpret a time-to-event model, and its parameter estimates
  • Specify time-to-event models appropriate to an example study data setting
  • Implement time-to-event models in Stata and be able to interpret the output
  • Properly interpret a joint model and its parameters estimates
  • Implement joint models in Stata and be able to interpret the output
  • Read, understand, critique and discuss an application in the research literature
  • Make use of face-to-face learning to apply multilevel modelling or survival analysis to a typical example dataset to address a particular scientific problem

General: Knowledge, Understanding and Skills 

On successful completion of this module the student should be able to:

  • Justify and critique the use of statistical methods for real life applications.
  • Show confidence in the use of STATA to implement statistical models for real life application.
  • Interact effectively in a group through discussion forums
  • Identify key features of data, specify analysis goals and objectives, and operationalize into an appropriate statistical model
  • Develop and rehearse ability to communicate analysis findings clearly.

Cost and Booking

Cost
  • External Early bird: £675 (till 15/10/18, price thereafter £750)
  • KCL Staff Early bird: £506.25 (till 15/10/18, price thereafter £562.5)
  • KCL Student Early bird: £337.5 (till 15/10/18, price thereafter £475)
  • Other Student Early bird: £506.25 (till 15/10/18, price thereafter £562.5)
  • Kings Health Partner Early bird: £506.25 (till 15/10/18, price thereafter £562.5)

That is, 50% discount to King's College London PhD students, 25% discount to other students and staff at King's College London and King's Health Partners.

Booking

Booking for this course has now closed.

Course Team

Professor Richard Emsley (Academic Lead)

RichardEmsleyI joined the Institute of Psychiatry, Psychology and Neuroscience at King's College London as Professor of Medical Statistics and Trials Methodology in January 2018.

Prior to this, I held a personal Chair at The University of Manchester as Professor of Medical Statistics and was Deputy Director of the Manchester Clinical Trials Unit.

Research interests

My research interests are in clinical trials methodology, and developing statistical methods for efficacy and mechanisms evaluation using causal inference approaches. The applications of these methods include randomised trials of complex interventions in mental health, and trial designs and associated analysis methods in precision medicine.

My research programme aims to answer four key questions about treatments when analysing clinical trials:

  1. Are treatments effective?
  2. How do they work?
  3. What factors make them work more effectively?
  4. Which patients are they most effective for?

I develop clinical trial designs which aim to answer questions about treatments more quickly and using fewer patients.

Expertise and public engagement

Within the Department of Biostatistics and Health Informatics, I lead the Clinical Trials Methodology research theme, and am the lead for the joint department and Maudsley BRC Methodology Patient and Public Involvement group.

Externally, I am an investigator on the MRC-NIHR Trials Methodology Research Partnership, and co-lead of its Statistical Analysis Working Group. I am the founder of the European Causal Inference Meeting (Euro-CIM). I sit on the NIHR Health Technology Assessment Researcher-led Committee.

Research profile

See Richard's research profile here.

Notes

Your place will not be confirmed until payment has been made. Failure to cancel without sufficient notice will forfeit your course fee and access to future courses. If you would like to pay by internal transfer, please contact iop-biostatisticseducation@kcl.ac.uk

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