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  • Jan 22 / 2021
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Current Research, Ryan Field

RESEARCHER SHOWCASE: Ryan Field and Joint Modelling in Heart Failure

Ryan is a second year PhD candidate in Public Health, investigating joint modelling within heart failure.

Joint Modelling is a statistical technique for modelling longitudinal repeat measures together with survival data.

The Longitudinal data is typically modelled using an LME (Linear mixed effects model), whereas the survival data is modelled using standard regression survival models such as Cox Proportionate Hazards Model

These models are then linked through an association structure via their shared random effects, i.e. the individual patients or participants, resulting in a joint model

For my PhD I am exploring the uses of Joint Modelling within heart failure, with the aims to produce a prognostic model using repeat measures

Some advantages of Joint Modelling are:

  • It can reduce Bias both with respect to treatment effect and (bias) due to censoring
  • It is more efficient than traditional Models
  • And it allows the use of repeat measure while inherently accounting for measurement error and allowing for different follow-up times

On the other hand, some disadvantages are:

  • It requires repeat measurements to be integrated in the study design 
  • More complex and harder to fit than traditional models thus taking longer to fit
  • And not as well known as traditional model

Currently in heart failure  the primary focus is on the association of longitudinal repeat measurements and an endpoint such as a composite endpoint

Longitudinal measurements include: Quality of Life and Bio Markers such as: Natriuretic Peptides e.g. NT-ProBNP and Renal markers such are Creatinine

Other novel applications include physical activity as reported by an implanted device such as an ICD

There are a limited number of studies looking at prognostic models and individual patient survival predictions

My research aims to focus more on the prognostic value of bio markers in Heart Failure using data collected from randomised control trials in an effort to improve the existing prognostic models and create new prognostic models using joint modelling.

From the image we can see the results from a joint model presented as a graph looking at a patient specific trajectory with the longitudinal measurements on the left and the survival probability on the right.

The model assumes that the patient has survived until the last measurement and calculated the survival probability from that point.

The joint model can also provide coefficients from both the longitudinal and survival models with the latter being exponentiated to produce a hazard ratio e.g. of treatment effect or the association of the biomarker through an association parameter.

Ryan can be contacted at:

Twitter: @Ryan_J_Field

Email: r.field.1@research.gla.ac.uk

This presentation was presented as part of an IHW PGR half-day conference, All aboard

References

  • Tsiatis AA, Degruttola V, Wulfsohn MS. Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS. J Am Stat Assoc. 1995;90(429):27–37.
  • Tsiatis AA, Davidian M %J SS. Joint modeling of longitudinal and time-to-event data: an overview. 2004;809–34.
  • Ibrahim JG, Chu H, Chen LM. Basic concepts and methods for joint models of longitudinal and survival data. J Clin Oncol. 2010/05/05. 2010;28(16):2796–801.
  • Papageorgiou G, Mauff K, Tomer A, Rizopoulos D. An Overview of Joint Modeling of Time-to-Event and Longitudinal Outcomes. Annu Rev Stat Its Appl. 2019;6(1):223–40.
  • Sudell M, Kolamunnage-Dona R, Tudur-Smith C. Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis. BMC Med Res Methodol. 2016;16(1):168.
  • Rizopoulos D. JM: An R package for the joint modelling of longitudinal and time-to-event data. J Stat Softw. 2010 Jul;35(9):1–33.
  • Rizopoulos D. The R package jmbayes for fitting joint models for longitudinal and time-to-event data using MCMC. J Stat Softw. 2016 Jan;72(1):1–46.
  • Rizopoulos D. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. 2012.
  • Dec 18 / 2020
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Alexandra Rose, Current Research, Uncategorized

RESEARCHER SHOWCASE: Alexandra Rose and The assessment of mood after severe brain injury

Alexandra Rose (CPsychol) is a Principal Clinical Psychologist based in a London hospital, working with patients with brain injury. Her research is focused on understanding the assessment of mood, depression and distress after severe brain injury. She is supervised by Professor Jonathan Evans and Dr Breda Cullen. Alex is in the 2nd year of pursuing her PhD in Psychological Medicine. She is studying remotely whilst continuing her clinical work and is being assisted financially by a Francis Newman Foundation grant.  The following is part of her project exploring the assessment of mood after severe brain injury.

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  • Dec 17 / 2020
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Current Research, Warut Aunjitsakul

RESEARCHER SHOWCASE: Warut Aunjitsakul and maintenance mechanisms of social anxiety disorder in people with psychosis

Warut Aunjitsakul is a psychiatrist and clinical instructor from Prince of Songkla University, Thailand and very keen to develop theoretical understanding and improve psychological intervention in people with psychosis. He is now pursuing his PhD in Psychological Medicine.  

The following is part of Warut’s PhD project aiming to understand the maintenance mechanisms of social anxiety disorder in people with psychosis. 

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  • Dec 14 / 2020
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Current Research, Meigan Thomson

RESEARCHER SHOWCASE: Meigan Thomson and Barriers and Facilitators of Weight Loss in participants of a Behavioural Weight Loss Programmes.

Meigan is a PhD student based in the MRC/CSO Social and Public Health Sciences unit and is supervised by Professor Sharon Simpson, Dr Anne Martin, Dr Emily Long and Dr Jennifer Logue. Meigan’s PhD topic is Understanding the Barriers & Facilitators of Weight Loss in adults participating in Behavioural Weight Loss Programmes.  

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  • Mar 25 / 2019
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Alessio Albanese, Current Research

Researcher showcase: Alessio Albanese and the impact of post-migration life difficulties on mental health

I am in my first year of completing a PhD looking into the impact of post-migration life difficulties on mental health and somatic symptoms. I would like to take this opportunity to present my current work which focuses on the mental health and somatic symptoms of asylum seekers and refugees in the context of post-migration life difficulties. In addition to presenting my research work as it is developing, I would also like to briefly talk about my personal background and how this has influenced my personal and academic development.

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  • Mar 13 / 2019
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Current Research, Lauren Gatting

Promoting some of our ECR’s work

On Tuesday 26th February, Seven Early Career researchers working within UoG’s Institute of Health and Wellbeing presented their work, during the institute’s annual research away day. Following the format of the three minute thesis competitions held in universities worldwide, each presentation had to be under 3 minutes long and use only one power point slide (no animations allowed). During the away day, the presentations were judged by a panel for winners of 1st, 2nd and 3rd place. All the presentations were excellent.

I drew up a brief summary of each person’s work, while they were presenting, which I now present to you:

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  • Jun 17 / 2015
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Current Research, Siobhán O’Connor

Enabling patient-centred care through information and technology

By Siobhan O’Connor:

A snapshot of this year’s Kings Fund Digital Health and Care Congress in London highlighted the focus on enabling patient-centred care through information and technology. Beverley Bryant, Director of Digital Technology for NHS England outlined the NHS’s Five Year Forward View and the Department of Health’s Personalised Health and Care 2020 framework. These two important strategy documents outline how health services in the United Kingdom will be transformed through information technology over the next fives years.

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