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Modelling multiple ordinal outcomes in clinical trials


   Peninsula Medical School

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  Dr Y Wei  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Background to the project: Ordinal outcome data are frequently reported in clinical trials, where participants’ outcomes are “rated” on ordered categorical scales. These data consist of the information about outcome rating for individual participants and the ordering of the rating. The proposed methods to be developed in this project will be useful for analysing clinical trials with ordinal outcomes, such as Quality of Life, pain, disease severity, obstetric outcomes, stroke, mental health outcomes, and data obtained from ordinal scales in questionnaires for health. 

Naïve statistical methods, such as dichotomising ordinal outcomes into two categories only or transforming them to a continuous scale, remain widespread in practice, leading to a substantial loss of information where data are discarded, degraded or have unrealistic assumptions imposed for individual outcomes in trials. The development of the methods proposed in this project are key to helping to solve this problem, through developing realistic models which make use of all available information provided by ordinal outcomes and to make reliable estimates of the effects of interventions.

What the studentship will encompass: This project provides an exciting opportunity to contribute to the methodological development for the analysis of ordinal outcomes reported in clinical trials. The proposed outline of the project is as follows.

1)      Develop methods for analysing multiple ordinal outcomes from clinical trials

We will develop methods for estimating intervention effects and the associated variance-covariance matrix. We aim to incorporate all available information from multiple ordinal outcomes in order to describe the association between them, leading to more precise estimation and predictions of the intervention effects. The novelty is that we will model multiple ordinal outcomes simultaneously.

2)      Include participant characteristics in the multiple ordinal outcome model

The extension of the models to include participant covariates can help identify which population(s) the intervention should potentially target. Where appropriate, we will also investigate the interaction between the intervention effects and covariates. Methods for dealing with missing covariate values will also be compared.

3)      Extend the methods to analyse multiple trials with multiple ordinal outcomes

The methods for trial analysis will be extended to meta-analysis of multiple clinical trials. A model is first fitted to each trial separately, and then the estimated intervention effects are combined across trials in the second stage. In a one-stage approach, a hierarchical model will be fitted to data from all trials to estimate the overall effect. As well as participant level covariates, we will also include trial-level covariates to explain the between-trial heterogeneity.

 4)      Application and evaluation of the proposed methods

The proposed methods will be applied to examples from clinical trials identified through collaboration with clinical trials units. Examples may be clinical trials related to pain, Quality of Life, wellbeing (Warwick & Edinburgh mental well-being scale) or mental health (eg. depression/anxiety) measures. A possible application to meta-analysis of multiple ordinal outcomes is from a systematic review of physical activity on cigarette cravings. The dataset provides an example of having two ordinal outcomes of interest in multiple clinical trials.

Yinghui Wei (Associate Professor of Statistics, University of Plymouth) will supervise the student, co-supervised by Joanne Hosking (Senior Research Fellow, Medical Statistics, University of Plymouth). Further input will be sought from advisors Dr Dan Jackson (Statistical Science Director at AstraZeneca, Cambridge), and Professor Adrian Taylor (Professor in Health Services Research, University of Plymouth).

We will conduct outreach activities to help disseminate the results of our research widely. 

Candidates should hold:

·        A first degree in mathematics, statistics, computer science or a subject with a substantial component in quantitative methods.

·        An MSc or equivalent degree in a discipline with a strong component in quantitative methods is preferred.

HOW TO APPLY

You are applying for a PhD studentship from the MRC TMRP DTP. A list of potential projects and the application form is available online at:

http://www.methodologyhubs.mrc.ac.uk/about/tmrp-doctoral-training-partnership/

Please complete the form fully. Incomplete forms will not be considered. CVs will not be accepted for this scheme.

Please apply giving details for your first choice project. You can provide details of up to two other TMRP DTP projects you may be interested in at section B of the application form.

Before making an application, applicants should contact the project primary supervisor to find out more about the project and to discuss their interests in the research.

The deadline for applications is 4pm (GMT) 18 February 2022. Late applications will not be considered.

Completed application forms must be returned to: [Email Address Removed]

Informal enquiries may be made to [Email Address Removed]


Funding Notes

Studentships are funded by the Medical Research Council (MRC) for 3 years. Funding will cover tuition fees at the UK rate only, a Research Training and Support Grant (RTSG) and stipend (stipend to include London Weighting where appropriate). We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.
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