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Comparison of statistical methods for the analysis of patient-reported outcomes in Randomised Controlled Trials

Project Description

Patient reported outcome measures (PROMs), such as the SF-36, are now frequently used in trials as outcomes and increasingly in routine clinical practice. Investigators are now asking statisticians for advice on how to plan and analyse studies using such outcomes. Therefore, the analysis of studies using PROMs has become a methodological issue of interest to researchers in the area.

PROMs, like the SF-36, are usual measured on an ordinal scale and this means they tend to generate data that have discrete, bounded and skewed distributions. Thus, standard methods of analysis, e.g. linear regression, that assume Normality and constant variance may not be appropriate and may result in imprecise estimates; wider confidence intervals and larger Type I and Type II errors than alternative techniques.

Data are available from over a dozen RCTs and observational studies that have used PROMS as the primary outcome that the supervisors have been involved with.

The proposed research plan would be to undertake a review of the statistical literature on methods for analysing patient reported outcome measures, particularly when used in RCTs. This would be followed by an audit of recently published RCTs (that have used PROMS) to describe what statistical methods are commonly used to analyse patient reported outcomes. The statistical methods, identified from the review and audit, and their effect on the results and conclusions will be compared using the data from several RCTs with PROMs. Finally, if time allows the project would then involve computer simulation to compare the power (and significance) of the different statistical methods/models, to detect a treatment effect (under the null and alternative hypothesis), with the view to developing guidance on which statistical methods to use to analyse PROs in randomized controlled trials.

How to apply:
Follow the link in this advert to our online application form.
Select a start date of October 2019 and ensure that you complete the scholarships section of the application form by the deadline.
Please ensure that the project title and supervisors’ names are included in the application.

Funding Notes

This project is being advertised for the China Scholarship Council Award, further details can be found here: View Website

The deadline for applications is 5pm on the 23rd January.

Candidates should possess either one of the following:
A First class honours degree in mathematics or statistics, or
An Upper Second class honours degree in mathematics or statistics and a Masters qualification in statistics.

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