Phd Studentship: Predicting direction and magnitude of bias in clinical trials
- Employer
- Global Academy Jobs
- Location
- United Kingdom
- Closing date
- Apr 26, 2017
View more
- Sector
- Business and Finance, Science, Computer Science and IT
- Hours
- Full Time
- Organization Type
- University and College
- Jobseeker Type
- Academic (e.g. 'Lecturer')
Job Details
Aims
The project aims to lead to a better understanding of the results of clinical trials in the context of potential flaws, through prediction of the likely direction and magnitude of any bias. Methods of 'bias-adjustment' will be further developed and investigated, aiming to produce results that are less biased and less likely to be spuriously precise.
Methods
The primary purpose of the project is to develop methods for predicting the magnitude and direction of bias in a clinical trial. A key area of work will be the development of guidance for users of the newly developed (HTMR funded) tool for assessing risk of bias in RCTs (RoB 2.0) [available at http://www.riskofbias.info], which includes an optional facility of assessing the likely direction of any bias. An extensive library of examples will be collated from among our existing meta-epidemiological studies to gather insight into how large apparent biases might have arisen. The work will start by considering individual domains of bias and interactions between domains. Further re-analyses of meta-epidemiological data are expected to lead to insight into the extent to which the biases are additive.
A second area of work is to further develop methods for bias adjustment. The student will focus on the development of user-friendly integrated methods, based on a combination of expert opinion and empirical evidence. The student will develop a standardised way of converting qualitative RoB judgements (e.g. “high risk of bias in the direction of overestimation of treatment benefit”) into a numerical interquartile range estimate of bias on the ratio of odds ratio scale (e.g. “0.71-0.99”) and use this IQR as the opinion component for the COMBAT method of bias adjustment. We plan for the student to write software (e.g. a Stata command) for bias adjustments based on an integrated approach.
Details of supervision and training
Supervisory team have extensive expertise in methods relevant to this project and the completing student will develop skills and expertise in this area (e.g. clinical trial methods, epidemiology, evidence synthesis, Bayesian statistics, meta-analysis and decision theory). In addition, Jonathan Sterne, Nicky Welton and Rebecca Turner (MRC BSU Hub in Cambridge) will have an advisory role. Our School also offers a comprehensive and highly regarded programme of over 30 short courses in research methods, freely available to our PhD students.
Supervisors: Dr Savovic and Dr Jones
Company
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