Our PhD/MPhil Biostatistics programme enables you to undertake a research project that will improve understanding of Biostatistics.
Underpinning all our work in collaborative clinical and healthcare research is a vibrant programme of methodological research. Our main research themes within biostatistics investigate design and analysis of both randomised trials and epidemiological studies. These include inference from large e-health datasets and statistical modelling of high-dimensional genomic/biomarker data and clustered or correlated outcomes. More information on the r...
Our PhD/MPhil Biostatistics programme enables you to undertake a research project that will improve understanding of Biostatistics.<br/><br/>Underpinning all our work in collaborative clinical and healthcare research is a vibrant programme of methodological research. Our main research themes within biostatistics investigate design and analysis of both randomised trials and epidemiological studies. These include inference from large e-health datasets and statistical modelling of high-dimensional genomic/biomarker data and clustered or correlated outcomes. More information on the research that we conduct at The University of Manchester can be found on the Biostatistics research pages http://research.bmh.manchester.ac.uk/biostatistics/research .<br/><br/>In particular, our research covers:<br/><br/><br/>- Modern statistical approaches to causal inference<br/><br/><br/>- The design and analysis of efficient and adaptive randomised controlled trials<br/><br/><br/>- Meta-analysis<br/><br/><br/>- Inference from large e-health datasets such as CPRD and the HFEA<br/><br/><br/>- Applications of complex statistical modelling, including the use of Bayesian methods, to high-dimensional <br/><br/><br/> genomic/biomarker data, neuroscience and clustered or correlated outcomes arising in assisted reproduction research;<br/>- Computationally intensive statistical methods;<br/><br/><br/>- Genetic epidemiology;<br/><br/><br/>- Prediction/prognostic modelling<br/>
12 options available
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Course Details
Information
Study Mode
Part-time
Duration
48 Months
Start Date
01/2026
Campus
Main Site
Varied
Application deadline
Provider Details
Codes/info
Course Code
Unknown
Institution Code
M20
Points of Entry
Unknown
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