A network for students interested in evidence-based health care

Bethan Copsey

I am a PhD student in medical statistics at the Centre for Statistics in Medicine at the University of Oxford. I have worked as a medical statistician in rehabilitation research, including randomised trials, implementation research, systematic reviews and meta-analyses. I am interested in clinical trial design and systematic review methodology and patient decision-making. I am passionate about explaining statistical concepts and disseminating research findings in clear messages that are accessible to everyone. Follow me on Twitter @bethan_copsey

Articles by Bethan:

Tutorials and Fundamentals

How certain is the evidence?

This is the thirty-fifth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.

The certainty of the evidence (the extent to which the research provides a good indication of the likely effects of treatments) can affect the treatment decisions people make. For example, someone might decide not to use or to pay for a treatment if the certainty of the evidence is low or very low.

Tutorials and Fundamentals

Are you very different from the people studied?

This is the thirty-second blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.

Are you very different from the people studied? Systematic reviews of studies that only include animals or a selected minority of people may not provide results that are relevant to most people. This may be misleading.

Tutorials and Fundamentals

Don’t confuse “no evidence of a difference” with “evidence of no difference”

This is the thirtieth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.

Systematic reviews sometimes conclude that there is “no evidence of a difference” when there is uncertainty about the difference between two treatments. This is often misinterpreted as meaning that there is “no difference” between the treatments compared.

Tutorials and Fundamentals

Average measures of effects can be misleading

This is the twenty-sixth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.

This blog explains that for outcomes measured on a scale (e.g. weight, or pain) the difference between the average in one treatment group and the average in a comparison group may not make it clear how many people experienced a big enough change (e.g. in weight or pain) for them to notice it, or that they would regard as important.

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