Close

Royal Free Hospital Medical Library Blog

Home

## Archive for the 'Top tips' Category

### Critical appraisal top tips: Understanding confidence intervals

Jennifer RFord2 June 2017

Have you ever wanted to feel more confident about interpreting the findings of clinical trials and reviews? We’re here to help with a new series of blog posts aiming to help you get to grips with understanding and interpreting biomedical research.

This month’s post focuses on 95% confidence intervals.

## What is a confidence interval?

Confidence intervals can be calculated for most kinds of statistical test, e.g. p values, t tests, measures of risk and numbers needed to treat. They provide an indication of the strength of evidence e.g. for the effectiveness of a treatment. Confidence intervals define a range of values within which you can expect the statistical results of a study to fall.

If a study is carried out once it will obviously end with one set of results, and one set of statistics calculated from those results, e.g. a p value. If that study were to be repeated over and over again, you’d expect the results to be slightly different every time –if only because there will always be some variation.

So, you might find a different p value if a study were repeated. Statistics such as p values are often used to try and determine whether an intervention has a statistically significant effect, and it’s possible that one repeat of a study would find a non-statistically significant p value, even if the original study findings had been statistically significant. Confidence intervals can help you to understand how likely this is to happen.

## What do confidence intervals mean?

Confidence intervals provide a range of values within which you would expect the results of a study to fall, were it to be repeated. Although the same trial repeated hundreds of times would not yield the same results every time, on average the results would be within a certain range.  95% confidence intervals are often calculated as standard. A 95% confidence interval means that there is a 95% chance that the true size of the intervention effect will lie within this range – so if a study were repeated there is a 95% chance that it would produce a result falling somewhere within the range defined by the confidence interval.

## Find out more

In the next of this regular series of posts on interpreting medical statistics we’ll look at another common statistical technique, odds ratios. In the meantime the Library provides you with access to range of resources and papers on confidence intervals and other statistics. A short selection of further reading is given below.

Davies, Huw T.O., Crombie, Iain K. (2009) What are confidence intervals and p-values? Hayward Medical Communications

Sedgwick, Philip (2014). Understanding confidence intervals BMJ 2014; 349 :g6051

### Don’t forget, we also run regular training sessions on critical appraisal for study types such as RCTs, systematic reviews and qualitative research.

Sources

Greenhalgh, Trisha (2014). How to read a paper : The basics of evidence-based medicine (5th ed.). Chichester: Wiley ; BMJ Books.

### Searching top tips – Choosing your databases

12 May 2017

This blog post is the first of our Searching top tips series. These tips are especially important for systematic and comprehensive searching. This first post focuses on choosing which databases to search.

In order to undertake a comprehensive search you will need to search more than one database. There are many databases available so you should familiarise yourself with those most appropriate to your research topic. Ask a librarian if you’re unsure!

### Core biomedical databases – Medline/PubMed and Embase

For biomedical topics, at the very least you should search Medline and Embase. These databases are both large biomedical databases.  In most cases you will not need to search PubMed as well, as the majority of PubMed records are also within Medline. You could however consider a supplementary search of PubMed following the method recommended by Duffy et al. (2016).

### Specialist databases to be searched depending on topic

This is not an exhaustive list so do speak to a librarian, especially for subjects which broach non-health and biomedical areas such as bioengineering and technology.

– CINAHL (Cumulative Index of Nursing and Allied Health Literature) via EBSCO – a good database for subjects related to nursing, allied health, public health and complimentary medicine.

– AMED (Allied and Complimentary Medicine) (Ovid) – a small database covering allied health and complimentary medicine. Also good for palliative care.

– Cochrane Central Register of Controlled Trials (CENTRAL) – Contains randomised and quasi-randomised controlled trials, so highly recommended if you are looking for this study design.

– PsycINFO (Ovid) –psychological and behavioural sciences

– Maternity and Infant Care –midwifery, pregnancy and birth, postnatal care and infant feeding, including care of the infant from 0-2 years

– ASSIA (Applied Social Sciences Index and Abstracts) – covers social and psychosocial topics, as well as literature around social services such as social work and prison services

– Social Care Online –social care and social work

– HMIC – UK focused health and social care management information. A rich source of grey literature.

### Multidisciplinary/citation reference searching databases

There are two multidisciplinary databases (Web of Science Core Collection and Scopus) worth considering for topics benefiting from a broad search crossing the scientific (e.g. engineering and technology), social sciences and arts and humanities.

Web of Science Core Collection and Scopus also allow you to do something called cited reference searching, which is a supplementary search technique whereby you can identify articles which have cited a known relevant article in the hope that papers referring a relevant study may also be relevant.

### Grey and unpublished literature

If you are undertaking a systematic review or a comprehensive search then it’s important to search for unpublished studies and grey literature (studies not commercially published). Searching for unpublished clinical trials can help to reduce the problem of publication bias.

To identify registered clinical trials, we recommend searching the WHO International Clinical Trials Registry Platform. Grey literature can exist in many formats, such as reports from professional organisations or research groups, or even as a PhD thesis. Conference abstracts are another source of grey literature that can be searched for in some databases and may report on research that is never fully written up in an academic journal. Get in touch with one of our librarians if you’d like advice about potential sources of grey literature.

Duffy S, de Kock S, Misso K, Noake C, Ross J, Stirk L. (2016) Supplementary searches of PubMed to improve currency of MEDLINE and MEDLINE In-Process searches via Ovid. Journal of the Medical Library Association, 104(4):309-312. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5079494/

Higgins JPT, Green S (2011) 6.2 Sources to search. In Higgins JPT, Green S (eds.) Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration. Available at: http://handbook.cochrane.org/chapter_6/6_2_sources_to_search.htm

### Critical appraisal top tips: Understanding and interpreting P values

Jennifer RFord10 April 2017

Have you ever wanted to feel more confident about interpreting the findings of clinical trials and reviews? We’re here to help with the first in a new series of blog posts aiming to help you get to grips with understanding and interpreting biomedical research.

We’re starting this series with a post on a commonly used statistical technique – the p value. You’ve probably come across p values before as they’re often used to present results of research studies. If you’ve ever wanted to better understand what they mean then read on…

### What is a P value?

A P value is a statistic that is calculated in order to show how likely it is that the result of a research study has arisen by chance alone (rather than as a result of the effect of an intervention being tested).

### What do P values mean?

The key to interpreting p values is to remember that a p value of less than 0.05 is statistically significant. The threshold of 0.05 is a standard threshold used across disciplines.

A p value of less than 0.05 simply means that there is a less than 1 in 20 chance that the result of a study is due to pure random chance.

So a p value of 0.03 would be a statistically significant result, while a p value of 0.07 would not. For example, in a study such as an RCT, where measures for two groups of participants (i.e. an intervention and a control group) are being compared, a p value of less than 0.05 suggests that there is a statistically significant difference between these two groups. As the difference between the two groups passes the threshold of statistical significance, this is taken to indicate that the difference could potentially be the result of the intervention, rather than being due to chance alone.

### Find out more

In the next of this regular series of posts on interpreting medical statistics we’ll look at another common statistical technique, often used alongside p values – confidence intervals. Don’t forget, we also run regular training sessions on critical appraisal for study types such as RCTs, systematic reviews and qualitative research.

In the meantime, if you want to find out more about how to interpret and understand statistics such as p values, don’t forget that a range of resources are available from the Medical Library, including:

Greenhalgh, Trisha (2014). How to read a paper : The basics of evidence-based medicine (5th ed.). Chichester: Wiley ; BMJ Books.

Kirkwood, Betty R, Kirkwood, Betty R., author, & Sterne, Jonathan A. C. (2003). Essential medical statistics (2nd ed.). Malden, Mass. ; Oxford: Blackwell Science.

Peat, J., & Barton, Belinda. (2005). Medical statistics : A guide to data analysis and critical appraisal. Malden, Mass. ; Oxford: Blackwell Pub.

Sources

Greenhalgh, Trisha (2014). How to read a paper : The basics of evidence-based medicine (5th ed.). Chichester: Wiley ; BMJ Books.