Epidemic curves are an important component of the public health and global health toolbox. Learn more about creating and interpretting them.

9th January 2017 • comment

Become a Cochrane citizen scientist. Anyone can join their collaborative volunteer effort.

23rd December 2016 • comment

Around half of the clinical trials done on medicines we use today are not published; a tragic truth that needs to be changed.

24th October 2016 • comment

The objective of the study was to develop maternal, fetal, and neonatal composite outcomes relevant to the evaluation of diet and lifestyle interventions in pregnancy by individual patient data (IPD) meta-analysis.A two-generational Delphi survey involving members of the i–WIP collaborative network (26 members in 11 countries) was undertaken to prioritise the individual outcomes for their importance in clinical care. The final components of the composite outcomes were identified using pre-specified criteria. The study has identified the components of maternal, fetal, and neonatal composite outcomes required for the assessment of diet and lifestyle interventions in pregnancy by IPD meta-analysis.

19th February 2016 • comment
22nd September 2015 • comment

Professor Mike English explains how KEMRI-Wellcome are ''working with government to generate patient level data from a network of Kenyan hospitals as a platform for research'.

12th May 2015 • comment

In celebration of Global Health Trials' fifth birthday (May 11th 2015) Professor Trudie Lang, Principal Investigator of the programme, talks to us about why Global Health Trials was started, why people should share their experience, and what the future holds.

8th May 2015 • comment

There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, the author discusses the implications of these problems for the conduct and interpretation of research.

22nd April 2014 • comment

ADMIT Workshop in India

by Paritosh Malavyia, Raffaella Ravinetto, Shyam Sundar
6th May 2013 • comment

In this article, the authors illustrate five basic statistical concepts that can significantly impact the interpretation of the medical literature and its application to the care of patients, drawing examples from the vaccine literature: (i) consider clinical and statistical significance separately, (ii) evaluate absolute risks rather than relative risks, (iii) examine confidence intervals rather than p values, (iv) use caution when considering isolated significant p values in the setting of multiple testing, and (v) keep in mind that statistically nonsignificant results may not exclude clinically important benefits or harms.

26th March 2013 • comment