This is a basic introduction to interpreting odds ratios, confidence intervals and p-values and should help healthcare students begin to make sense of published research, which can initially be a daunting prospect.
The blog features a ‘concept check’ question as each new element is introduced. The scenario for this tutorial is based on a fictional parallel two arm randomised controlled trial of a new cholesterol lowering medication against a placebo.
The blog explains that an odds ratio (OR) is a relative measure of effect, which allows the comparison of the intervention group of a study relative to the comparison or placebo group. If the OR is > 1 the control is better than the intervention. If the OR is < 1 the intervention is better than the control.
The blog then explains that the confidence interval indicates the level of uncertainty around the measure of effect (precision of the effect estimate). It explains that confidence intervals are used because a study recruits only a small sample of the overall population so by having an upper and lower confidence limit we can infer that the true population effect lies between these two points. Most studies report the 95% confidence interval (95%CI).
It then explains that, P < 0.05 indicates a statistically significant difference between groups. P > 0.05 indicates there is not a statistically significant difference between groups. read the blog
Students 4 Best Evidence (S4BE) is a growing network of students from around the world, from school age to university, who are interested in learning more about evidence-based healthcare (EBH). The network is supported by the UK Cochrane Centre. In addition to the website, the S4BE has a Facebook group and Twitter feed. For more information, read Selena Ryan-Vigs blog which introduces Students 4 Best Evidence.