Tips on overlapping confidence intervals and univariate linear models.

In randomised controlled trials, an overlap of confidence intervals is often cited as evidence of ‘no statistically significant difference’ between intervention groups. This paper illustrates the limitations of this strategy and compares different univariate linear regression models with baseline and follow-up response measures. The researchers also demonstrate that using ‘change in response’ or exit score as a function of the baseline response in clinical studies leads to the same results. Further, using a model that includes baseline response as covariate leads to more precise estimates. The implications for future trials are discussed. Introduction Due to the inherent difficulties and costs associated with obtaining a census, researchers use a sample to estimate an unknown population quantity. However, as errors in estimation can occur, it is important to describe a range of values in which this unknown quantity is likely to be found. This is achieved by using confidence intervals, which allows investigators to assess the precision and usefulness of estimates of unknown quantities. linear models regression models analysis of variance experimental design overlapping confidence intervals nursing intervention ▲ ▲