Many statistical analyses in ophthalmic and other clinical fields are concerned with describing relationships between one or more ‘predictors’ (explanatory or independent variables) and usually one outcome measure (response or dependent variable). Our earlier statistical notes make reference to the fact that statistical techniques often make assumptions about data.

One approach when assumptions are not adhered to is to use alternative tests which place fewer restrictions on the data – non-parametric or so-called distribution free methods.

Previous notes in this series have been concerned with the common situation in ophthalmic and other clinical fields of describing relationships between one or more ‘predictors’ (explanatory variables) and, usually, one outcome measure (response variable). A classic method used in deriving relationships between outcomes and predictors is linear regression analysis. Linear regression is a member of a family of techniques known as general linear models, which also include analysis of variance and analysis of covariance; the latter of which was covered in a previous Ophthalmic Statistics Note.

A key feature of all these models is that the outcome measure—for example, postoperative refractive prediction error or intraocular pressure—is continuous. While other notes in the series

Example 1: A study was conducted on 137 patients to identify risk...]]>