Opinion: on the importance of maintaining the functional form of explanatory variables

In medical research, continuous variables are often categorised into two or more groups before being included in the analysis; this practice often comes with a cost, such as loss of power in analysis, less reliable estimates, and can often leave residual confounding in the results. In this research report, we show this by way of estimates from a regression analysis looking at the association between acute kidney injury and post-operative mortality in a sample of 194 neonates who underwent the Norwood operation. Two models were developed, one using a continuous measure of renal function as the main explanatory variable and second using a categorised version of the same variable. A continuous measure of renal function is more likely to yield reliable estimates and also maintains more statistical power in the analysis to detect a relation between the exposure and outcome. It also reveals the true biological relationship between the exposure and outcome. Categorising a continuous variable may not only miss an important message, it can also get it wrong. Additionally, given a non-linear relationship is commonly encountered between the exposure and outcome variable, investigators are advised to retain a predictor with a linear term only when supported by data. All of this is particularly important in small data sets which account for the majority of clinical research studies.