Handling categories properly: a novel objective of clinical research

A major objective of clinical research is to study outcome effects in subgroups. Such effects generally have stepping functions that are not strictly linear. Analyzing stepping functions in linear models thus raises the risk of underestimating the effects. In the past few years, recoding subgroup properties from continuous variables into categorical ones has been recommended as a solution to the problem. The objectives of this study were to demonstrate from examples how recoding works and to show that stepping functions, if used as continuous variables, do not produce significant effects, whereas they produce very significant effects after recoding. In the first example, the effects on physical strength were assessed in 60 subjects of different races. A linear regression in SPSS with race as the independent and physical strength score as the dependent variable showed that race was not a significant predictor of physical strength. Using the process of recoding, the variable race into categorical dummy variables showed that compared with the presence of Hispanic race, the black and white races were significant positive predictors (P = 0.0001 and 0.004 respectively) and Asian race is a significant negative predictor (P = 0.050). In the second example, the effects of numbers of comedications on admissions to a hospital resulting from adverse drug effects were assessed. A logistic regression in SPSS with numbers of comedications as the independent variable showed that comedications was not a significant predictor of iatrogenic admission. Using again the process of recoding for categorical dummy variables showed that comedication was a very significant predictor of iatrogenic admission with P = 0.004. Categorical variables are currently rarely analyzed in a proper way. Mostly they are analyzed in the form of continuous variables. This approach does not always fit the data patterns causing negative results as demonstrated in the examples of this article. We recommend that such variables be recoded into categorical dummy variables