Analyzing data with clumping at zero - An example demonstration

This article demonstrates the use of two approaches to analyzing the relati onship of multiple covariates to an outcome which has a high proportion of zero values. One approach is to categorize the continuous outcome (includin g the zero category) and then fit a proportional odds model. Another approa ch is to use logistic regression to model the probability of a zero respons e and ordinary least squares linear regression to model the non-zero contin uous responses. The use of these two approaches was demonstrated using outc omes data on hours of care received from the Springfield Elder Project. A c rude linear model including both zero and non-zero values was also used for comparison. We conclude that the choice of approaches for analysis depends on the data. If the proportional odds assumption is valid, then it appears to be the method of choice; otherwise, the combination of logistic regress ion and a linear model is preferable. (C) 2000 Elsevier Science Inc. All ri ghts reserved.