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Multiple regression models are increasingly being applied to clinical studies. Such models are powerful analytic tools that yield valid statistical inferences and make reliable predictions if various assumptions are satisfied. Two types of assumptions made by regression models concern the distribution of the response variable and the nature or shape of the relationship between the predictors and the response. This paper addresses the latter assumption by applying a direct and flexible approach, cubic spline functions, to two widely used models: the logistic regression model for binary responses and the Cox proportional hazards regression model for survival time data.

We studied 109 consecutive patients with variant angina who underwent cardiac catheterization over an 1 1 year period. All patients were followed for at least 6 months or until death, and 46 patients (22 treated medically and 24 treated surgically) were followed for 5 years or more. Of the 62 patients initially treated medically, 14 had nonfatal myocardial infarctions (12 within 1 month of catheterization) and 12 died (six within 6 months). Survival

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