Regression models in clinical studies: determining relationships between predictors and response.

Harrell FE, Lee KL, Pollock BG
J Natl Cancer Inst. 1988 80 (15): 1198-202

PMID: 3047407 · DOI:10.1093/jnci/80.15.1198

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.

MeSH Terms (5)

Humans Models, Biological Mortality Regression Analysis Software

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