PROPORTIONAL HAZARDS ( C o x ) REGRESSION is a powerful analytic tool for testing whe the r several factors (e.g., cigarette smoking, hyper tens ion) are independent ly related to the rate (over t ime) of a specific event (e.g., heart attack yes /no) . It can also be used to control for baseline differences be t ween groups in nonrandomized studies and randomized clinical trials (RCTs). The availabili ty of desktop compute r s and userfr iendly software has resul ted in a marked increase in the use of propor t iona l hazards regression by clinical researchers. However , most detai led reviews of the t echnique 1-5 cannot be unders tood by non-statisticians. In this article we begin wi th a rev iew of s impler types of survival analyses, highl ight ing the concepts of rate of ou t come and censored observations. Building on these two concepts , we descr ibe the statistical propert ies , under lying assumptions, interpretat ion, and applicat ion of propor t iona l hazards regression. Also we describe t ime-dependen t covariates, the use of proport ional hazards regression versus logistic regression, and other technical aspects of propor t iona l hazards regression. Finally, we illustrate the appl icat ions of this techn ique by reviewing 80 articles f rom the New England Journal o f Medicine and the Annals of Internal Medicine that used propor t iona l hazards regression dur ing 1984, 1987, and 1990. Our goal is to enable non-statisticians to in terpret these models and to provide guidelines for clinical researchers per forming this type of analysis.