The usual approach to estimating regression parameters in the Cox regression model uses the partial likelihood. If the covariates are not time-dependent, the model can be stated in terms of the survival function, which allows one to derive a generalized likelihood containing both regression and survival curve parameters. It is shown that, in the absence of ties, an estimator results which is asymptotically equivalent to the partial likelihood estimator. A joint information matrix leads simply to standard errors for both regression and survival curve parameters which are asymptotically correct.