  Thomas' partial likelihood estimator of regression parameters is widely used
in the analysis of nested case-control data with Cox's model. This paper
proposes a new estimator of the regression parameters, which is consistent and
asymptotically normal. Its asymptotic variance is smaller than that of Thomas'
estimator away from the null. Unlike some other existing estimators, the
proposed estimator does not rely on any more data than strictly necessary for
Thomas' estimator and is easily computable from a closed form estimating
equation with a unique solution. The variance estimation is obtained as minus
the inverse of the derivative of the estimating function and therefore the
inference is easily available. A numerical example is provided in support of
the theory.
