In randomized cancer screening trials, mortality rates for the screened group relative to those of the control group are not likely to be constant as a function of years from randomization due to the inherent lag between initiation of screening and any putative effects of screening on mortality. In this situation, a log rank test for differences in mortality between the randomization groups will not be optimal. Although optimality could potentially be recovered by use of a weighted log rank statistic, the optimal weights are difficult to specify a priori and the potential loss of power by use of poorly specified weights is great. We describe a likelihood ratio test with two degrees of freedom for use in this situation which is based on a fit of a weakly structured full model. Computation of an approximate significance level for this test is described and a large sample justification for this approximation is given. Size and power properties of the proposed statistic are compared to that of several other statistics in a small simulation study and the statistic is applied to data from the HIP Breast Cancer Screening Trial.