For this analysis, we chose the proportional hazards (PH) model for conditional logistic regression. 42 The PH model is similar to regular conditional logistic regression but modified to allow for multiple and variable numbers of controls per case and specimens per subject. Dummy ''survival time'' to diagnosis was generated so that all samples for a given case had the same event time with corresponding controls censored at a later time. Proportional hazard regression was then used to study associations, reported as hazard ratios (HRs), between ''survival time'' and the risk factors. Because the PH assumption might not be true for all the data, but may be true among specific demographic subgroups, we performed stratified analysis on several of the matched variables.