In the first session of this conference, we have heard three very different papers on three very interesting topics. I wish, however, to claim the prerogative granted to me by the organizers to comment in detail on the paper by Dr. Gore and to make only brief reference to the other papers. This approach is primarily motivated by the prior availability of Dr, Gore's manuscript and is no reflection on the other presentations.

Dr. Gore has provided an impressive survey of data analysis methods which have been employed for the study of a variety of diseases with long development times. In my comments, I hope to elaborate on some of the issues raised rather than to offer specific criticisms.

Time to event regression models have played a major role in the analysis of longitudinal data. Dr. Gore has placed some stress on the need for further consideration of the covariate codings in such models, in particular with respect to HIV disease. I have five comments on this issue.

(1) When using time dependent variables, it is almost essential that lagged covariates be used. It is unlikely that, for example, interest is directed towards the predictive role of CD4 counts at the time of AIDS diagnosis. In an analysis of the Toronto Sexual Contact Cohort (TSCC) data (Coates et al. 1992), we adopted the approach of lagging immunological markers by one year so that the developed models used covariates of the form X(t – 1) rather than X(t). The need for this is sometimes not recognized because covariates are not updated continuously and therefore an effective lagging takes place because the last available measurement is used in the regression models.