Simon,

All SPSS procedures can be done with or without weighting. To be precise, they can only be done WITH weighting, because SPSS always multiplies the given data by the value of a hidden variable called $WEIGHT, but this variable is by default set to 1 in all cases. When you issue the command WEIGHT BY [somevariable], $WEIGHT is given the values of that variable. SPSS computes significance levels and related values such as CI based on the sum of weighted cases, or more precisely the sum of weights. By default this equals the number of cases. The distortion of significance and CI measures occurs when you use so-called inflationary weights, i.e. weights that expand the total number of cases to population size, or more generally, where the sum of weights differs from the sum of cases. But you may choose a set of weights lacking this effect, so-called non-inflationary proportional weighting, when each case is augmented or reduced in weight but the sum of weighted cases is always n, the original sample size. In this list's archives there are some contributions of mine dealing in detail with this.

Notice that if your sampling probabilities are not equal for all cases, i.e. if yours is not a simple random sample, then obtaining your CI or significance probabilities from unweighted data would also produce distorted results. Differential sampling ratios for different cases may arise from two main features in sample design: stratification and clustering. Using reverse sampling ratios (N/n) as weights corrects for the effect of stratification but cannot correct for clustering. Even if you use non-inflationary weights, your results would be distorted from failing to account for stering. However, whenever you need differential weighting you are in the presence of complex samples, and in that case you should use the Complex Samples module of SPSS which gives the right estimates. The weighting facility in SPSS was originally intended only to expand frequencies to population scale, not to deal with inferential estimates. Hector

Personally, I prefer to use SAS when doing survival analysis. In case of SPSS weighting option, I think it's just a way to deal with different kind of sampling. I will be cautious in doing Cox regression when there are tied data points, several ways to deal with this issue, find out what SPSS does. Another issue is heterogeneity, if you have repeated measure, you definitely wan to deal with it.