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INTRODUCTION

This chapter shows you how to use the LOGISTIC procedure to analyze data in which many events occur at the same points in time. In Chapter 5, "Estimating Cox Regression Models with PROC PHREG," we looked at several different methods for handling tied data with PROC PHREG. There we saw that Breslow's method-the standard formula for partial likelihood estimation with tied data-is often a poor approximation when there are many ties. This problem was remedied by two exact methods, one that assumed that ties result from imprecise measurement and another that assumed that events really occur at the same (discrete) time. Unfortunately, both of these methods are computationally demanding for large data sets with many ties. We also looked at tied data in Chapter 4, "Estimating Parametric Regression Models with PROC LIFEREG," under the heading of interval censoring. While PROC LIFEREG is adept at estimating parametric models with interval censoring, it cannot incorporate time-dependent covariates.

The maximum likelihood methods described in this chapter do not suffer from these limitations. They do not rely on approximations, the computations are quite manageable even with large data sets, and they are particularly good at handling large numbers of time-dependent covariates. In addition, the methods make it easy to test hypotheses about the dependence of the hazard on time.


  

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