DESCRIPTION: Clinical trials are frequently constructed with surrogate
endpoints, for practical or cost considerations. Often such trials are used
to make implicit inferences about the effect of treatment on a more critical
endpoint, such as survival. This translation is often made by medical
investigators if the surrogate is known to be correlated with the true
endpoint, but without reference to the strength of this association.
The general purposes of this proposal are to examine,in a quantitative way,
the extent to which such inferences are justifiable, and to develop and
evaluate new statistical methods that account for the dependencies in a more
complete way. Specifically, we will examine the mathematical relationships
between the parameters characterizing the associations between each of the
variables, focusing on odds ratios for binary data and proportional hazards
models for survival-type data. Using these results we will develop a
conceptual strategy for interpreting the results of trials with surrogate
endpoints. We will develop a method for optimizing the sample size for
pilot screening trials employing surrogate endpoints, useful in
chemoprevention research. We will study the properties of estimated
likelihood methods which may have utility in conventional trials where the
surrogate endpoints are used to augment the limited information on the true
end-point. Finally the applicants will apply the methods to numerous
relevant datasets available at their Center.
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