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PubMed1995

A two-stage procedure for survival studies with surrogate endpoints.

A two-stage procedure for survival studies with surrogate endpoints is proposed. The objective of the procedure is to reduce the duration of a survival study relative to classical procedure. A surrogate endpoint is an event which is related to survival time and may occur earlier during follow up. In the first stage, all patients are followed to the primary endpoint in order to evaluate the strength of the relationship between the surrogate endpoint and survival. In the second stage, follow up is terminated on patients who reach the surrogate endpoint. Indirect inferences on the survival endpoint is now possible by virtue of the first stage analysis. We present methods for data collected in the two-stage procedure, for estimating the survivorship function, S(t), and for comparing two treatment groups using a non-parametric permutation test. The methods are applied to the results of a study of resected lung cancer.

Biometry↗
PubMed1995

ROC curves with multiple marker measurements.

Properties of receiver operating characteristic (ROC) curves are explored for markers that are measured repeatedly, through space or time, for each subject. The true underlying response, positive or negative, of each subject is assumed to be constant across marker measurements, and is determined from assessment of some "gold standard." A marker-based test is considered positive when at least one of a subject's marker values exceeds a designated cutoff. If subjects with positive and negative underlying responses differ in the number of marker measurements per subject, the ROC curve for a noninformative marker is bowed above or below the diagonal line representing the "null" curve for a marker that is measured just once per subject. If subjects with negative responses tend to have more measurements than those with positive responses, the ROC curve for even an informative marker may lie beneath the curve for the same marker measured once per subject. The form of the ROC curve for a marker used in this way is strongly influenced by the strength of the correlation of measurements within subjects.

Biomarkers↗