Testing for center effects on survival and competing risks outcomes using pseudo-value regression

Abstract

In multi-center studies, the presence of a cluster effect leads to correlation among outcomes within a center and requires different techniques to handle such correlation. Testing for a cluster effect can serve as a pre-screening step to help guide the researcher towards the appropriate analysis. With time to event data, score tests have been proposed which test for the presence of a center effect on the hazard function. However, sometimes researchers are interested in directly modeling other quantities such as survival probabilities or cumulative incidence at a fixed time. We propose a test for the presence of a center effect acting directly on the quantity of interest using pseudo-value regression, and derive the asymptotic properties of our proposed test statistic. We examine the performance of our proposed test through simulation studies in both survival and competing risks settings. The proposed test may be more powerful than tests based on the hazard function in settings where the center effect is time-varying. We illustrate the test using a multicenter registry study of survival and competing risks outcomes after hematopoietic cell transplantation.

Keywords: Clustered time to event data; Cumulative incidence; Generalized linear mixed model; Pseudo-value regression.

Fig. 1

Simulation cenarios for a covariate value of 1. Solid line indicates the null situation, or the curves with a center effect of 0. Other curves represent alternative scenarios at a 1 SD center effect. Upper panel shows the survival curves, while lower panel shows the cumulative incidence curves. Vertical dotted lines represent the time points at which the proposed test is evaluated in the simulations

Fig. 2

Overall survival and treatment-related mortality for example. The first vertical dotted line represents the time point day 100; the second dotted line represents the time point 2 years