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Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class Model
Author(s) -
JacqminGadda Hélène,
ProustLima Cécile,
Taylor Jeremy M.G.,
Commenges Daniel
Publication year - 2010
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2009.01234.x
Subject(s) - latent class model , event (particle physics) , conditional independence , outcome (game theory) , random effects model , econometrics , score test , statistics , local independence , population , independence (probability theory) , latent variable model , statistical hypothesis testing , mathematics , demography , medicine , meta analysis , physics , mathematical economics , quantum mechanics , sociology
Summary Latent class models have been recently developed for the joint analysis of a longitudinal quantitative outcome and a time to event. These models assume that the population is divided in G latent classes characterized by different risk functions for the event, and different profiles of evolution for the markers that are described by a mixed model for each class. However, the key assumption of conditional independence between the marker and the event given the latent classes is difficult to evaluate because the latent classes are not observed. Using a joint model with latent classes and shared random effects, we propose a score test for the null hypothesis of independence between the marker and the outcome given the latent classes versus the alternative hypothesis that the risk of event depends on one or several random effects from the mixed model in addition to the latent classes. A simulation study was performed to compare the behavior of the score test to other previously proposed tests, including situations where the alternative hypothesis or the baseline risk function are misspecified. In all the investigated situations, the score test was the most powerful. The methodology was applied to develop a prognostic model for recurrence of prostate cancer given the evolution of prostate‐specific antigen in a cohort of patients treated by radiation therapy.