Joint analysis of multiple longitudinal outcomes: Application of a latent class model

We address the problem of joint analysis of more than one series of longitudinal measurements. The typical way of approaching this problem is as it joint mixed effects model For the two outcomes. Apart from the large number of parameters needed to specify such it model, perhaps the biggest drawback of this approach is the difficulty in interpreting the results Of the model, particularly when the main interest is in the relation between the two longitudinal Outcomes. Here we propose an alternative approach to this problem. We use a latent class joint model for the longitudinal outcomes ill order to reduce the dimensionality of the problem. We then use a two-stage estimation procedure to estimate the parameters in this model. fit the first stage, the latent classes, their probabilities and the mean and covariance Structure are estimated based oil the longitudinal data of the first Outcome. In the second stage, We Study the relation between the latent classes and patient characteristics and the Other outcome(s). We apply the method to data from 195 consecutive lung cancer patients ill two Outpatient Clinics of lung diseases ill The Hague, and we study the relation between denial and longitudinal health measure. Our approach clearly revealed an interesting phenomenon: although no difference between Classes Could be detected for objective Measures Of health, patients in classes representing higher levels of denial consistently scored objective significantly higher in Subjective measures of health. Copyright (C) 2008 John Wiley & Sons, Ltd.