Erasmus MC

Summary A class of nonignorable models is presented for handling nonmonotone missingness in categorical longitudinal responses. This class of models includes the traditional selection models and shared parameter models. This allows us to perform a broader than usual sensitivity analysis. In particular, instead of considering variations to a chosen nonignorable model, we study sensitivity between different missing data frameworks. An appealing feature of the developed class is that parameters with a marginal interpretation are obtained, while algebraically simple models are considered. Specifically, marginalized mixed-effects models (Heagerty, 1999, Biometrics 55, 688–698) are used for the longitudinal process that model separately the marginal mean and the correlation structure. For the correlation structure, random effects are introduced and their distribution is modeled either parametrically or non-parametrically to avoid potential misspecifications.

Important long-term health problems have been described after severe paediatric trauma. The International Classification of Functioning (ICF) was developed as a universal framework to describe that health. We evaluated outcome in children after ‘severe’ trauma (defined as: hospitalised >48 h) by means of a questionnaire based on this ICF construct (IROS). Questionnaires were sent to children; one year after this trauma and to ‘control’ children without any previous ‘severe’ trauma. We created propensity score-matched pairs (n = 133) and evaluated differences in health perception. IROS characteristics were investigated by means of Item Response Theory models. We then estimated the health state of each individual based on his/her response pattern (factor score z01) and investigated the effect of selected covariates with simple linear regression. Significant odds ratios for differences between matched groups (p < 0.05) were observed for among others emotional problems, mobility, societal life and family burden, but not for chronic pain. Children in the trauma group showed, e.g. significant more physician (estimated relative risk R′ 1.7) and psychologist (R′ 3.5) visits. IROS primarily provides information from medium to high health burden levels and factor scores ranged from 0.41 (lowest) to 0.967 (highest burden). A significant impact on health burden could only be proven for the ‘state at discharge’ (p = 0.015), although there was a tendency towards worse factor scores for children that were older, had a higher Injury Severity Score or after traffic injury. In conclusion, we showed that the burden of health problems for children and families after severe trauma is still high and physical, as well as psychosocial in nature. The health state at discharge seems to predict long-term outcome, which might be of importance in view of, e.g. trajectory assistance. IROS may provide an improved scoring system to evaluate outcome after (paediatric) injury or critical illness.

We consider power and sample size calculations for randomized trials with a bounded outcome score (BOS) as primary response adjusted for a priori chosen covariates. We define BOS to be a random variable restricted to a finite interval. Typically, a BOS has a J- or U-shaped distribution hindering traditional parametric methods of analysis. When no adjustment for covariates is needed, a non-parametric test could be chosen. However, there is still a problem with calculating the power since the common location-shift alternative does not hold in general for a BOS. In this paper, we consider a parametric approach and assume that the observed BOS is a coarsened version of a true BOS, which has a logit-normal distribution in each treatment group allowing correction for covariates. A two-step procedure is used to calculate the power. Firstly, the power function is defined conditionally on the covariate values. Secondly, the marginal power is obtained by averaging the conditional power with respect to an assumed distribution for the covariates using Monte Carlo integration. A simulation study evaluates the performance of our method which is also applied to the ECASS-1 stroke study. Copyright © 2006 John Wiley & Sons, Ltd.

It has been demonstrated that aortic stiffness is an independent predictor of cardiovascular disease. We investigated whether this measure is of use in cardiovascular risk stratification in clinical practice for elderly subjects (mean age 71.5 years). Within the framework of the Rotterdam Study, we stratified subjects free of coronary heart disease (CHD) at baseline into categories of low (&lt; 10%), intermediate (10–20%) and high (&gt; 20%) 10-year risk of CHD based on Framingham risk factors. Within each risk category, we ...