Relationship between damage and mortality in juvenile-onset systemic lupus erythematosus: Cluster analyses in a large cohort from the Spanish Society of Rheumatology Lupus Registry (RELESSER)

Objectives: To identify patterns (clusters) of damage manifestation within a large cohort of juvenile SLE (jSLE) patients and evaluate their possible association with mortality. Methods: This is a multicentre, descriptive, cross-sectional study of a cohort of 345 jSLE patients from the Spanish Society of Rheumatology Lupus Registry. Organ damage was ascertained using the Systemic Lupus International Collaborating Clinics Damage Index. Using cluster analysis, groups of patients with similar patterns of damage manifestation were identified and compared. Results: Mean age (years) ± S.D. at diagnosis was 14.2 ± 2.89; 88.7% were female and 93.4% were Caucasian. Mean SLICC/ACR DI ± S.D. was 1.27 ± 1.63. A total of 12 (3.5%) patients died. Three damage clusters were identified: Cluster 1 (72.7% of patients) presented a lower number of individuals with damage (22.3% vs. 100% in Clusters 2 and 3, P < 0.001); Cluster 2 (14.5% of patients) was characterized by renal damage in 60% of patients, significantly more than Clusters 1 and 3 (P < 0.001), in addition to increased more ocular, cardiovascular and gonadal damage; Cluster 3 (12.7%) was the only group with musculoskeletal damage (100%), significantly higher than in Clusters 1 and 2 (P < 0.001). The overall mortality rate in Cluster 2 was 2.2 times higher than that in Cluster 3 and 5 times higher than that in Cluster 1 (P < 0.017 for both comparisons). Conclusions: In a large cohort of jSLE patients, renal and musculoskeletal damage manifestations were the two dominant forms of damage by which patients were sorted into clinically meaningful clusters. We found two clusters of jSLE with important clinical damage that were associated with higher rates of mortality, especially for the cluster of patients with predominant renal damage. Physicians should be particularly vigilant to the early prevention of damage in this subset of jSLE patients with kidney involvement

Estimation in a Competing Risks Proportional Hazards Model Under Length-biased Sampling With Censoring

International audienceWhat population does the sample represent? The answer to this question is of crucial importance when estimating a survivor function in duration studies. As is well-known, in a stationary population, survival data obtained from a cross-sectional sample taken from the population at time $t_0$ represents not the target density $f(t)$ but its length-biased version proportional to $tf(t)$, for $t>0$. The problem of estimating survivor function from such length-biased samples becomes more complex, and interesting, in presence of competing risks and censoring. This paper lays out a sampling scheme related to a mixed Poisson process and develops nonparametric estimators of the survivor function of the target population assuming that the two independent competing risks have proportional hazards. Two cases are considered: with and without independent consoring before length biased sampling. In each case, the weak convergence of the process generated by the proposed estimator is proved. A well-known study of the duration in power for political leaders is used to illustrate our results. Finally, a simulation study is carried out in order to assess the finite sample behaviour of our estimators

Nonparametric estimation of a conditional distribution from length-biased data

Cross-sectional sampling, Left-truncation, Regression, Unemployment duration,

Estimation of the bivariate distribution function for censored gap times

First published online: 12 Dec 2014In many medical studies, patients may experience several events during follow-up. The times between consecutive events (gap times) are often of interest and lead to problems that have received much attention recently. In this work we consider the estimation of the bivariate distribution function for censored gap times. Some related problems such as the estimation of the marginal distribution of the second gap time and the conditional distribution are also discussed. In this paper we introduce a nonparametric estimator of the bivariate distribution function based on Bayes' theorem and Kaplan-Meier survival function and explore the behavior of the four estimators through simulations. Real data illustration is included.Ana Moreira acknowledges financial support by grant SFRH/BD/62284/2009 of the Portuguese Ministry of Science, Technology and Higher Education. This research was also financed by FEDER Funds through Programa Operacional Factores de Competitividade COMPETE and by Portuguese Funds through FCT - Fundação para a Ciência e a Tecnologia, within Projects PTDC/MAT/104879 and PEst-OE/MAT/UI0013/2014