Infant sibling studies have been at the vanguard of autism spectrum disorders (ASD) research over the past decade, providing important new knowledge about the earliest emerging signs of ASD and expanding our understanding of the developmental course of this complex disorder. Studies focused on siblings of children with ASD also have unrealized potential for contributing to ASD etiologic research. Moving targeted time of enrollment back from infancy toward conception creates tremendous opportunities for optimally studying risk factors and risk biomarkers during the pre-, peri- and neonatal periods. By doing so, a traditional sibling study, which already incorporates close developmental follow-up of at-risk infants through the third year of life, is essentially reconfigured as an enriched-risk pregnancy cohort study. This review considers the enriched-risk pregnancy cohort approach of studying infant siblings in the context of current thinking on ASD etiologic mechanisms. It then discusses the key features of this approach and provides a description of the design and implementation strategy of one major ASD enriched-risk pregnancy cohort study: the Early Autism Risk Longitudinal Investigation (EARLI).

SummaryBackground and objectives Infection and cardiovascular disease are leading causes of hospitalization and death in patients on dialysis. The objective of this study was to determine whether an infection-related hospitalization increased the short-term risk of a cardiovascular event in older patients on dialysis.Design, setting, participants, & measurements With use of the United States Renal Data System, patients aged 65 to 100 years who started dialysis between January 1, 2000, and December 31, 2002, were examined. All hospitalizations were examined from study entry until time of transplant, death, or December 31, 2004. All discharge diagnoses were examined to determine if an infection occurred during hospitalization. Only principal discharge diagnoses were examined to ascertain cardiovascular events of interest. We used the self-controlled case-series method to estimate the relative incidence of a cardiovascular event within 90 days after an infection-related hospitalization as compared with other times not within 90 days of such a hospitalization.Results A total of 16,874 patients had at least one cardiovascular event and were included in the self-controlled case-series analysis. The risk of a cardiovascular event was increased by 25% in the first 30 days after an infection and was overall increased 18% in the 90 days after an infection-related hospitalization relative to control periods. ConclusionsThe first 90 days, and in particular the first 30 days, after an infection-related hospitalization is a high-risk period for cardiovascular events and may be an important timeframe for cardiovascular risk reduction, monitoring, and intervention in older patients on dialysis.

Infection and cardiovascular disease are leading causes of hospitalization and death in older patients on dialysis. Our recent work found an increase in the relative incidence of cardiovascular outcomes during the ~ 30 days after infection-related hospitalizations using the case series model, which adjusts for measured and unmeasured baseline confounders. However, a major challenge in modeling/assessing the infection-cardiovascular risk hypothesis is that the exact time of infection, or more generally “exposure,” onsets cannot be ascertained based on hospitalization data. Only imprecise markers of the timing of infection onsets are available. Although there is a large literature on measurement error in the predictors in regression modeling, to date there is no work on measurement error on the timing of a time-varying exposure to our knowledge. Thus, we propose a new method, the measurement error case series (MECS) models, to account for measurement error in time-varying exposure onsets. We characterized the general nature of bias resulting from estimation that ignores measurement error and proposed a bias-corrected estimation for the MECS models. We examined in detail the accuracy of the proposed method to estimate the relative incidence. Hospitalization data from United States Renal Data System, which captures nearly all (> 99%) patients with end-stage renal disease in the U.S. over time, is used to illustrate the proposed method. The results suggest that the estimate of the cardiovascular incidence following the 30 days after infections, a period where acute effects of infection on vascular endothelium may be most pronounced, is substantially attenuated in the presence of infection onset measurement error.

Summary We propose novel estimation approaches for generalized varying coefficient models that are tailored for unsynchronized, irregular and infrequent longitudinal designs/data. Unsynchronized longitudinal data refers to the time-dependent response and covariate measurements for each individual measured at distinct time points. The proposed methods are motivated by data from the Comprehensive Dialysis Study (CDS). We model the potential age-varying association between infection-related hospitalization status and the inflammatory marker, C-reactive protein (CRP), within the first two years from initiation of dialysis. Traditional longitudinal modeling cannot directly be applied to unsynchronized data and no method exists to estimate time- or age-varying effects for generalized outcomes (e.g., binary or count data) to date. In addition, through the analysis of the CDS data and simulation studies, we show that preprocessing steps, such as binning, needed to synchronize data to apply traditional modeling can lead to significant loss of information in this context. In contrast, the proposed approaches discard no observation; they exploit the fact that although there is little information in a single subject trajectory due to irregularity and infrequency, the moments of the underlying processes can be accurately and efficiently recovered by pooling information from all subjects using functional data analysis. Subject-specific mean response trajectory predictions are derived and finite sample properties of the estimators are studied.

In contrast to prior published work, we found no difference in rates of both invasive and noninvasive staging between white and non-white patients. However, non-white patients-particularly blacks-were less likely to receive surgery. The reason for the apparent difference in surgical rates could not be explained by the variables we evaluated. Thus, other factors such as personal preference or access to care require further investigation.