Multivariate longitudinal data frequently arise in biomedical applications, however their analysis, especially when adjusting for covariates, is often carried out one outcome at a time, or jointly using existing software in an adhoc fashion. A main challenge in the proper analysis of such data is the fact that the different outcomes are measured on different unknown scales. Methodology for handling the scale problem has been previously proposed for cross-sectional data, and here we extend it to the longitudinal setting. We consider modeling the longitudinal data using random effects, while leaving the joint distribution of the multiple outcomes unspecified. We propose an estimating equation together with an EM-type (ES) algorithm. The consistency and the asymptotic distribution of the parameter estimates are established. The method is evaluated using simulations, and applied to a longitudinal nutrition data set from a large dietary intervention trial on breast cancer survivors, the Women's Healthy Eating and Living (WHEL) Study. In some medical studies, a portion of the population that is being sampled may not be susceptible to the event. Cure-rate models are used to analyze the survival regression as well as the proportion "cured" individuals in the population. Analysis with these models has been limited to data featuring right-censoring only, however, for some data, such as pregnancy outcomes, the time to incident should not be measured from entry into the study. In these cases, the time of entry is a truncation time, and we propose a cure-rate model that accounts for this left-truncation. The primary challenge in developing a model for such data is that left- truncation incurs biased estimates of the cure-rate. To correct for this, we incorporate inverse probability weights (IPW) based on the estimated distribution of entry times into an augmented form of the complete data likelihood. From this estimating equations are derived, and we propose parameter estimation through an EM-type (ES) algorithm. The consistency and asymptotic distribution of the parameter estimates are established. The approach is illustrated through simulated data examples as well as pregnancy data from the Organization of Teratology Information Specialists (OTIS) where we consider the outcome spontaneous abortion (SAB)

Multivariate longitudinal data frequently arise in biomedical applications; however, their analyses are often performed one outcome at a time, or jointly using existing software in an ad hoc fashion. A main challenge in the proper analysis of such data is the fact that the different outcomes are measured on different unknown scales. Methodology for handling the scale problem has been previously proposed for cross-sectional data, and here we extend it to the longitudinal setting. We consider modeling the longitudinal data using random effects, while leaving the joint distribution of the multiple outcomes unspecified. We propose an estimating equation together with an expectation-maximization-type (expectation-substitution) algorithm. The consistency and the asymptotic distribution of the parameter estimates are established. The method is evaluated using extensive simulations and applied to a longitudinal nutrition data set from a large dietary intervention trial on breast cancer survivors, the Women's Healthy Eating and Living Study.

Background

Neurocognitive deficits in pediatric cancer survivors occur frequently; however, individual outcomes are unpredictable. We investigate clinical, genetic, and imaging predictors of neurocognition in pediatric cancer survivors, with a focus on survivors of central nervous system (CNS) tumors exposed to radiation.

Methods

One hundred eighteen patients with benign or malignant cancers (median diagnosis age: 7; 32% embryonal CNS tumors) were selected from an existing multi-institutional cohort (RadART Pro) if they had: 1) neurocognitive evaluation; 2) available DNA; 3) standard imaging. Utilizing RadART Pro, we collected clinical history, genomic sequencing, CNS imaging, and neurocognitive outcomes. We performed single nucleotide polymorphism (SNP) genotyping for candidate genes associated with neurocognition: COMT, BDNF, KIBRA, APOE, KLOTHO. Longitudinal neurocognitive testing were performed using validated computer-based CogState batteries. The imaging cohort was made of patients with available iron-sensitive (n = 28) and/or T2 FLAIR (n = 41) sequences. Cerebral microbleeds (CMB) were identified using a semi-automated algorithm. Volume of T2 FLAIR white matter lesions (WML) was measured using an automated method based on a convolutional neural network. Summary statistics were performed for patient characteristics, neurocognitive assessments, and imaging. Linear mixed effects and hierarchical models assessed patient characteristics and SNP relationship with neurocognition over time. Nested case-control analysis was performed to compare candidate gene carriers to non-carriers.

Results

CMB presence at baseline correlated with worse performance in 3 of 7 domains, including executive function. Higher baseline WML volumes correlated with worse performance in executive function and verbal learning. No candidate gene reliably predicted neurocognitive outcomes; however, APOE ϵ4 carriers trended toward worse neurocognitive function over time compared to other candidate genes and carried the highest odds of low neurocognitive performance across all domains (odds ratio 2.85, P=0.002). Hydrocephalus and seizures at diagnosis were the clinical characteristics most frequently associated with worse performance in neurocognitive domains (5 of 7 domains). Overall, executive function and verbal learning were the most frequently negatively impacted neurocognitive domains.

Conclusion

Presence of CMB, APOE ϵ4 carrier status, hydrocephalus, and seizures correlate with worse neurocognitive outcomes in pediatric cancer survivors, enriched with CNS tumors exposed to radiation. Ongoing research is underway to verify trends in larger cohorts.

BACKGROUND/OBJECTIVES: The primary objective was to assess pain catastrophizing and functional disability in pediatric patients with epidermolysis bullosa (EB) and their parents/guardians. Secondary objectives included examining relationships between pain catastrophizing, functional disability, and correlations with other factors (e.g., age, disease severity, and percent of body surface area (BSA) involved). METHODS: Patients with EB ages 8-16 and their parents/guardians who were English or Spanish speaking completed a one-time online survey. Parent measures included: demographics questionnaire, Pain Catastrophizing Scale-Parent (PCS), and Parent Functional Disability Inventory (FDI). Child measures included: PCS child and child FDI. Higher scores on both scales indicate higher levels of catastrophizing and functional disability. RESULTS: Of 31 children, the mean age was 11.47 years and the majority (70.97%) had dystrophic EB. Mean scores were: 35.84 = PCS parent; 34.58 = PCS child; 30.87 = parent FDI; 29.77 = child FDI. Total scores for PCS parent, parent FDI, and child FDI increased significantly with disease severity and percentage of involved BSA (p < .01 for all). Total scores for PCS child increased significantly with percent of EB skin involvement (p = .04) but not disease severity. Older children reported more functional disability than their parents and younger children (p = .02). CONCLUSIONS: Our results demonstrate significant positive correlations between negative thoughts related to pain and the experience of functional difficulties in patients with EB and their caregivers. Psychological, psychiatric, and/or behavioral interventions to help managing chronic pain may be effective for patients with EB.

Electronic health records (EHRs) offer opportunities for research and improvements in patient care. However, challenges exist in using data from EHRs due to the volume of information existing within clinical notes, which can be labor intensive and costly to transform into usable data with existing strategies. This case report details the collaborative development and implementation of the postencounter form (PEF) system into the EHR at the Road Home Program at Rush University Medical Center in Chicago, IL to address these concerns with limited burden to clinical workflows. The PEF system proved to be an effective tool with over 98% of all clinical encounters including a completed PEF within 5 months of implementation. In addition, the system has generated over 325,188 unique, readily-accessible data points in under 4 years of use. The PEF system has since been deployed to other settings demonstrating that the system may have broader clinical utility.

Electronic health records (EHRs) offer opportunities for research and improvements in patient care. However, challenges exist in using data from EHRs due to the volume of information existing within clinical notes, which can be labor intensive and costly to transform into usable data with existing strategies. This case report details the collaborative development and implementation of the postencounter form (PEF) system into the EHR at the Road Home Program at Rush University Medical Center in Chicago, IL to address these concerns with limited burden to clinical workflows. The PEF system proved to be an effective tool with over 98% of all clinical encounters including a completed PEF within 5 months of implementation. In addition, the system has generated over 325,188 unique, readily-accessible data points in under 4 years of use. The PEF system has since been deployed to other settings demonstrating that the system may have broader clinical utility.