Search

Background: Diagnosis and treatment for individuals with brain tumors during childhood involves sequelae, including abnormal weight gain. This symptom is commonly clustered with fatigue and increased risk for cardiovascular disease. Children's Oncology Group recommendations include annual surveillance of body mass index (BMI) and cardiometabolic comorbidities; however, there has been little emphasis on individualized screening early in survivorship. Purpose: The primary purpose of this paper is to describe the findings of abnormal weight gain and its correlates in a sample of young childhood brain tumor survivors during early survivorship. Design and Methods: This cross‐sectional multi‐site study included brain tumor survivors of ages 8–12 years who were less than 6 years posttreatment. Convenience sampling from two pediatric cancer centers in the southern United States was utilized. Data collected included BMI, parent report of sleep, and child report of fatigue and stress. Results: The sample (N = 21) consisted of children who had received chemotherapy, radiation treatment, and surgery for childhood brain tumor. BMI in overweight and obese categories exceeded normative samples with 38% at or above the 85th percentile. There were clinically significant relationships with fatigue, stress about weight, tumor location, cranial radiation, chemotherapy, and recurrence Practice Implications: Screening for abnormal weight gain and related factors, such as fatigue should begin early in survivorship after childhood brain tumor treatment completion with the aim of health promotion and disease prevention. Adiposity measurement techniques should be utilized in future clinical and research settings to improve assessment of cardiometabolic risk. [ABSTRACT FROM AUTHOR]

Missing data is a common occurrence in epidemiologic research. In this paper, 3 data sets with induced missing values from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are provided as examples of prototypical epidemiologic studies with missing data. Our goal was to estimate the association of maternal smoking behavior with spontaneous abortion while adjusting for numerous confounders. At the same time, we did not necessarily wish to evaluate the joint distribution among potentially unobserved covariates, which is seldom the subject of substantive scientific interest. The inverse probability weighting (IPW) approach preserves the semiparametric structure of the underlying model of substantive interest and clearly separates the model of substantive interest from the model used to account for the missing data. However, IPW often will not result in valid inference if the missing-data pattern is nonmonotone, even if the data are missing at random. We describe a recently proposed approach to modeling nonmonotone missing-data mechanisms under missingness at random to use in constructing the weights in IPW complete-case estimation, and we illustrate the approach using 3 data sets described in a companion article (AmJ Epidemiol. 2018;187(3):568-575). [ABSTRACT FROM AUTHOR]