Newborn Screening for the Detection of the TP53 R337H Variant and Surveillance for Early Diagnosis of Pediatric Adrenocortical Tumors: Lessons Learned and Way Forward.

The incidence of pediatric adrenocortical tumors (ACT) is high in southern Brazil due to the founder TP53 R337H variant. Neonatal screening/surveillance (NSS) for this variant resulted in early ACT detection and improved outcomes. The medical records of children with ACT who did not participate in newborn screening (non-NSS) were reviewed (2012-2018). We compared known prognostic factors between the NSS and non-NSS cohorts and estimated surveillance and treatment costs. Of the 16 non-NSS children with ACT carrying the R337H variant, the disease stages I, II, III, and IV were observed in five, five, one, and five children, respectively. The tumor weight ranged from 22 to 608 g. The 11 NSS children with ACT all had disease stage I and were alive. The median tumor weight, age of diagnosis, and interval between symptoms and diagnosis were 21 g, 1.9 years, and two weeks, respectively, for the NSS cohort and 210 g, 5.2 years, and 15 weeks, respectively, for the non-NSS cohort. The estimated surveillance/screening cost per year of life saved is US$623/patient. NSS is critical for improving the outcome of pediatric ACT in this region. Hence, we strongly advocate for the inclusion of R337H in the state-mandated universal screening and surveillance.

Multivariate generalized linear mixed models for continuous bounded outcomes: Analyzing the body fat percentage data.

We propose a multivariate regression model to handle multiple continuous bounded outcomes. We adopted the maximum likelihood approach for parameter estimation and inference. The model is specified by the product of univariate probability distributions and the correlation between the response variables is obtained through the correlation matrix of the random intercepts. For modeling continuous bounded variables on the interval (0,1) we considered the beta and unit gamma distributions. The main advantage of the proposed model is that we can easily combine different marginal distributions for the response variable vector. The computational implementation is performed using Template Model Builder, which combines the Laplace approximation with automatic differentiation. Therefore, the proposed approach allows us to estimate the model parameters quickly and efficiently. We conducted a simulation study to evaluate the computational implementation and the properties of the maximum likelihood estimators under different scenarios. Moreover, we investigate the impact of distribution misspecification in the proposed model. Our model was motivated by a data set with multiple continuous bounded outcomes, which refer to the body fat percentage measured at five regions of the body. Simulation studies and data analysis showed that the proposed model provides a general and rich framework to deal with multiple continuous bounded outcomes.