A New Sonographic Weight Estimation Formula for Small-for-Gestational-Age Fetuses

Boosting distributional copula regression

Capturing complex dependence structures between outcome variables (e.g., study endpoints) is of high relevance in contemporary biomedical data problems and medical research. Distributional copula regression provides a flexible tool to model the joint distribution of multiple outcome variables by disentangling the marginal response distributions and their dependence structure. In a regression setup, each parameter of the copula model, that is, the marginal distribution parameters and the copula dependence parameters, can be related to covariates via structured additive predictors. We propose a framework to fit distributional copula regression via model-based boosting, which is a modern estimation technique that incorporates useful features like an intrinsic variable selection mechanism, parameter shrinkage and the capability to fit regression models in high-dimensional data setting, that is, situations with more covariates than observations. Thus, model-based boosting does not only complement existing Bayesian and maximum-likelihood based estimation frameworks for this model class but rather enables unique intrinsic mechanisms that can be helpful in many applied problems. The performance of our boosting algorithm for copula regression models with continuous margins is evaluated in simulation studies that cover low- and high-dimensional data settings and situations with and without dependence between the responses. Moreover, distributional copula boosting is used to jointly analyze and predict the length and the weight of newborns conditional on sonographic measurements of the fetus before delivery together with other clinical variables.

Birth weight and gestational age distributions in a rural Kenyan population

BACKGROUND

With the increased availability of access to prenatal ultrasound in low/middle-income countries, there is opportunity to better characterize the association between fetal growth and birth weight across global settings. This is important, as fetal growth curves and birthweight charts are often used as proxy health indicators. As part of a randomized control trial, in which ultrasonography was utilized to establish accurate gestational age of pregnancies, we explored the association between gestational age and birthweight among a cohort in Western Kenya, then compared our results to data reported by the INTERGROWTH-21st study.

METHODS

This study was conducted in 8 geographical clusters across 3 counties in Western Kenya. Eligible subjects were nulliparous women carrying singleton pregnancies. An early ultrasound was performed between 6 + 0/7 and 13 + 6/7 weeks gestational age. At birth, infants were weighed on platform scales provided either by the study team (community births), or the Government of Kenya (public health facilities). The 10th, 25th, median, 75th, and 90th BW percentiles for 36 to 42 weeks gestation were determined; resulting percentile points were plotted, and curves determined using a cubic spline technique. A signed rank test was used to quantify the comparison of the percentiles generated in the rural Kenyan sample with those of the INTERGROWTH-21st study.

RESULTS

A total of 1291 infants (of 1408 pregnant women randomized) were included. Ninety-three infants did not have a measured birth weight. The majority of these were due to miscarriage (n = 49) or stillbirth (n = 27). No significant differences were found between subjects who were lost to follow-up. Signed rank comparisons of the observed median of the Western Kenya data at 10th, 50th, and 90th birthweight percentiles, as compared to medians reported in the INTERGROWTH-21st distributions, revealed close alignment between the two datasets, with significant differences at 36 and 37 weeks. Limitations of the current study include small sample size, and detection of potential digit preference bias.

CONCLUSIONS

A comparison of birthweight percentiles by gestational age estimation, among a sample of infants from rural Kenya, revealed slight differences as compared to those from the global population (INTERGROWTH-21st).

TRIAL REGISTRATION

This is a single site sub-study of data collected in conjunction with the Aspirin Supplementation for Pregnancy Indicated Risk Reduction In Nulliparas (ASPIRIN) Trial, which is listed at  ClinicalTrials.gov ,  NCT02409680 (07/04/2015).

An Update on Statistical Boosting in Biomedicine

Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine.