The relationship between maternal-fetus attachment and perceived parental bonds in pregnant women: Considering a possible mediating role of psychological distress

Maternal-Fetal Attachment (MFA) delineates the emotional, cognitive, and behavioral aspects that mothers develop toward the unborn baby during pregnancy. The literature indicates that optimal attachment in pregnancy represents a protective factor for the mother-child attachment bond after birth and child development outcomes. To date, there are few studies that have investigated associated factors of MFA. This study sets out to explore the association between perceived parental bonds and maternal-fetal bonding in pregnant women, accounting for factors such as psychological distress, socio-demographic and obstetric characteristics.MethodsIn this cross-sectional study, 1,177 pregnant women an…

Quantifying treatment effects when flexibly modeling individual change in a nonlinear mixed effects model

A core task in analyzing randomized clinical trials based on longitudinal data is to find the best way to describe the change over time for each treatment arm. We review the implementation and estimation of a flexible piecewise Hierarchical Linear Model (HLM) to model change over time. The flexible piecewise HLM consists of two phases with differing rates of change. The breakpoints between these two phases, as well as the rates of change per phase are allowed to vary between treatment groups as well as individuals. While this approach may provide better model fit, how to quantify treatment diff erences over the longitudinal period is not clear. In this paper, we develop a procedure for summ…

Variable selection with unbiased estimation: the CDF penalty

We propose a new SCAD-type penalty in general regression models. The new penalty can be considered a competitor of the LASSO, SCAD or MCP penalties, as it guarantees sparse variable selection, i.e., null regression coefficient estimates, while attenuating bias for the non-null estimates. In this work, the method is discussed, and some comparisons are presented.

Adaptive P-splines via L1-type penalty in generalized additive models

Variable Selection with Quasi-Unbiased Estimation: the CDF Penalty

We propose a new non-convex penalty in linear regression models. The new penalty function can be considered a competitor of the LASSO, SCAD or MCP penalties, as it guarantees sparse variable selection while reducing bias for the non-null estimates. We introduce the methodology and present some comparisons among different approaches.

Adaptive smoothing spline using non-convex penalties

We propose a new adaptive penalty for smoothing via penalized splines. The new form of adaptive penalization is based on penalizing the differences of the coefficients of adjacent bases, using non-convex penalties. This makes possible to estimate curves with varying amounts of smoothness. Comparisons with respect to some competitors are presented.