Harmonizing measurements: establishing a common metric via shared items across instruments.

Background: The proliferation of instruments that define instrument-specific metrics impedes progress in comparative assessment across populations. This paper explores a method to extract a common metric from related but different instruments and transform the original measurements into scores with a standard unit of measurement.

Methods: Existing data from four assessment instruments of child development, collected from three different samples of children, were used to create "equate clusters" of items that measure the same behaviour in (slightly) different ways.

Stability of neurodevelopmental trajectories in moderately late and early preterm children born 15 years apart.

Background: Neurodevelopmental trajectories of preterm children may have changed due to changes in care and in society. We aimed to compare neurodevelopmental trajectories in early and moderately late preterm children, measured using the Developmental (D)-score, in two cohorts born 15 years apart.

Methods: We included early preterm and moderately late preterm children from two Dutch cohorts (LOLLIPOP, 2002-2003 and ePREM, 2016-2017).

Changes in neonatal morbidity, neonatal care practices, and length of hospital stay of surviving infants born very preterm in the Netherlands in the 1980s and in the 2000s: a comparison analysis with identical characteristics definitions.

Background: This study evaluates changes in the neonatal morbidity, the neonatal care practices, and the length of hospital stay of surviving very preterm (VP) infants born in the Netherlands in the 1980s and in the 2000s; a period over which historical improvements were introduced into neonatal care. We, herein, also study whether these changes in neonatal morbidity, neonatal care practices and length of hospital stay are associated with sociodemographic, prenatal, and infant characteristics.

Methods: Two community-based cohorts from 1983 (POPS) and 2002-03 (LOLLIPOP) have provided the perinatal data for our study.

Logistic regression vs. predictive mean matching for imputing binary covariates.

Multivariate imputation using chained equations (MICE) is a popular algorithm for imputing missing data that entails specifying multivariate models through conditional distributions. For imputing missing continuous variables, two common imputation methods are the use of parametric imputation using a linear model and predictive mean matching. When imputing missing binary variables, the default approach is parametric imputation using a logistic regression model.

Evaluation and prediction of individual growth trajectories.

Background: Conventional growth charts offer limited guidance to track individual growth.

Aim: To explore new approaches to improve the evaluation and prediction of individual growth trajectories.

Subjects And Methods: We generalise the conditional SDS gain to multiple historical measurements, using the Cole correlation model to find correlations at exact ages, the sweep operator to find regression weights and a specified longitudinal reference.

Graphical and numerical diagnostic tools to assess multiple imputation models by posterior predictive checking.

Problem: The congenial of the imputation model is crucial for valid statistical inferences. Hence, it is important to develop methodologies for diagnosing imputation models.

Aim: We propose and evaluate a new diagnostic method based on posterior predictive checking to diagnose the congeniality of fully conditional imputation models.

Impute-then-exclude versus exclude-then-impute: Lessons when imputing a variable used both in cohort creation and as an independent variable in the analysis model.

We examined the setting in which a variable that is subject to missingness is used both as an inclusion/exclusion criterion for creating the analytic sample and subsequently as the primary exposure in the analysis model that is of scientific interest. An example is cancer stage, where patients with stage IV cancer are often excluded from the analytic sample, and cancer stage (I to III) is an exposure variable in the analysis model. We considered two analytic strategies.

Protocol for validation of the Global Scales for Early Development (GSED) for children under 3 years of age in seven countries.

Introduction: Children's early development is affected by caregiving experiences, with lifelong health and well-being implications. Governments and civil societies need population-based measures to monitor children's early development and ensure that children receive the care needed to thrive. To this end, the WHO developed the Global Scales for Early Development (GSED) to measure children's early development up to 3 years of age.

Longitudinal individual predictions from irregular repeated measurements data.

Intensive longitudinal data can be used to explore important associations and patterns between various types of inputs and outcomes. Nonlinear relations and irregular measurement occasions can pose problems to develop an accurate model for these kinds of data. This paper focuses on the development, fitting and evaluation of a prediction model with irregular intensive longitudinal data.

The creation of the Global Scales for Early Development (GSED) for children aged 0-3 years: combining subject matter expert judgements with big data.

Introduction: With the ratification of the Sustainable Development Goals, there is an increased emphasis on early childhood development (ECD) and well-being. The WHO led Global Scales for Early Development (GSED) project aims to provide population and programmatic level measures of ECD for 0-3 years that are valid, reliable and have psychometrically stable performance across geographical, cultural and language contexts. This paper reports on the creation of two measures: (1) the GSED Short Form (GSED-SF)-a caregiver reported measure for population-evaluation-self-administered with no training required and (2) the GSED Long Form (GSED-LF)-a directly administered/observed measure for programmatic evaluation-administered by a trained professional.

Joint distribution properties of fully conditional specification under the normal linear model with normal inverse-gamma priors.

Fully conditional specification (FCS) is a convenient and flexible multiple imputation approach. It specifies a sequence of simple regression models instead of a potential complex joint density for missing variables. However, FCS may not converge to a stationary distribution.

Health-Related Quality-of-Life Outcomes of Very Preterm or Very Low Birth Weight Adults: Evidence From an Individual Participant Data Meta-Analysis.

Background And Objective: Assessment of health-related quality of life for individuals born very preterm and/or low birthweight (VP/VLBW) offers valuable complementary information alongside biomedical assessments. However, the impact of VP/VLBW status on health-related quality of life in adulthood is inconclusive. The objective of this study was to examine associations between VP/VLBW status and preference-based health-related quality-of-life outcomes in early adulthood.

The effect of high prevalence of missing data on estimation of the coefficients of a logistic regression model when using multiple imputation.

Background: Multiple imputation is frequently used to address missing data when conducting statistical analyses. There is a paucity of research into the performance of multiple imputation when the prevalence of missing data is very high. Our objective was to assess the performance of multiple imputation when estimating a logistic regression model when the prevalence of missing data for predictor variables is very high.

Birth outcomes between 22 and 26 weeks' gestation in national population-based cohorts from Sweden, England and France.

Aim: We investigated the timing of survival differences and effects on morbidity for foetuses alive at maternal admission to hospital delivered at 22 to 26 weeks' gestational age (GA).

Methods: Data from the EXPRESS (Sweden, 2004-07), EPICure-2 (England, 2006) and EPIPAGE-2 (France, 2011) cohorts were harmonised. Survival, stratified by GA, was analysed to 112 days using Kaplan-Meier analyses and Cox regression adjusted for population and pregnancy characteristics; neonatal morbidities, survival to discharge and follow-up and outcomes at 2-3 years of age were compared.

Neighbors-based prediction of physical function after total knee arthroplasty.

The purpose of this study was to develop and test personalized predictions for functional recovery after Total Knee Arthroplasty (TKA) surgery, using a novel neighbors-based prediction approach. We used data from 397 patients with TKA to develop the prediction methodology and then tested the predictions in a temporally distinct sample of 202 patients. The Timed Up and Go (TUG) Test was used to assess physical function.

Strategies for assessing the impact of loss to follow-up on estimates of neurodevelopmental impairment in a very preterm cohort at 2 years of age.

Background: Loss to follow-up is a major challenge for very preterm (VPT) cohorts; attrition is associated with social disadvantage and parents with impaired children may participate less in research. We investigated the impact of loss to follow-up on the estimated prevalence of neurodevelopmental impairment in a VPT cohort using different methodological approaches.

Methods: This study includes births < 32 weeks of gestational age (GA) from 4 regions in the UK and Portugal participating in a European birth cohort (N = 1737 survivors).

Missing Data in Clinical Research: A Tutorial on Multiple Imputation.

Missing data is a common occurrence in clinical research. Missing data occurs when the value of the variables of interest are not measured or recorded for all subjects in the sample. Common approaches to addressing the presence of missing data include complete-case analyses, where subjects with missing data are excluded, and mean-value imputation, where missing values are replaced with the mean value of that variable in those subjects for whom it is not missing.

Artifacts annotations in anesthesia blood pressure data by man and machine.

Physiologic data from anesthesia monitors are automatically captured. Yet erroneous data are stored in the process as well. While this is not interfering with clinical care, research can be affected.

Reference chart for knee flexion following total knee arthroplasty: a novel tool for monitoring postoperative recovery.

Background: Clinicians and patients lack an evidence-based framework by which to judge individual-level recovery following total knee arthroplasty (TKA) surgery, thus impeding personalized treatment approaches for this elective surgery. Our study aimed to develop and validate a reference chart for monitoring recovery of knee flexion following TKA surgery.

Methods: Retrospective analysis of data collected in routine rehabilitation practice for patients following TKA surgery.

Artifact Processing Methods Influence on Intraoperative Hypotension Quantification and Outcome Effect Estimates.

Background: Physiologic data that is automatically collected during anesthesia is widely used for medical record keeping and clinical research. These data contain artifacts, which are not relevant in clinical care, but may influence research results. The aim of this study was to explore the effect of different methods of filtering and processing artifacts in anesthesiology data on study findings in order to demonstrate the importance of proper artifact filtering.

Patient and anesthesia characteristics of children with low pre-incision blood pressure: A retrospective observational study.

Background: Intraoperative blood pressure has been suggested as a key factor for safe pediatric anesthesia. However, there is not much insight into factors that discriminate between children with low and normal pre-incision blood pressure. Our aim was to explore whether children who have a low blood pressure during anesthesia are different than those with normal blood pressure.

The D-score: a metric for interpreting the early development of infants and toddlers across global settings.

Introduction: Early childhood development can be described by an underlying latent construct. Global comparisons of children's development are hindered by the lack of a validated metric that is comparable across cultures and contexts, especially for children under age 3 years. We constructed and validated a new metric, the Developmental Score (D-score), using existing data from 16 longitudinal studies.

Gestational weight gain charts for different body mass index groups for women in Europe, North America, and Oceania.

Background: Gestational weight gain differs according to pre-pregnancy body mass index and is related to the risks of adverse maternal and child health outcomes. Gestational weight gain charts for women in different pre-pregnancy body mass index groups enable identification of women and offspring at risk for adverse health outcomes. We aimed to construct gestational weight gain reference charts for underweight, normal weight, overweight, and grades 1, 2 and 3 obese women and to compare these charts with those obtained in women with uncomplicated term pregnancies.

Combining multiple imputation and bootstrap in the analysis of cost-effectiveness trial data.

In healthcare cost-effectiveness analysis, probability distributions are typically skewed and missing data are frequent. Bootstrap and multiple imputation are well-established resampling methods for handling skewed and missing data. However, it is not clear how these techniques should be combined.

Systematic review indicates postnatal growth in term infants born small-for-gestational-age being associated with later neurocognitive and metabolic outcomes.

Unlabelled: We systematically reviewed papers published in English between 1994 and October 2015 on how postnatal weight gain and growth affect neurodevelopment and metabolic outcomes in term-born small-for-gestational-age (SGA) infants. Two randomised trials reported that enriched infant formulas that promoted early growth also increased fat mass, lean mass and blood pressure (BP), but had no effect on early neurocognitive outcomes. Meanwhile, 31 observational studies reported consistent positive associations between postnatal weight gain and growth with neurocognitive outcomes, adiposity, insulin resistance and BP.