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BACKGROUND: Low birth weight (LBW) is associated with neonatal mortality and sequelae of lifelong health problems; prioritizing the most promising antenatal interventions may guide resource allocation and improve health outcomes. OBJECTIVE: We sought to identify the most promising interventions that are not yet included in the policy recommendations of the World Health Organization (WHO) but could complement antenatal care and reduce the prevalence of LBW and related adverse birth outcomes in low- and middle-income settings. METHODS: We utilized an adapted Child Health and Nutrition Research Initiative (CHNRI) prioritization method. RESULTS: In addition to procedures already recommended by WHO for the prevention of LBW, we identified six promising antenatal interventions that are not currently recommended by WHO with an indication for LBW prevention, namely: (1) provision of multiple micronutrients; (2) low-dose aspirin; (3) high-dose calcium; (4) prophylactic cervical cerclage; (5) psychosocial support for smoking cessation; and (6) other psychosocial support for targeted populations and settings. We also identified seven interventions for further implementation research and six interventions for efficacy research. CONCLUSION: These promising interventions, coupled with increasing coverage of currently recommended antenatal care, could accelerate progress toward the global target of a 30% reduction in the number of LBW infants born in 2025 compared to 2006-10.

The problems of point estimation and classification under the assumption that the training data follow a Lindley distribution are considered. Bayes estimators are derived for the parameter of the Lindley distribution applying the Markov chain Monte Carlo (MCMC), and Tierney and Kadane's [Tierney and Kadane, Accurate approximations for posterior moments and marginal densities, J. Amer. Statist. Assoc. 81 (1986), pp. 82–86] methods. In the sequel, we prove that the Bayes estimators using Tierney and Kadane's approximation and Lindley's approximation both converge to the maximum likelihood estimator (MLE), as $ n \rightarrow \infty $ n → ∞ , where n is the sample size. The performances of all the proposed estimators are compared with some of the existing ones using bias and mean squared error (MSE), numerically. It has been noticed from our simulation study that the proposed estimators perform better than some of the existing ones. Applying these estimators, we construct several plug-in type classification rules and a rule that uses the likelihood accordance function. The performances of each of the rules are numerically evaluated using the expected probability of misclassification (EPM). Two real-life examples related to COVID-19 disease are considered for illustrative purposes. [ABSTRACT FROM AUTHOR]

Analysis of covariance (ANCOVA) models are used when apart from the main treatments, some covariates also affect the response variable. In this article, the problem of testing the homogeneity of treatment effects against ordered alternatives is addressed in a fixed effects one‐way ANCOVA model when error variances are heterogeneous. The likelihood ratio test (LRT) and two approximate tests based on the asymptotic distributions of some union‐intersection type test statistics are proposed. A parametric bootstrap technique has been used to implement the LRT and its asymptotic validity is proved. A method to construct simultaneous confidence intervals is proposed. All the test procedures are further extended to the case of more than one covariate. The robustness of tests is also studied under departure from normality. Extensive simulation studies show that the proposed test procedures perform well in terms of achieving the nominal size value and good power values. [ABSTRACT FROM AUTHOR]

In a two‐way additive analysis of variance (ANOVA) model, we consider the problem of testing for homogeneity of both row and column effects against their simultaneous ordering. The error variances are assumed to be heterogeneous with unbalanced samples in each cell. Two simultaneous test procedures are developed—the first one using the likelihood ratio test (LRT) statistics of two independent hypotheses and another based on the consecutive pairwise differences of estimators of effects. The parametric bootstrap (PB) approach is used to find critical points of both the tests and the asymptotic accuracy of the bootstrap is established. An extensive simulation study shows that the proposed tests achieve the nominal size and have very good power performance. The robustness of the tests is also analyzed under deviation from normality. An "R" package is developed and shared on "GitHub" for ease of implementation of users. The proposed tests are illustrated using a real data set on the mortality due to alcoholic liver disease and it is shown that age and gender have a significant impact on the increasing incidence of mortality. [ABSTRACT FROM AUTHOR]