Robust estimation in familial and longitudinal models

There exists many studies on the robust estimation of the regression effects in a linear model set up for continuous such as Gaussian data possibly containing one or more outliers. The robust estimation of the regression effects in a generalized linear model (GLM) set up for the count and binary data in the presence of outliers is, however, relatively difficult. In this thesis, we deal with this difficult estimation issue and develop the robust estimation procedures under three scenarios. First, a fully standardized Mallows-type quasi-likelihood (FSMQL) estimation technique is developed to obtain consistent regression estimates in the GLM set up for both independent count and binary data. Secondly, we develop a robust generalized quasi-likelihood (RGQL) estimation procedure to deal with the outliers in the generalized linear mixed model (GLMM) set up for both count and binary data. Finally, we also develop the RGQL estimation procedure to deal with possible outliers in the GLM set up for the longitudinal count and binary data. The performances of the proposed robust estimators are examined through extensive simulation studies under all three set up: the GLM for the independent count and binary data; the GLMM for the familial count and binary data; and the GLM for the longitudinal count and binary data

Analyzing binary longitudinal data in adaptive clinical trials

In an adaptive clinical trial research, it is common to use certain data dependent design weights to assign individuals to treatments so that more study subjects are assigned to the better treatment. These design weights must also be used for consistent estimation of the treatment effects as well as the effects of other prognostic factors. In practice, there are however situations where it may be necessary to collect binary responses repeatedly from an individual over a period of time and to obtain consistent and efficient estimates for the treatment effects as well as the effects of the other covariates. In this thesis, we introduce a binary response based longitudinal adaptive design for the allocation of individuals to a better treatment, and propose a weighted generalized quasi-likelihood (WGQL) approach for the consistent and efficient estimation of the regression parameters, including the treatment effects. We also introduce a binary longitudinal adaptive mixed model assuming that given the treatment effects and the unobservable individual random effect, repeated responses of an individual are longitudinally correlated. An extended WGQL approach is also used to obtain consistent and efficient estimators for the regression parameters and the variance component of individual random effects

On Bias Reduction in Robust Inference for Generalized Linear Models

It is well known that one or more outlying points in the data may adversely affect the consistency of the quasi-likelihood or the likelihood estimators for the regression effects. Similar to the quasi-likelihood approach, the existing outliers-resistant Mallow's type quasi-likelihood (MQL) estimation approach may also produce biased regression estimators. As a remedy, by using a fully standardized score function in the MQL estimating equation, in this paper, we demonstrate that the fully standardized MQL estimators are almost unbiased ensuring its higher consistency performance. Both count and binary responses subject to one or more outliers are used in the study. The small sample as well as asymptotic results for the competitive estimators are discussed. Copyright (c) 2009 Board of the Foundation of the Scandinavian Journal of Statistics.

Observation-Driven Model for Zero-Inflated Daily Counts of Emergency Room Visit Data

Time series data with excessive zeros frequently occur in medical and health studies. To analyze time series count data without excessive zeros, observation-driven Poisson regression models are commonly used in the literature. As handling excessive zeros in count data is not straightforward, observation-driven models are rarely used to analyze time series count data with excessive zeros. In this paper an observation-driven zero-inflated Poisson (ZIP) model for time series count data is proposed. This approach can accommodate an autoregressive serial dependence structure which commonly appears in time series. The estimation of the model parameters by using the quasi-likelihood estimating equation approach is discussed. To estimate the correlation parameters of the dependence structure, a moment approach is used. The proposed methodology is illustrated by applying it to a data set of daily emergency room visits due to bronchitis

Skilled maternal healthcare and good essential newborn care practice in rural Bangladesh: A cross‐sectional study

Abstract Background and Aims Essential newborn care (ENC) practices play an important role in reducing the risk of infant mortality and morbidity. Therefore, more studies are needed on ENC practices. Skilled maternal healthcare can be a good strategy to increase the practice. Learn about the independent and joint effects of skilled maternal healthcare during pregnancy and childbirth on newborn care practices. Methods The study used a cross‐sectional data obtained from Bangladesh Multiple Indicator Cluster Survey, 2019. To investigate the association between maternal healthcare utilization and good ENC practice (cord care, delayed bathing, and immediate breast‐feeding), χ2 test and t‐test in bivariate and binary logistic regression analysis, respectively have been performed after taking into account complex survey design. Results Only about 24% (95% confidence interval [CI]: 22.95%–25.89%) women given birth at home in rural Bangladesh followed good newborn care practice. The results obtained from adjusted regression analysis showed that a woman was 24%, 49%, and 75% more likely of having good ENC practice if she received four or more skilled checkups during antenatal period only (adjusted odds ratios [AOR]: 1.24, 95% CI: 0.97, 1.60), received assistance from SBA during delivery only (AOR: 1.49, 95% CI 1.12, 1.97) and received skilled healthcare in both pregnancy and delivery (AOR: 1.75, 95% CI 1.13, 2.71), respectively compared to a woman who did not get an opportunity to receive skilled healthcare during pregnancy and delivery. Among the selected confounders, maternal age at birth, birth order, education of household heads and religion showed a significant association with good ENC practice. Conclusion The study revealed that proper maternal healthcare during pregnancy and childbirth from skilled health personnel can improve the rate of ENC practices. For this, more training programs should be started, especially at the community level, and health promotion activities are needed to create awareness about efficient maternal healthcare practices

Trend of determinants of birth interval dynamics in Bangladesh

Abstract Background The distribution of birth intervals can be used to draw attention to important characteristics of dynamics of fertility process. The main objective of this paper is to examine the effects of socioeconomic, demographic and proximate determinants on the length of birth intervals of women of Bangladesh and also to see whether the effects are changed over the years. Methods Birth intervals can be considered as correlated time-to-event data because two or more birth intervals could correspond to a single mother. Moreover, women from the same neighborhood usually share certain unobserved characteristics, which may also lead to correlated time-to-event data (birth interval). A parametric random effect (frailty) model is used to analyze correlated birth interval data obtained from three Bangladesh Demographic and Health Surveys (BDHS 2004, 2007, and 2011). Results The results show that alongside different socioeconomic, demographic determinants, unobserved community and mother effects have considerable impact on birth interval in Bangladesh. However, the effects of different factors on birth interval changes in a small scale over the duration of 2004–2011. Conclusions Efficient policy is a priority for promoting longer birth spacing and achieving a decline in fertility