scholarly journals Bayesian Analysis of Trends in Utilization of Maternal Healthcare Services in Pakistan during 2006-2018

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Navid Feroze ◽  
Muhammad Ajmal Ziad ◽  
Rabia Fayyaz ◽  
Yaé Ulrich Gaba
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Healthcare Services ◽  
Maternal Healthcare ◽  
Logistic Regression Models ◽  
Socioeconomic Groups ◽  
Bayesian Logistic Regression ◽  
Skilled Health ◽  
Better Than

Objectives. This study is aimed at investigating the time trends and disparities in access to maternal healthcare in Pakistan using Bayesian models. Study Design. A longitudinal study from 2006 to 2018. Methods. The detailed analysis is based on the data from Pakistan Demographic and Health Survey (PDHS) conducted during 2006-2018. We have proposed Bayesian logistic regression models (BLRM) to investigate the trends of maternal healthcare in the country. Based on different goodness-of-fit criteria, the performance of proposed models has also been compared with repeatedly used classical logistic regression models (CLRM). Results. The results from the analysis suggested that BLRM perform better than CLRM. The access to antenatal healthcare increased from 61% to 86% during years 2006-18. The utilization of medication also improved from 44% in 2006 to 60% in 2018. Despite the improvements from 2006 to 2018, every three out of ten women were not protected against neonatal tetanus, neither delivered in the health facility place nor availed with the skilled health provider at the time of delivery during 2018. Similarly, two-fifth mothers did not received any skilled postnatal checkup within two days after delivery. Additionally, the likelihood of MHS provided to mothers is in favor of mothers with lower ages, lower birth orders, urban residences, higher education, higher wealth quintiles, and residents of Sindh and Punjab. Conclusions. The gaps in utilization of MHS in different socioeconomic groups of the society have not decreased significantly during 2006-2018. Any future maternal health initiative in the country should focus to reduce the observed disparities among different socioeconomic sectors of the society.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

2009 ◽  
Vol 48 (03) ◽  
pp. 306-310 ◽  
Author(s):  
C. E. Minder ◽  
G. Gillmann
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Graphical Method ◽  
Logistic Models ◽  
Binary Logistic Regression ◽  
Logistic Regression Models ◽  
Logistic Analysis ◽  
Viable Solution

Summary Objectives: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. Methods: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. Results: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. Conclusion: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

scholarly journals Ordinal logistic regression models: application in quality of life studies

2008 ◽  
Vol 24 (suppl 4) ◽  
pp. s581-s591 ◽  
Author(s):  
Mery Natali Silva Abreu ◽  
Arminda Lucia Siqueira ◽  
Clareci Silva Cardoso ◽  
Waleska Teixeira Caiaffa
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Ordinal Logistic Regression ◽  
Proportional Odds Model ◽  
Outcome Variable ◽  
Proportional Odds ◽  
Logistic Regression Models

Quality of life has been increasingly emphasized in public health research in recent years. Typically, the results of quality of life are measured by means of ordinal scales. In these situations, specific statistical methods are necessary because procedures such as either dichotomization or misinformation on the distribution of the outcome variable may complicate the inferential process. Ordinal logistic regression models are appropriate in many of these situations. This article presents a review of the proportional odds model, partial proportional odds model, continuation ratio model, and stereotype model. The fit, statistical inference, and comparisons between models are illustrated with data from a study on quality of life in 273 patients with schizophrenia. All tested models showed good fit, but the proportional odds or partial proportional odds models proved to be the best choice due to the nature of the data and ease of interpretation of the results. Ordinal logistic models perform differently depending on categorization of outcome, adequacy in relation to assumptions, goodness-of-fit, and parsimony.

On the existence of posterior mean for Bayesian logistic regression

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Huong T. T. Pham ◽  
Hoa Pham
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Sufficient Conditions ◽  
Maximum Likelihood Estimates ◽  
Logistic Regression Models ◽  
Posterior Mean ◽  
Different Types ◽  
Data Points ◽  
Necessary And Sufficient ◽  
Bayesian Logistic Regression

Abstract Existence conditions for posterior mean of Bayesian logistic regression depend on both chosen prior distributions and a likelihood function. In logistic regression, different patterns of data points can lead to finite maximum likelihood estimates (MLE) or infinite MLE of the regression coefficients. Albert and Anderson [On the existence of maximum likelihood estimates in logistic regression models, Biometrika 71 1984, 1, 1–10] gave definitions of different types of data points, which are complete separation, quasicomplete separation and overlap. Conditions for the existence of the MLE for logistic regression models were proposed under different types of data points. Based on these conditions, we propose the necessary and sufficient conditions for the existence of posterior mean under different choices of prior distributions. In this paper, a general wide class of priors, which are informative priors and non-informative priors having proper distributions and improper distributions, are considered for the existence of posterior mean. In addition, necessary and sufficient conditions for the existence of posterior mean for an individual coefficient is also proposed.

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