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Editor’s Choice : Predicting Risks of Increased Morbidity among Atrial Fibrillation Patients using Consumption Classes
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Abstract: Background: Atrial fibrillation (AF) is the most common chronic cardiac arrhythmia. Predicting the risk of complications, or associated increases in healthcare costs, among AF patients is important for effective health care management. Methods: A bivariate regression model including a latent morbidity index is used to predict both risk of transition to higher health costs, and mortality risk over a single year. A risk scoring algorithm for predicting transition to higher cost levels is then set out which incorporates the most significant risk factors from the regression. Results: The regression analysis shows that in addition to age and comorbidities, baseline consumption category, ethnic group, metropolitan residence and Warfarin adherence are also significant influences on progression to increased health consumption, and relevant to assessing risk. The resulting risk scoring algorithm produces a higher AUC than the widely applied CHADS2 score. Conclusions: The utility of a bivariate regression method with a latent morbidity index for predicting transition to worsening health status among AF patients is demonstrated. A risk scoring system based on this method outperforms an established risk score. Keywords: Morbidity, Risk scores, Latent variable, Atrial fibrillation, Consumption class.Download Full Article |
Editor’s Choice : Research Article: Survival Analysis of Under Five Mortality in Rural Parts of Ethiopia
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Abstract: Child mortality is a factor that is associated with the well-being of a population and it is taken as an indicator of health development and socioeconomic status. According to the 2011 UN report during the last 10 years, the death rate for children under five has decreased by 35% worldwide. UNICEF in 2008 reported that Ethiopia has reduced under-five mortality by 40 percent over the past 15 years. From the EDHS 2011 report child mortality rate in Ethiopia was reduced from 50/1000 deaths in 2005 to 31/1000 deaths in 2011. The Ethiopian Demographic and Health Survey data are used for the study. In this paper we have attempted to find out the impact of socioeconomic, demographic and environmental factors in the context of under five mortality. In this attempt we first analyzed our data using Kaplan-Meier non-parametric method of estimation of survival function and also using lifetable. We have also used Log-Rank test to compare different survival functions and found that sex, type of birth, religion, mothers’ education, birth order, maternity age, source of drinking water and region have statistically significant difference in the under five survival time. We have also used Cox proportional hazard model to identify the covariates which influence the under five mortality. But we found that our data do not fulfill the proportionality assumption of Cox proportional model in case of infant and child mortality. Then we applied stratified Cox proportional model to our data to find out the potential covariates which influence under five mortality and found birth order, mothers’ education level, sex, type of birth and the interaction of birth order and sex as vital factors for the deaths occurring under the age of five. The Cox proportional hazard models which were used separately for each stratum also identified mothers’ educational level, sex, type of birth, and the interaction of sex and water supply as the risk factors for the death of infants. Whereas for child stratum; type of birth, mothers’ education, sex and the interaction of water supply and sex were the risk factors associated with the death of children. Keywords: Under five mortality, maternal, socioeconomic and environmental factor.Download Full Article |
Editor’s Choice : The Methodology of Human Diseases Risk Prediction Tools
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Abstract: Disease risk prediction tools are used for population screening and to guide clinical care. They identify which individuals have particularly elevated risk of disease. The development of a new risk prediction tool involves several methodological components including: selection of a general modelling framework and specific functional form for the new tool, making decisions about the inclusion of risk factors, dealing with missing data in those risk factors, and performing validation checks of a new tool’s performance. There have been many methodological developments of relevance to these issues in recent years. Developments of importance for disease detection in humans were reviewed and their uptake in risk prediction tool development illustrated. This review leads to guidance on appropriate methodology for future risk prediction development activities. Keywords: Disease risk prediction, missing data, model validation, model updating, model utility.Download Full Article |
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Abstract: This paper discusses the use of PP-plots for survival distributions where for a pair of survival distributions, one is plotted against the other. This is another way of visualizing the nature of the relationship between the two survival distributions along with typical Kaplan-Meier plots. For three survival distributions, the PPP-plot is introduced where the survival distributions are plotted against each other in three-dimensions. At the population level, measures of divergence between distributions are introduced based on areas and lengths associated with the PP- and PPP- plots. At the sample level, two test statistics are defined, based on these areas and lengths, to test the null hypothesis of equivalent survival curves. A simulation exercise showed that, overall, the new tests are worthy competitors to the log-rank and Wilcoxon tests and also to a Levine-type test and a Kolmogorov-Smirnov type test for the case of crossing survival curves. The paper also shows how the PP-plot can be used to estimate the hazard ratio and to assess the ratio of hazard functions if proportional hazards are not appropriate. Finally, the methods introduced are illustrated on two cancer data sets. Keywords: Crossing survival curves, Hazard ratio, Kaplan-Meier, Log-rank test, PP-plot, Wilcoxon test. |
Editor’s Choice : Validation of Gene Expression Profiles in Genomic Data through Complementary Use of Cluster Analysis and PCA-Related Biplots
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Abstract: High-throughput genomic assays are used in molecular biology to explore patterns of joint expression of thousands of genes. These methodologies had relevant developments in the last decade, and concurrently there was a need for appropriate methods for analyzing the massive data generated. Identifying sets of genes and samples characterized by similar values of expression and validating these results are two critical issues related to these investigations because of their clinical implication. From a statistical perspective, unsupervised class discovery methods like Cluster Analysis are generally adopted. However, the use of Cluster Analysis mainly relies on the use of hierarchical techniques without considering possible use of other methods. This is partially due to software availability and to easiness of representation of results through a heatmap, which allows to simultaneously visualize clusterization of genes and samples on the same graphical device. One drawback of this strategy is that clusters’ stability is often neglected, thus leading to over-interpretation of results. Moreover, validation of results using external datasets is still subject of discussion, since it is well known that batch effects may condition gene expression results even after normalization. In this paper we compared several clustering algorithms (hierarchical, k-means, model-based, Affinity Propagation) and stability indices to discover common patterns of expression and to assess clustering reliability, and propose a rank-based passive projection of Principal Components for validation purposes. Results from a study involving 23 tumor cell lines and 76 genes related to a specific biological pathway and derived from a publicly available dataset, are presented. Keywords: Microarrays, cluster stability, multivariate visualization, Principal Components Analysis, cell polarity.Download Full Article |