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Abstract: A matingsystem, previously derived, which is more general than random mating is defined by the gene frequency q and a parameter F which measures divergence from Hardy-Weinberg proportions commonly used in genetic analysis. F can be viewed as the average coefficient of inbreeding in a population, the use emphasized here. Also it can characterize the variation in gene frequency in a stratified population. Taking q as fixed, the distribution of F over values admissible under the general mating system is derived by simulation. The mating system may be seen to be based on indifference as to choice of mates. This is the first object of the paper. The second uses the derived distribution of F to make a Bayesian estimate of F from a single sample of genotypic counts. Such an estimate has a number of uses in genetic analysis. Keywords: Genetic Equilibrium, Hardy-Weinberg Law, Mate choice indifference, Inbreeding coefficient, Bayesian estimation.Download Full Article |
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Abstract: Objectives: The aim of this study was to identify the risk factors associated with number of crime committed by youth (Youth Delinquency) between ages 10-17, using Ordinary Least Square (OLS), Poisson Regression model (PRM), Negative Binomial Regression model (NBRM)& Zero Inflated Negative Binomial (ZINB) with the aim to choose the most appropriate model for the observed count data. Methodology: The data in the study was collected from youth whose mothers enrolled in Philadelphia Collaborative Perinatal Project (CPP). School and delinquency record (between ages 10-17) was obtained by the Centre for studies in Criminology and Criminal Law. Literature search suggest that factors associated with child delinquency can be divided into four main factors as Individual, Family, School and Peer. Therefore we included variables in the analysis accordingly. Result: For OLS scatter plot of residuals versus estimated counts showed definite pattern of heterogeneity (non-constant variance). The likelihood-ratio (LR) test of over dispersion yields the significant p-value, which implied that the outcome variable is overdispersed. The plot of the difference between the actual probabilities and the mean predicted probabilities for each model showed that PRM has poor predictions for low counts (0-2). Conclusion:NBRM and ZINB both performed well, however fit statistics revealed that NBRM has provided more closed predication as compare ZINB.NB modeling techniques provides much more compelling and accurate results instead of basic PRM or those available through simple linear or log-linear modeling techniques. Keywords: Count Data, Poisson regression model, Negative Binomial Regression.Download Full Article |
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Abstract: Background: Receiver operating characteristic (ROC) curve and derived measures as the Area Under the Curve (AUC) are often used for evaluating the discriminatory capability of a continuous biomarker in distinguishing between alternative states of health. However, if the marker shows an irregular distribution, with a dominance of diseased subjects in noncontiguous regions, classification using a single cutpoint is not appropriate, and it would lead to erroneous conclusions. This study sought to describe a procedure for improving the discriminatory capacity of a continuous biomarker, by using generalized additive models (GAMs) for binary data. Methods: A new classification rule is obtained by using logistic GAM regression models to transform the original biomarker, with the predicted probabilities being the new transformed continuous biomarker. We propose using this transformed biomarker to establish optimal cut-offs or intervals on which to base the classification. This methodology is applied to different controlled scenarios, and to real data from a prospective study of patients undergoing surgery at a University Teaching Hospital, for examining plasma glucose as postoperative infection biomarker. Results: Both, theoretical scenarios and real data results show that when the risk marker-disease relationship is not monotone, using the new transformed biomarker entails an improvement in discriminatory capacity. Moreover, in these situations, an optimal interval seems more reasonable than a single cutpoint to define lower and higher disease-risk categories. Conclusions: Using statistical tools which allow for greater flexibility (e.g., GAMs) can optimize the classificatory capacity of a potential marker using ROC analysis. So, it is important to question linearity in marker-outcome relationships, in order to avoid erroneous conclusions. Keywords: Discriminatory capability, ROC, AUC, optimal cutpoint, biomarker, plasma glucose.Download Full Article |
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Abstract: Missing data commonly occur in large epidemiologic studies. Ignoring incompleteness or handling the data inappropriately may bias study results, reduce power and efficiency, and alter important risk/benefit relationships. Standard ways of dealing with missing values, such as complete case analysis (CCA), are generally inappropriate due to the loss of precision and risk of bias. Multiple imputation by fully conditional specification (FCS MI) is a powerful and statistically valid method for creating imputations in large data sets which include both categorical and continuous variables. It specifies the multivariate imputation model on a variable-by-variable basis and offers a principled yet flexible method of addressing missing data, which is particularly useful for large data sets with complex data structures. However, FCS MI is still rarely used in epidemiology, and few practical resources exist to guide researchers in the implementation of this technique. We demonstrate the application of FCS MI in support of a large epidemiologic study evaluating national blood utilization patterns in a sub-Saharan African country. A number of practical tips and guidelines for implementing FCS MI based on this experience are described. Keywords: Missing data, multiple imputation, fully conditional specification, complete case analysis, blood utilization.Download Full Article |
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Abstract: When national policy decisions are to be guided by the results of statistical analyses, it is important, to avoid being misled to look beyond the authors’ conclusions and first to assess the study design, measurement and analytic methods, in order to decide whether a study’s conclusions rest on a solid foundation. In particular, observational studies must be carefully and critically evaluated. Using a study widely cited concerning the effects of low-level lead exposure and IQ, we illustrate several methodological errors, long known but often ignored. The goal is not to settle the controversies about the effect of lead on IQ, nor to disparage observational studies, for they are the foundation of all studies done to guide policy, but to encourage additional care in the use of such studies to address policy questions. Keywords: Policy decisions, Statistical Significance, Practical or Policy Significance, Methodological Errors, Lead/IQ Association. Download Full Article |