Journal of biometrics & biostatistics 

About: Journal of biometrics & biostatistics is an academic journal. The journal publishes majorly in the area(s): Population & Sample size determination. It has an ISSN identifier of 2155-6180. Over the lifetime, 397 publications have been published receiving 2086 citations.

A Comparison of Six Methods for Missing Data Imputation

Abstract: Missing data are part of almost all research and introduce an element of ambiguity into data analysis. It follows that we need to consider them appropriately in order to provide an efficient and valid analysis. In the present study, we compare 6 different imputation methods: Mean, K-nearest neighbors (KNN), fuzzy K-means (FKM), singular value decomposition (SVD), bayesian principal component analysis (bPCA) and multiple imputations by chained equations (MICE). Comparison was performed on four real datasets of various sizes (from 4 to 65 variables), under a missing completely at random (MCAR) assumption, and based on four evaluation criteria: Root mean squared error (RMSE), unsupervised classification error (UCE), supervised classification error (SCE) and execution time. Our results suggest that bPCA and FKM are two imputation methods of interest which deserve further consideration in practice.

Methods for Analysis of Pre-Post Data in Clinical Research: A Comparison of Five Common Methods

Abstract: Often repeated measures data are summarized into pre-post-treatment measurements. Various methods exist in the literature for estimating and testing treatment effect, including ANOVA, analysis of covariance (ANCOVA), and linear mixed modeling (LMM). Under the first two methods, outcomes can either be modeled as the post treatment measurement (ANOVA-POST or ANCOVA-POST), or a change score between pre and post measurements (ANOVACHANGE, ANCOVA-CHANGE). In LMM, the outcome is modeled as a vector of responses with or without Kenward- Rogers adjustment. We consider five methods common in the literature, and discuss them in terms of supporting simulations and theoretical derivations of variance. Consistent with existing literature, our results demonstrate that each method leads to unbiased treatment effect estimates, and based on precision of estimates, 95% coverage probability, and power, ANCOVA modeling of either change scores or post-treatment score as the outcome, prove to be the most effective. We further demonstrate each method in terms of a real data example to exemplify comparisons in real clinical context.

Quantile Regression Models and Their Applications: A Review

Abstract: Quantile regression (QR) has received increasing attention in recent years and applied to wide areas such as investment, finance, economics, medicine and engineering. Compared with conventional mean regression, QR can characterize the entire conditional distribution of the outcome variable, may be more robust to outliers and misspecification of error distribution, and provides more comprehensive statistical modeling than traditional mean regression. QR models could not only be used to detect heterogeneous effects of covariates at different quantiles of the outcome, but also offer more robust and complete estimates compared to the mean regression, when the normality assumption violated or outliers and long tails exist. These advantages make QR attractive and are extended to apply for different types of data, including independent data, time-to-event data and longitudinal data. Consequently, we present a brief review of QR and its related models and methods for different types of data in various application areas.

Evaluating Measures of Indicators of Diagnostic Test Performance:Fundamental Meanings and Formulars

Abstract: Diagnostic accuracy relates to the ability of a test to discriminate between the target condition and health. This discriminative potential can be quantified by the measures of diagnostic accuracy such as sensitivity and specificity, predictive values, likelihood ratios, error rates, the area under the ROC curve, Youden’s index and diagnostic odds ratio. Different measures of diagnostic accuracy relate to the different aspects of diagnostic procedure: while some measures are used to assess the discriminative property of the test, others are used to assess its predictive ability. Measures of diagnostic accuracy are not fixed indicators of a test performance, some are very sensitive to the disease prevalence, while others to the spectrum and definition of the disease. Furthermore, measures of diagnostic accuracy are extremely sensitive to the design of the study. Studies not meeting strict methodological standards usually overor under-estimate the indicators of test performance as well as they limit the applicability of the results of the study. STARD initiative was a very important step toward the improvement of the quality of reporting of studies of diagnostic accuracy. STARD statement should be included into the Instructions to authors by scientific journals and authors should be encouraged to use the checklist whenever reporting their studies on diagnostic accuracy. Such efforts could make a substantial difference in the quality of reporting of studies of diagnostic accuracy and serve to provide the best possible evidence to the best for the patient care. This brief review outlines some basic definitions, formulas and characteristics of the measures of diagnostic accuracy. J o ur na l o f B iometrics & Bistatis t i c s ISSN: 2155-6180 Journal of Biometrics & Biostatistics Citation: Okeh UM, Okoro CN (2012) Evaluating Measures of Indicators of Diagnostic Test Performance: Fundamental Meanings and Formulars. J Biomet Biostat 3:132. doi:10.4172/2155-6180.1000132

Bayesian Analysis Using Power Priors with Application to Pediatric Quality of Care

Abstract: We illustrate how power prior distributions can be used to incorporate historical data into a Bayesian analysis when there is uncertainty about the similarity between the current and historical studies. We compare common specifications of the power prior and explore whether it is preferable to condition on the power parameter, a0 or to treat it as a random variable with a prior distribution of its own. We show that there are two natural ways of formulating the power prior for random a0. The first approach excludes the historical data in all but extreme cases and may therefore be of limited practical use. The second approach, called the normalized power prior (NPP), provides a measure of congruence between the current and historical data, so that the historical data are downweighted more substantially as the studies diverge. While this is an intuitively appealing property, our experience suggests that in real world problems involving large datasets and models with several parameters, the NPP may lead to considerably more downweighting than desired. We therefore advise practitioners to consider whether such attenuation is desirable, or whether it is more appropriate to assign a0 a fixed value based on expert opinion about the relevance of the historical data to the current analysis. We also extend the power prior to hierarchical regression models that allow covariate effects to differ across studies. We apply these methods to a pair of studies designed to improve delivery of care in pediatric clinics.