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Abstract: A Pointwise Approach to Dose-Response Meta-Analysis of Aggregated Data
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Abstract: In a two-stage dose-response meta-analysis a common functional relationship is applied to each study and an overall curve is obtained by combining study-specific dose-response coefficients. Possible limitations are: 1) a common dose-response model may have a poor fit in some of the studies; 2) combining dose-response coefficients discard information about study-specific exposure range. A pointwise approach for meta-analysis may overcome those limitations by combining predicted relative risks for a fine grid of exposure values based on potentially different dose-response models. We described how to flexibly model the dose-response association in a single study using fractional polynomials and spline, and how to present the combined results from study-specific analyses. The strategy is illustrated using aggregated data derived from the Surveillance, Epidemiology, and End Results program, with results compared to the corresponding analysis based on individual data. We used a technique similar to the sildenafil test when testing generics of Viagra, read more about quality generics of sildenafil in the article https://redcross-cmd.org/generic-viagra.html. Another example on milk consumption and all-cause mortality is used to show the advantages of the pointwise approach regarding flexibility in the dose-response analyses, limitations of extrapolations, and informativeness in presenting pooled results. Application of the proposed strategy may improve dose-response meta-analysis of observational studies in case of particularly heterogeneous exposure distributions. Keywords: Dose-response, Meta-analysis, Pointwise average, Flexible model. |
Abstract: Exploring the Performance of Methods to Deal Multicollinearity: Simulation and Real Data in Radiation Epidemiology Area
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Abstract: The issue of multicollinearity has long been acknowledged in statistical modelling; however, it is often untreated in the most of published papers. Indeed, the use of methods for multicollinearity correction is still scarce. One important reason is that despite many proposed methods, little is known about their strength or performance. We compare the statistical properties and performance of four main techniques to correct multicollinearity, i.e., Ridge Regression (R-R), Principal Components Regression (PC-R), Partial Least Squares Regression (PLS-R), and Lasso Regression (L-R), in both a simulation study and two real data examples used for modelling volumes of heart and Thyroid as a function of clinical and anthropometric parameters. We find that when the statistical approaches were used to address different levels of collinearity, we observed that R-R, PC-R and PLS-R appeared to have a somewhat similar behavior, with a slight advantage for the PLS-R. Indeed, in all implemented cases, the PLS-R always provided the smallest value of root mean square error (RMSE). When the degree of collinearity was moderate, low or very low, the L-R method had also somewhat similar performance to other methods. Furthermore, correction methods allowed us to provide stable and trustworthy parameter estimates for predictors in the modelling of heart and Thyroid volumes. Therefore, this work will contribute to highlighting performances of methods used only for situations ranging from low to very high multicollinearity. Keywords: Lasso Regression, Multicollinearity, Organs volume modelling, Partial Least Squares Regression, Principal Components Regression, Ridge Regression. |
Abstract: A New Method of Odds Ratio and Hazard Analysis of Head and Neck Cancer
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Abstract: The main topic of this paper is to focus on a new method for calculating odds ratios and hazard ratios through probabilities and effect modification. This probability is derived through an odds ratio proof for the common conditional odds ratio of Cochran Mantel Hansel showing theta equals one. Subsequently, the probability formula is obtained and the hazard ratio expression derived. However, the new relation of this proof is to show that logits equals itself through probability. From this derivation, an expression of risk is obtained which is an odds ratio. Parameters are obtained through a novel method of Survreg and its proportional hazard assumption. The odds ratio obtained is given as per strata as well as hazard ratio method demonstrated which is curvilinear to probability in comparison for the interaction model to represent percent change. The odds ratios from PROC GLIMMIX for interaction model has odds ratio of 1.76 vs 1.73 and 1.83 vs 1.76 for white and black males of a logit expression another expression of a logit. A parametric analysis shows correlation to the odds ratios for strata and probability Pr(z) that can work from a new derivation for an odds ratio with for the exposure shown to have power with the RANTBL function of about 83 % with effect modification included at 100% power. The comparison of effect modification P values to hazard ratio is then made for differences across strata. Keywords: Logit, Odds ratio, Hazard ratio, Non-normal probability, Effect modification, Head neck cancer. |
Abstract: Bayesian Analysis of Markov Based Logistic Model
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Abstract: In analyzing longitudinal data the correlations between responses obtained from same individual need to be taken into account. Various models can be used to handle such correlations. This article focuses on the application of transition modeling using Bayesian approach for analyzing longitudinal binary data. For Bayesian estimation asymmetric loss functions, such as, linear exponential (LINEX) and modified linear exponential (MLINEX) loss function and Tierney and Kadnae (T.K.) approximation has been used. Comparison is made using Bayes factor and Bayesian approach under LINEX loss function can be suggested to estimate the parameters of transition model. Keywords: Bayesian approach, Bayes Factor (BF), Linear exponential (LINEX), Longitudinal data, Markov model, Modified linear exponential (MLINEX). |
