Glomerular Filtration Rate Estimation by a Novel Numerical Binning-Less Isotonic Statistical Bivariate Numerical Modeling Method

Abstract

Statistical bivariate numerical modeling is a method to infer an empirical relationship between unpaired sets of data based on statistical distributions matching. In the present paper, a novel efficient numerical algorithm is proposed to perform bivariate numerical modeling. The algorithm is then applied to correlate glomerular filtration rate to serum creatinine concentration. Glomerular filtration rate is adopted in clinical nephrology as an indicator of kidney function and is relevant for assessing progression of renal disease. As direct measurement of glomerular filtration rate is highly impractical, there is considerable interest in developing numerical algorithms to estimate glomerular filtration rate from parameters which are easier to obtain, such as demographic and `bedside’ assays data.

A Novel Non-Isotonic Statistical Bivariate Regression Method—Application to Stratigraphic Data Modeling and Interpolation

“…Statistical bivariate regression is a mathematical method to deduce the value of missing points between adjacent pairs of data points (x i , y i ) and constitutes an improvement over isotonic regression. In the paper [16] it was presented an algorithm that estimates the cumulative distribution function (CDF) of the x-set, the inverse cumulative distribution function (INVCDF) of the y-set, and combines such estimations to obtain the sought model. This algorithm can process effectively only monotonic data as it cannot cope with non-monotonic relationships.…”

“…In other words, the set q x contains values of the independent variable that were not observed, hence that do not belong to the x-set, and the procedure SBR infers the corresponding values of the dependent variable. For a detailed explanation of the underlying theory, interested readers might consult the published paper [16].…”

“…The present research takes its moves from the isotonic statistical bivariate regression (SBR) method presented in [16] (which was successfully applied to estimate the glomerular filtration rate in kidneys). Previous comparative studies [5,16] have clearly shown how statistical regression implemented by look-up tables is much faster in execution than traditional techniques while ensuring the same modeling/regression performance.…”

eHealth and Artificial Intelligence

“…The experiments provide a broad overview of the results obtainable on standard microarray datasets with different characteristics in terms of the number of features and number of patients. Giles et al [20] propose a novel numerical algorithm to perform bivariate numerical modeling. The algorithm is applied to correlate glomerular filtration rate to serum creatinine concentration.…”

Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data

“…On the hand, there are literatures which discussed misclassified binomial data, for one-sample and two-sample binomial data, with one-type or both-type of misclassifications, Bayesian or frequentist (i.e., likelihood-based) methods, but none of them are with the multiplicity adjustments for (pairwise) MCPs, either; for example, Rahardja in 2019 [6] reviewed such literature. Additionally, there are past papers which studied various ways to analyze binomial data, but they did not include misclassifications nor multiplicity adjustments for (pairwise) MCPs; for example, among many papers, Gianinetti in 2020 [7], Giles and Fiori in 2019 [8], Hodge and Vieland in 2017 [9]. Stamey, et al in 2004 [10] proposed multiple comparison method for comparing Poisson rate parameters where counts are subject to misclassification.…”