Comparison of ordinary linear regression, orthogonal regression, standardized principal component analysis, Deming and Passing-Bablok approach for method validation in laboratory medicine

2013 ◽  
Vol 37 (3) ◽  
Author(s):  
Rainer Haeckel ◽  
Werner Wosniok ◽  
Rainer Klauke
Keyword(s):  
Linear Regression ◽  
Method Validation ◽  
Regression Line ◽  
Method Comparison ◽  
Principal Component Regression ◽  
Principal Component ◽  
Orthogonal Regression ◽  
Study Results ◽  
Deming Regression ◽  
Method Comparison Study

AbstractA well-accepted tool for method validation is a method comparison study. Results are usually assessed on a scatter plot of which the fitting line is calculated by several approaches, for example, ordinary (vertical) linear regression (OLR), orthogonal regression (OR), Deming regression (DR), Passing-Bablok method (PBR) or standardized principal component regression (SPCR). DR was applied in its general form (gDR), requiring information of the imprecision of at least two different quantities and as simple DR (sDR) with imprecision information of only one quantity. The equation of the regression line calculated by these concepts varies depending on range of measurement, analytical variation and on imprecision ratio (

Functional Linear Regression

2018 ◽  
Author(s):  
Hervé Cardot ◽  
Pascal Sarda
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Estimation Error ◽  
Asymptotic Properties ◽  
Principal Component Regression ◽  
Principal Component ◽  
Penalized Least Squares ◽  
Open Problems ◽  
Functional Linear Regression

This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.

Is the Fluorometric Method for Blood Bilirubin Accurate?

PEDIATRICS ◽  
1980 ◽  
Vol 66 (4) ◽  
pp. 653-654
Author(s):  
George Cembrowski ◽  
Carl C. Garber
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Correlation Coefficient ◽  
Whole Blood ◽  
Total Bilirubin ◽  
Regression Line ◽  
Method Comparison ◽  
Fluorometric Method ◽  
Accuracy And Precision ◽  
Comparison Data

The article by Brown et al1 interested us greatly. Direct fluorometric measurement of whole blood bilirubin, if accurate and precise, will help in the care of the jaundiced neonate. The accuracy and precision of this technique, however, cannot be readily assessed from the authors' data. Consider the comparison of the fluorometric method to the Jendrassik-Grof method for total bilirubin. To assess accuracy, the authors use the correlation coefficient, r, and the slope of the linear regression line through the method comparison data. There are two problems with this data analysis.

Relative increase in creatine kinase MB isoenzyme during reperfusion after myocardial infarction is method dependent

1990 ◽  
Vol 36 (8) ◽  
pp. 1444-1449 ◽  
Author(s):  
R H Christenson ◽  
P Clemmensen ◽  
E M Ohman ◽  
J Toffaletti ◽  
L M Silverman ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Creatine Kinase ◽  
Regression Line ◽  
Method Comparison ◽  
Relative Increase ◽  
Peak Time ◽  
Time To Peak ◽  
Appearance Rate ◽  
Creatine Kinase Mb ◽  
Method Comparison Study

Abstract We compared relative increases in creatine kinase (EC 2.7.3.2) MB isoenzyme (CK-MB) after reperfusion in myocardial infarction for four popular methods: electrophoresis, immunoinhibition, the "Magic Lite" (Ciba-Corning) system, and the Stratus (Dade). In a method comparison study, we confirmed that all four methods correlated (r greater than 0.95). Electrophoresis demonstrated the greatest scatter about the regression line, immunoinhibition the least. For CK-MB quantities near each method's "positive cutoff" indicating myocardial infarction, results by all methods agreed in 95% of samples. To characterize relative increases in CK-MB, we computer-fitted data obtained from each method for serial specimens collected from six acute myocardial infarction patients during myocardial reperfusion. Although for each individual patient the four methods appeared to exhibit parallelism, the methods differed significantly in terms describing their appearance rate, peak-time & fall-off, and time-to-peak activity. Consistent with these data, we found that the relative CK-MB increases at various times after reperfusion, compared with baseline concentrations, are method-dependent. Therefore, when using CK-MB for indicating coronary patency, one must develop specific limits for each method utilized.

scholarly journals Constructed Analogs and Linear Regression

2013 ◽  
Vol 141 (7) ◽  
pp. 2519-2525 ◽  
Author(s):  
Michael K. Tippett ◽  
Timothy DelSole
Keyword(s):  
Linear Regression ◽  
Linear Combination ◽  
Ridge Regression ◽  
Principal Component Regression ◽  
Principal Component ◽  
Local Linear Regression ◽  
Analog Method ◽  
Local Linear ◽  
Nonlinear Extensions

Abstract The constructed analog procedure produces a statistical forecast that is a linear combination of past predictand values. The weights used to form the linear combination depend on the current predictor value and are chosen so that the linear combination of past predictor values approximates the current predictor value. The properties of the constructed analog method have previously been described as being distinct from those of linear regression. However, here the authors show that standard implementations of the constructed analog method give forecasts that are identical to linear regression forecasts. A consequence of this equivalence is that constructed analog forecasts based on many predictors tend to suffer from overfitting just as in linear regression. Differences between linear regression and constructed analog forecasts only result from implementation choices, especially ones related to the preparation and truncation of data. Two particular constructed analog implementations are shown to correspond to principal component regression and ridge regression. The equality of linear regression and constructed analog forecasts is illustrated in a Niño-3.4 prediction example, which also shows that increasing the number of predictors results in low-skill, high-variance forecasts, even at long leads, behavior typical of overfitting. Alternative definitions of the analog weights lead naturally to nonlinear extensions of linear regression such as local linear regression.

scholarly journals Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for Ozone (O3) Concentrations Prediction

2020 ◽  
Vol 616 ◽  
pp. 012004
Author(s):  
Nur Nazmi Liyana Mohd Napi ◽  
Mohammad Syazwan Noor Mohamed ◽  
Samsuri Abdullah ◽  
Amalina Abu Mansor ◽  
Ali Najah Ahmed ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Principal Component Regression ◽  
Principal Component

Optimized Mass Spectrometry-Based Metabolite Extraction and Analysis for the Geographical Discrimination of White Rice (Oryza sativa L.): A Method Comparison Study

2018 ◽  
Vol 101 (2) ◽  
pp. 498-506 ◽  
Author(s):  
Dong Kyu Lim ◽  
Nguyen Phuoc Long ◽  
Changyeun Mo ◽  
Ziyuan Dong ◽  
Jongguk Lim ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Extraction Method ◽  
Oryza Sativa L ◽  
Method Comparison ◽  
Principal Component ◽  
Extraction Methods ◽  
Analysis Method ◽  
White Rice ◽  
Geographical Discrimination ◽  
Method Comparison Study

Abstract In this study, we examined the effects of different extraction methods for the GC-MS- and LC-MS-based metabolite profiling of white rice (Oryza sativa L.). In addition, the metabolite divergence of white rice cultivated in either Korea or China was also evaluated. The discrimination analysis of each extraction method for white rice from Korea and China and the corresponding discriminatory markers were estimated by unpaired t-test, principal component analysis, k-means cluster analysis, partial least-squares discriminant analysis (PLS-DA), and random forest (RF). According to the prediction parameters obtained from PLS-DA and RF classifiers as well as features that could be identified, the extraction method using 75% isopropanol heated at 100°C coupled with LC-MS analysis was confirmed to be superior to the other extraction methods. Noticeably, lysophospholipid concentrations were significantly different in white rice between Korea and China, and they are novel markers for geographical discrimination. In conclusion, our study suggests an optimized extraction and analysis method as well as novel markers for the geographical discrimination of white rice.

SIMULTANEOUS ESTIMATION OF DAPAGLIFOZIN AND SAXAGLIPTIN IN TABLETS USING TWO CHEMOMETRIC-ASSISTED UV-SPECTROPHOTOMETRIC METHODS

INDIAN DRUGS ◽  
2019 ◽  
Vol 56 (03) ◽  
pp. 32-38
Author(s):  
S. S Sonawane ◽  
S. S More ◽  
S. S. Chhajed ◽  
S. J. Kshirsagar ◽  
Keyword(s):  
Linear Regression ◽  
Phosphate Buffer ◽  
Principal Component Regression ◽  
Principal Component ◽  
Simultaneous Estimation ◽  
Training Set ◽  
Spectrophotometric Methods ◽  
Accuracy And Precision ◽  
Ich Guidelines ◽  
Validation Set

Two simple, accurate, precise and economical UV spectrophotometric methods, Multiple Linear Regression (MLR) and Principal Component Regression (PCR), were developed for the simultaneous estimation of dapaglifozin (DAPA) and saxagliptin (SAXA) in tablets. Beer’s law was obeyed in the concentration ranges of 10 – 50 μg/mL for DAPA and 5 – 25 μg/mL for SAXA. Synthetic mixtures containing two drugs were prepared to build the training set and validation set in the calibration range using D-optimal mixture design in phosphate buffer pH 6.8 and were recorded at six wavelengths in the range of 230 – 215 nm at intervals of Δλ = 3 nm. Both methods were validated as per ICH guidelines with respect to the accuracy and precision and found suitable for routine analysis of tablets containing DAPA and SAXA without separation.

scholarly journals A Quantitative Analysis of Glucose from Enhanced NIR Spectra through Linear Regression Model Coupled with Optimized Bandpass Filtering

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 1010
Author(s):  
Mahbubur Rahman Mishal ◽  
Tanvir Tazul Islam ◽  
Shahadat Hossain Antor ◽  
Tanzilur Rahman
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Near Infrared ◽  
Phosphate Buffer Solution ◽  
Principal Component Regression ◽  
Principal Component ◽  
Calibration Model ◽  
Linear Regression Models ◽  
Buffer Solution ◽  
Correction Technique

This study proposes a new preprocessing technique that combines Chebyshev filtering with baseline correction technique Asymmetric Least Squares (ALS) and Savitzky-Golay transformation (SGT) to improve the prediction of Glucose from near Infrared (NIR) spectra through linear regression models Partial Least Squares (PLS) and Principal Component Regression (PCR). To investigate the performance of the proposed technique, a calibration model was first developed and then validated through prediction of Glucose from NIR spectra of a mixture of glucose, urea, and triacetin in a phosphate buffer solution where the component concentrations are within their physiological range in blood. Results indicate that the proposed technique improves the performance of both PLS and PCR and achieves standard error of prediction (SEP) as low as 12.76 mg/dL which is in the clinically acceptable level and comparable to the existing literature.

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