PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Data Analytics”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed2001

Factor transformation to produce statistics describing the uncertainty of analytical data.

Analytical uncertainty produced by random error has a positively skewed distribution and accuracy and precision have non-linear scales. Compared with conventional statistics, factor transformation of the data allows more appropriate interpretation of results and facilitates graphical inspection of data. Statistics are compared for practical examples of performance in proficiency tests and of repeatability and reproducibility in collaborative studies. Factor transformation is shown to be applicable to wide ranges of analyte concentration and measurement precision.

Chromatography, Gas↗
PubMed2000

Sequential projection pursuit using genetic algorithms for data mining of analytical data.

Sequential projection pursuit (SPP) is proposed to detect inhomogeneities (clusters) in high-dimensional analytical data. Such inhomogeneities indicate that there are groups of objects (samples) with different chemical characteristics. The method is compared with principal component analysis (PCA). PCA is generally applied to visually explore structure in high-dimensional data, but is not specifically used to find clustering tendency. Projection pursuit (PP) is specifically designed to find inhomogeneities, but the original method is computationally very intensive. SPP combines the advantages of both methods and overcomes most of their weak points. In this method, latent variables are obtained sequentially according to their importance measured by the entropy index. This involves an optimization step, which is achieved by using a genetic algorithm. The performance of the method is demonstrated and evaluated, first on simulated data sets, and then on near-infrared and gas chromatography data sets. It is shown that SPP indeed reveals more easily information about inhomogeneities than PCA.

Algorithms↗
PubMed2003

A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection.

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Algorithms↗
PubMed2005

Validation in pharmaceutical analysis. Part II: Central importance of precision to establish acceptance criteria and for verifying and improving the quality of analytical data.

Validation of analytical procedures is a vital aspect not just for regulatory purposes, but also for their efficient and reliable long-term application. In order to address the performance of the analytical procedure adequately, the analyst is responsible to identify the relevant parameters, to design the experimental validation studies accordingly and to define appropriate acceptance criteria. Establishing an acceptable analytical variability for the given application is of central importance as many other acceptance criteria can be derived from such a precision. Acceptable precision ranges for types of control tests and/or analytes can be obtained from validation, but also related activities such as transfer, control charts, or extracted from routine applications such as batch release or stability studies (data mining). Apart from compiling a database for general benchmarking, during such an information-building process, the reliability of the analytical variability of the specific procedure is more and more increased. This is important as a reliable target variability facilitates to detect or investigate atypical or out-of specification behaviour of analytical data in a routine application, thus improving the data quality and reliability. According to the life-cycle concept of validation, measures should be taken to maintain and control the validated status of analytical procedures during long-term routine application, such as monitoring relevant performance parameters (system suitability tests), control charts, etc. If the analytical system is demonstrated to be stable, i.e. under statistical control, a major variability contribution in LC originating from the standard preparation and analysis can be reduced. A concept of quantification by pre-determined calibration parameters instead of the classical approach of simultaneous calibration is described.

Chemistry, Pharmaceutical↗
PubMed1989

Statistical estimation of analytical data distributions and censored measurements.

A numerical method was developed for estimating the shapes of unknown distributions of analytical data and for estimating the expected values of censored data points. The method is based conceptually on the normal probability plot. Data are ordered and then transformed by using a power function to achieve approximate linearity with respect to a computed normal cumulative probability scale. The exponent used in the power transformation is an index of the distribution shape, which covers a continuum on which normality is defined as d = 1 and log normality is defined as d = 0. Expected transformed values of censored points are computed from a straight line fitted to the transformed, accepted data, and these are then back-transformed to the original distribution. The method gives improved characterization of analytical data distributions, particularly in the distribution extremities. It also avoids the biases from improper handling of censored data arising from measurements near the analytical detection limit. Illustrative applications were computed for atmospheric SO2 data and for mineral concentrations in hamburgers.

Least-Squares Analysis↗
PubMed1990

Empirical considerations in orthopaedic research design and data analysis. Part II: The application of data analytic techniques.

To assure that a hypothesis is tested as rigorously as possible, the proper statistical method must be used to analyze the data. But without a strong background in statistics, it may be difficult to determine the efficacy of the data analytic technique used in the study. This paper describes several widely used data analytic techniques and offers examples of their proper application in orthopaedic research design.

Data Interpretation, Statistical↗
PubMedDate not supplied

An analytic data analysis method for oscillatory slug tests.

An analytical data analysis method is developed for slug tests in partially penetrating wells in confined or unconfined aquifers of high hydraulic conductivity. As adapted from the van der Kamp method, the determination of the hydraulic conductivity is based on the occurrence times and the displacements of the extreme points measured from the oscillatory data and their theoretical counterparts available in the literature. This method is applied to two sets of slug test response data presented by Butler et al.: one set shows slow damping with seven discernable extremities, and the other shows rapid damping with three extreme points. The estimates of the hydraulic conductivity obtained by the analytic method are in good agreement with those determined by an available curve-matching technique.

Models, Theoretical↗
PubMed2005

Benford's Law and the screening of analytical data: the case of pollutant concentrations in ambient air.

The need to ensure the robustness of very large data sets produced by analytical measurement processes is increasing. This requires data screening techniques that can identify formatting or transcription errors in large data sets, that have undergone multiple data-handling and manipulation procedures. The empirical observation that the digits 1 to 9 are not equally likely to appear as the initial digit in multi-digit numbers is known as Benford's Law, and may provide a solution to this requirement. Several sets of data pertaining to the measured concentrations of pollutants in ambient air in the UK in 2004 have been analysed for their initial digit frequencies in order to assess the potential for the use of Benford's Law as a data screening, and authenticity-checking, tool for these types of analytical data sets. Benford's Law has been shown to be a robust top-level data screening tool provided that the numerical range of the data set being considered is four orders of magnitude or greater. It has been shown that small changes in the deviation of a data set from Benford's Law may indicate the introduction of errors during data processing. In this way, Benford's Law provides a sensitive technique for identifying data mishandling in large data sets.

Air Pollutants↗
PubMed1996

Chemometrical approaches to evaluate analytical data from aquatic macrophytes and marine algae.

The possibility of using aquatic macrophytes and algae as bioindicators of water pollution is investigated. The use of cluster analysis enables the treatment of the analytical data in a global way, revealing hidden clusters of similar behaviour between various biological species or sampling sites. A great number of species was collected from four rivers and fourteen coastal sites in Greece and were analysed for zinc, copper, cadmium, lead and manganese. The analytical data obtained were treated by hierarchical clustering, for marine algae and aquatic macrophytes separately.

Journal Article↗
PubMed1979

Statistical and other data-analytic techniques for the evaluation researcher: an identification, classification, and description of methods and resources.

The goal of any social/health intervention program is to improve the lot of the people it is designed to serve. Of critical importance is the development and implementation of programs able to achieve such a goal is evaluation research (ER)--a process representing an interface between the generic notion of evaluation and the rigor of social research methodology. The evaluation researcher should be familiar with, and be capable of using, any of a number of (statistical and non-statistical) data-analytic techniques. The objectives of this discussion, therefore, are to (1) identify, categorize, and briefly describe statistical and other data-analytic techniques of potential use to the evaluation researchers; and (2) identify currently available resources, i.e., texts, books of readings, monographs, etc., that offer discussions and analyses of these techniques. It is hoped that such an exposition will lead to a wider understanding, acceptance, and use of these procedures, which can only enhance the quality of subsequent program policy- and decisionmaking.

Analysis of Variance↗
PubMedDate not supplied

Data analytic techniques for treatment outcome studies with pretest/posttest measurements: an extensive primer.

This paper discusses several data analytic technique, for examining treatment efficacy in pretest-posttest control group designs. The following approaches are described: ANOVA on post scores, ANOVA on difference scores, split-plot repeated measures ANOVA, profile analysis, and ANOCOVA with prescore as the co-variate. Guidelines for choosing between available techniques are provided; the primary focus here is on the nature of the null hypothesis, the assumptions underlying the approach, and the power of the procedure. The importance of examining the characteristics of the data set in selecting an analytic technique is illustrated.

Analysis of Variance↗
PubMed2004

Descriptive analytical data and consequences for calculation of common reference intervals in the Nordic Reference Interval Project 2000.

In the Nordic Reference Interval Project (NORIP), data from 102 Nordic clinical chemical laboratories were obtained. Each laboratory reported analytical data on up to 25 of the most commonly used clinical biochemical properties, including results from each of a minimum of 25 reference individuals. A reference material consisting of a liquid frozen pool of serum with values traceable to reference methods (used as the project "calibrator" for non-enzymes to correct reference values) was measured together with other serum pool controls in each laboratory in the same analytical series as the project samples. The data on the controls were used to evaluate the analytical quality of the routine methods. For reference interval calculations, only such reference values on enzymes were accepted that were obtained by applying the International Federation of Clinical Chemistry (IFCC) compatible methods (37 degrees C), while "calibrator"-corrected reference values were used in the cases of non-enzymes. For each property, gender- and age-specific reference intervals were estimated, based on simple non-parametric calculations and using objective criteria to perform partitioning into subgroups. It is concluded that the same reference intervals are applicable in all five Nordic countries. The following descriptive data for the considered properties are presented in the tables: number of measurement values from each country and measurement system, certified/indicative target values for controls, differences between methods and measurement systems together with coefficients of variation, effects of control correction on the measurement values, differences between subgroups as determined by age, gender, country and material, and comparison of the new reference intervals with those presented in standard textbooks. The 25 components involved in this project were (listed in alphabetical order): Alanine transaminase, albumin, alkaline phosphatase, amylase, amylase pancreatic type, aspartate transaminase, bilirubin, calcium, carbamide, cholesterol, creatine kinase, creatininium, gamma-glutamyltransferase, glucose, HDL-cholesterol, iron, iron-binding capacity, lactate dehydrogenase, magnesium, phosphate, potassium, protein, sodium, triglyceride and urate.

Blood Chemical Analysis↗
PubMed1997

[Clenbuterol poisoning. Clinical and analytical data on an outbreak in Móstoles, Madrid].

OBJECTIVE: To report the clinical manifestations and analytical findings in an epidemic outbreak of acute food poisoning with clenbuterol. MATERIALS AND METHODS: The clinical manifestations, physical examination findings and results of complementary tests are reported of fifteen patients performed by veal liver contaminated with clenbuterol. The clinical course of patients at 72 hours is reported. A quantitative measurement of clenbuterol in urine specimens from patients and in a veal liver specimen was performed by high pressure liquid chromatography (HPLC). RESULTS: The male/female distribution of patients was 7/8 respectively, with age ranging from 6 to 44 years. Symptoms appeared after 30 minutes to 2 hours of having ingested veal liver in 93% of cases. Patients presented at the Emergency Department with tremors, palpitations, anxiety, malaise, nausea, and pruritus as the most common complaints. On physical examination tachycardia was noted in 100% of cases. The analytical data included mild hypokaliemia (66%) and leukocytosis (28%). Only one patient required hospital admission on account of an hypertensive crisis. After 72 hours, 67% of patients were asymptomatic. The remaining patients had mild symptoms which included headache, myalgia, asthenia and anorexia. Serum potassium values returned to normality (p < 0.05). Urine measurements of clenbuterol were positive for all analyzed cases (50 +/- 42 ng/ml). The concentration of clenbuterol in a veal liver sample was 500 ppb. CONCLUSION: Clenbuterol poisoning should be suspected when symptoms of adrenergic hyperstimulation occur after the ingestion of meal, usually liver. Common symptoms include tachycardia and mild hypopotasemia. Diagnosis is confirmed by quantitative measurement of clenbuterol in urine. Most patients improve spontaneously shortly afterwards.

Adolescent↗
PubMed2003

Methodological challenges in research on sexual risk behavior: I. Item content, scaling, and data analytical options.

Investigation of sexual behavior involves many challenges, including how to assess sexual behavior and how to analyze the resulting data. Sexual behavior can be assessed using absolute frequency measures (also known as counts) or with relative frequency measures (e.g., rating scales that range from never to always). We discuss these 2 assessment approaches in the context of research on HIV risk behavior. We conclude that these 2 approaches yield nonredundant information and, more important, that only data yielding information about the absolute frequency of risk behavior have the potential to serve as valid indicators of HIV contraction risk. However, analyses of count data may be challenging because of non-normal distributions with many outliers. Therefore, we identify new and powerful data analytical solutions that have been developed recently to analyze count data and discuss limitations of a commonly applied method (viz., analysis of covariance using baseline scores as covariates).

Data Collection↗
PubMed2005

Driving under the influence of drugs -- evaluation of analytical data of drugs in oral fluid, serum and urine, and correlation with impairment symptoms.

A study was performed to acquire urine, serum and oral fluid samples in cases of suspected driving under the influence of drugs of abuse. Oral fluid was collected using a novel sampling/testing device (Dräger DrugTest System). The aim of the study was to evaluate oral fluid and urine as a predictor of blood samples positive for drugs and impairment symptoms. Analysis for cannabinoids, amphetamine and its derivatives, opiates and cocaine was performed in urine using the Mahsan Kombi/DOA4-test, in serum using immunoassay and gas chromatography-mass spectrometry (GC-MS) confirmation and in oral fluid by GC-MS. Police and medical officer observations of impairment symptoms were rated and evaluated using a threshold value for the classification of driving inability. Accuracy in correlating drug detection in oral fluid and serum were >90% for all substances and also >90% in urine and serum except for THC (71.0%). Of the cases with oral fluid positive for any drug 97.1% of corresponding serum samples were also positive for at least one drug; of drug-positive urine samples this were only 82.4%. In 119 of 146 cases, impairment symptoms above threshold were observed (81.5%). Of the cases with drugs detected in serum, 19.1% appeared not impaired which were the same with drug-positive oral fluid while more persons with drug-positive urine samples appeared uninfluenced (32.7%). The data demonstrate that oral fluid is superior to urine in correlating with serum analytical data and impairment symptoms of drivers under the influence of drugs of abuse.

Amphetamines↗
PubMed1979

[Analytical data and preparation of important 3-hydroxy-5-phenyl-1,4-benzodiazepin-2-one derivatives (author's transl)].

The article describes analytical data (TLC, IR, MS) of 7-chloro-1-methyl-1,3-hydroxy-5-phenyl-2H-1,4-benzodiazepin-2-one (3-hydroxydiazepam) and 7-cyclopropyl-methyl-1,3-dihydro-3-hydroxy-5-phenyl-2H-1,4-benzodiazpin-2-one (3-hydroxy-prazepam). Analytical properties of important derivatives (N-oxides, 3-acetoxy compounds, re-arrangement products) are also reported. Besides preparation methods are given for the synthesis of these substances, considering in particular preparation as solution-reactions as well as reaction on the TLC-plate.

Benzodiazepinones↗
PubMed1993

Carotenoid content of fruits and vegetables: an evaluation of analytic data.

The test of the association between dietary intake of specific carotenoids and disease incidence requires the availability of accurate and current food composition data for individual carotenoids. To generate a carotenoid database, an artificial intelligence system was developed to evaluate data for carotenoid content of food in five general categories, namely, number of samples, analytic method, sample handling, sampling plan, and analytic quality control. Within these categories, criteria have been created to rate analytic data for beta-carotene, alpha-carotene, lutein, lycopene, and beta-cryptoxanthin in fruits and vegetables. These carotenoids are also found in human blood. Following the evaluation of data, acceptable values for each carotenoid in the foods were combined to generate a database of 120 foods. The database includes the food description; median, minimum, and maximum values for the specific carotenoids in each food; the number of acceptable values and their references; and a confidence code, which is an indicator of the reliability of a specific carotenoid value for a food. The carotenoid database can be used to estimate the intake of specific carotenoids in order to examine the association between dietary carotenoids and disease incidence.

Carotenoids↗