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At least 91 records · Page 5Linked to original sources
PubMed1981

Genetic analysis of the Stanford LRC family study data. I. Structured exploratory data analysis of height and weight measurements.

A new methodology for determining mode for inheritance of continuously distributed traits in nuclear families, structured exploratory data analysis (SEDA), is described and applied to height and weight measurements. The family data were collected as part of the Lipid Research Clinic's collaborative study (LRC) and consists of first degree relatives of Stanford University employees who were selected either as a 2% random sample or were identified through a high lipid value. The variables are all standardized using three methods of age and sex adjustment based on two reference populations. The analysis and interpretations are based on the following statistics and indices: 1) the major gene index (MGI (alpha); 2) two measures of correlations between the midparental value and offspring (MPCC); and 3) the offspring between parent functions (OBP (beta). Consistent with a number of other studies, the results support that height shows multifactorial inheritance while height is principally under the influence of non-genetic environmental factors. In contrast to the random families, the male children of the probands who were selected due to their high lipid values exhibit height measurements which appear to involve environmental components or some major gene concomitants. The difference between the random and high lipid families is supported by all three statistical methods.

Adolescent↗
PubMed2001

Studying acute confusion in long-term care: clinical investigation or secondary data analysis using the minimum data set?

Clinical investigations of acute confusion have largely been initiated in the acute care setting, where no uniform patient assessment exists. No reliable estimates of the prevalence of acute confusion in long-term care (LTC) residents have been reported. Delirium indicators are present in the nursing home Minimum Data Set (MDS), suggesting that MDS assessments could be used to facilitate studies of acute confusion in LTC. Methods to study acute confusion in LTC are discussed, with an emphasis on the advantages and disadvantages of using secondary analysis of MDS assessments as one research strategy.

Acute Disease↗
PubMed2003

Typing single-nucleotide polymorphisms using a gel-based sequencer: a new data analysis tool and suggestions for improved efficiency.

Single-nucleotide polymorphisms (SNPs) are increasingly used as genetic markers. Although a high number of SNP-genotyping techniques have been described, most techniques still have low throughput or require major investments. For laboratories that have access to an automated sequencer, a single-base extension (SBE) assay can be implemented using the ABI SNaPshot trade mark kit. Here we present a modified protocol comprising multiplex template generation, multiplex SBE reaction, and multiplex sample analysis on a gel-based sequencer such as the ABI 377. These sequencers run on a Macintosh platform, but on this platform the software available for analysis of data from the ABI 377 has limitations. First, analysis of the size standard included with the kit is not facilitated. Therefore a new size standard was designed. Second, using Genotyper (ABI), the analysis of the data is very tedious and time consuming. To enable automated batch analysis of 96 samples, with 10 SNPs each, we developed SNPtyper. This is a spreadsheet-based tool that uses the data from Genotyper and offers the user a convenient interface to set parameters required for correct allele calling. In conclusion, the method described will enable any lab having access to an ABI sequencer to genotype up to 1000 SNPs per day for a single experimenter, without investing in new equipment.

Computational Biology↗
PubMed2001

Critical assessment of microarray data analysis: the 2001 challenge.

UNLABELLED: We initiated the Critical Assessment of Microarray Data Analysis (CAMDA) conference to stimulate and evaluate the development of advanced data analysis techniques for microarrays. A standard data set has been released for this data analysis challenge. The goal of this challenge is to assess the performance of different analytical methods and at the same time to determine how such methods should be evaluated. We hope this effort will catalyze the discussion of microarray data analysis among the research community of biologists, statisticians, mathematicians, and computer scientists. AVAILABILITY: http://camda.duke.edu.

Computational Biology↗
PubMed1981

Genetic analysis of the Stanford LRC family study data. II. Structured exploratory data analysis of lipids and lipoproteins.

A newly developed methodology for the assessment of mode of inheritance of continuously distributed traits in nuclear families, structured exploratory data analysis (SEDA), is applied to lipid and lipoprotein measurements. Specifically, three measures: the major gene index (MGI), the pairwise midparental correlation coefficient (MPCC), and the offspring between parents (OBP) curves are used to determine whether these trait expressions are more major gene, sporadic, or multifactorial relative to one another. Triglycerides and the closely-related VLDL-cholesterol measurement appears to be transmitted through some major components. HDL-cholesterol measurements are more consistent with some degree of multifactorial transmission or a major gene model with additive allelic effects and similar allele frequencies. The results for LDL-cholesterol suggest a modicum of major gene influences. The composite total cholesterol measurement appears to be under multifactorial transmission but of lower order than height. Younger families who were selected through a parent with high lipid levels exhibit some major gene influences which do not appear in the older families. Several possible explanations are proposed for this difference.

Cholesterol↗
PubMed1993

Methodologic research needs in environmental epidemiology: data analysis.

A brief review is given of data analysis methods for the identification and quantification of associations between environmental exposures and health events of interest. Data analysis methods are outlined for each of the study designs mentioned, with an emphasis on topics in need of further research. Particularly noted are the need for improved methods for accommodating exposure assessment measurement errors in analytic epidemiologic studies and for improved methods for the conduct and analysis of aggregate data (ecologic) studies.

Data Interpretation, Statistical↗
PubMed2006

Patterns of dosage changes with transdermal buprenorphine and transdermal fentanyl for the treatment of noncancer and cancer pain: a retrospective data analysis in Germany.

BACKGROUND: Previous studies have suggested that buprenorphine may have a low association with tolerance development compared with other strong opioids. In a previous study by our group, mean cohort and intraindividual dosage increases over an entire course of treatment and on a per-day basis were significantly lower with transdermal (TD) buprenorphine than with TD fentanyl. However, no information concerning the relationship between qualitative and quantitative dose changes is available. OBJECTIVE: The aim of this study was to compare TD buprenorphine and TD fentanyl with respect to dosage increases, dosage stability, and the nature of dosage changes. METHODS: This retrospective analysis used data from the IMS Disease Analyzer-Mediplus database, which contains patient-related data documented by 400 medical practices in Germany. Data from patients with noncancer or cancer pain treated with TD buprenorphine or TD fentanyl for at least 3 months between May 2002 and April 2005 were analyzed. Daily dosages were directly determined from the prescribed patch strength, taking into account the possibility of multiple patches applied simultaneously. To determine dosage stability, patients were classified based on the type of dosage change (stable, increase, alternating, or decrease) of the prescribed dosages. From the prescribed daily dosages, mean percentage increases were calculated on a per-patient basis for the entire treatment period and per day, and these were assessed in relation to the type of dosage change. RESULTS: In total, 631 patients with noncancer pain and 605 patients with cancer pain were included in the analysis (782 women, 454 men; mean age, 76.3 years [range, 29-100 years]). Treatment indications included osteoarthritis, low back pain, osteoporosis (noncancer groups), and neoplasm (cancer groups). Patients had similar analgesic premedication requirements based on steps 1 to 3 of the World Health Organization analgesic ladder. Comedication requirements for breakthrough pain were also similar between the TD buprenorphine and TD fentanyl groups. The mean percentage increases per day were 0.10% (TD buprenorphine) and 0.25% (TD fentanyl) in the noncancer groups and 0.19% (TD buprenorphine) and 0.47% (TD fentanyl) in the cancer groups (both, P < 0.05). A significantly larger proportion of patients receiving TD buprenorphine had stable dosages over the entire treatment period compared with patients receiving TD fentanyl (noncancer groups: 56.9% vs 41.6%; cancer groups: 50.0% vs 26.2% [both, P < 0.05]). Compared with TD buprenorphine, the proportion of patients with alternating dosage changes was significantly greater in patients receiving TD fentanyl (noncancer groups: 22.7% vs 13.1%; cancer groups: 30.6% vs 11.8% [both, P < 0.05]). CONCLUSIONS: In this retrospective data analysis, compared with TD buprenorphine, the increase in mean daily dosage was significantly greater in patients treated with TD fentanyl. Also, compared with TD buprenorphine, alternating dosage changes were seen in a significantly greater proportion of patients receiving TD fentanyl. On the other hand, a significantly greater proportion of patients treated with TD buprenorphine had stable dosages over their entire treatment periods.

Administration, Cutaneous↗
PubMed1996

Evidence that Lp[a] contains one molecule of apo[a] and one molecule of apoB: evaluation of amino acid analysis data.

Amino acid analysis was performed on four Lp[a] preparations to evaluate whether or not the amino acid data was consistent with Lp[a] containing one molecule of apolipoprotein[a] [apo(a)] linked to one molecule of apoB-100. Amino acid analysis was carried out in duplicate on a Beckman model 121 amino acid analyzer. Apo[a] size was determined by a high-resolution agarose gel electrophoretic method that provides an estimate of apo[a] kringle 4 repeats. When Lp[a] was assumed to contain one apo[a] and one apoB molecule per particle, the average absolute bias between the expected molar percentage of each amino acid, as based on the known sequence of apo[a] and apoB, and the obtained molar percentage ranged from 2 to 3.5%. In contrast, by assuming two molecules of apo[a] and one of apoB per Lp[a] particle, the bias between the expected and observed molar percentage ranged from 8.5% to 10%, and by assuming one apo[a] and two apoB the bias ranged from 8.8% to 11.4%. Comparison of Lp[a] concentrations, calculated from six stable amino acids and the Lp[a] composition predicted from the known sequence, was in excellent agreement (bias ranging from 0.3% to 0.9%) with the Lp[a] concentration calculated from the sum of the amino acid concentrations, when Lp[a] was assumed to contain one molecule of apo[a] and one molecule of apoB. However, there was poor agreement (7.4% to 8.4% bias) when it was assumed that Lp[a] contains two molecules of apo[a] and one molecule of apoB. These results indicate that the evaluated Lp[a] preparations contain one apo[a] per Lp[a] particle. Evaluation of amino acid analysis data provides a relatively simple approach to determine the molar ratio of apoB to apo[a] in Lp[a] and provides evidence that Lp[a] contains one molecule of apo[a] and one molecule of apoB.

Amino Acids↗
PubMed2004

New challenges in gene expression data analysis and the extended GEPAS.

Since the first papers published in the late nineties, including, for the first time, a comprehensive analysis of microarray data, the number of questions that have been addressed through this technique have both increased and diversified. Initially, interest focussed on genes coexpressing across sets of experimental conditions, implying, essentially, the use of clustering techniques. Recently, however, interest has focussed more on finding genes differentially expressed among distinct classes of experiments, or correlated to diverse clinical outcomes, as well as in building predictors. In addition to this, the availability of accurate genomic data and the recent implementation of CGH arrays has made mapping expression and genomic data on the chromosomes possible. There is also a clear demand for methods that allow the automatic transfer of biological information to the results of microarray experiments. Different initiatives, such as the Gene Ontology (GO) consortium, pathways databases, protein functional motifs, etc., provide curated annotations for genes. Whereas many resources on the web focus mainly on clustering methods, GEPAS has evolved to cope with the aforementioned new challenges that have recently arisen in the field of microarray data analysis. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://gepas.bioinfo.cnio.es.

Gene Expression Profiling↗
PubMed1993

Evaluating the effects of tubal sterilization on menstrual function: selected issues in data analysis.

We examined selected issues in data analysis in the Collaborative Review of Sterilization (CREST). CREST is a multicentre, prospective, observational study of women undergoing tubal sterilization. We analysed menstrual function after sterilization in over 5000 women who were enrolled in the period 1978-1983 and followed for 5 years with yearly follow-up interviews. To take into account the dependency among repeated responses from the same individuals, we used the generalized estimating equations (GEE) approach to longitudinal data analysis. Marginal modelling resulted in a statistically significant increase in the odds of menstrual dysfunction at 5 years after tubal sterilization. Transitional modelling produced rates of menstrual dysfunction given a woman's menstrual function at baseline, after adjusting for other baseline characteristics such as method of contraception before sterilization. To examine the direction of the bias that could result from non-random missing data, we refitted our models using imputed values. The models with imputed values showed the same trends as the original models.

Bias↗
PubMedDate not supplied

Proteomic 2DE database for spot selection, automated annotation, and data analysis.

We present a software solution that enables faster and more accurate data analysis of 2DE/MALDI TOF MS data. The software supports data analysis through a number of automated data selection functions and advanced graphical tools. Once protein identities are determined using MALDI TOF MS, automated data retrieval from online databases provides biological information. The software, called 2DDB, reduces analysis time to a fraction without losing any quality compared to more manual data analysis. The database contains over 100,000 data entries, and selected parts can be reached at http://2ddb.org.

Animals↗
PubMed1998

Data analysis in multiple-frequency bioelectrical impedance analysis.

The performance of three analytical methods for multiple-frequency bioelectrical impedance analysis (MFBIA) data was assessed. The methods were the established method of Cole and Cole, the newly proposed method of Siconolfi and co-workers and a modification of this procedure. Method performance was assessed from the adequacy of the curve fitting techniques, as judged by the correlation coefficient and standard error of the estimate, and the accuracy of the different methods in determining the theoretical values of impedance parameters describing a set of model electrical circuits. The experimental data were well fitted by all curve-fitting procedures (r = 0.9 with SEE 0.3 to 3.5% or better for most circuit-procedure combinations). Cole-Cole modelling provided the most accurate estimates of circuit impedance values, generally within 1-2% of the theoretical values, followed by the Siconolfi procedure using a sixth-order polynomial regression (1-6% variation). None of the methods, however, accurately estimated circuit parameters when the measured impedances were low (< 20 omega) reflecting the electronic limits of the impedance meter used. These data suggest that Cole-Cole modelling remains the preferred method for the analysis of MFBIA data.

Body Composition↗
PubMed2005

BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis.

biomaRt is a new Bioconductor package that integrates BioMart data resources with data analysis software in Bioconductor. It can annotate a wide range of gene or gene product identifiers (e.g. Entrez-Gene and Affymetrix probe identifiers) with information such as gene symbol, chromosomal coordinates, Gene Ontology and OMIM annotation. Furthermore biomaRt enables retrieval of genomic sequences and single nucleotide polymorphism information, which can be used in data analysis. Fast and up-to-date data retrieval is possible as the package executes direct SQL queries to the BioMart databases (e.g. Ensembl). The biomaRt package provides a tight integration of large, public or locally installed BioMart databases with data analysis in Bioconductor creating a powerful environment for biological data mining.

Algorithms↗
PubMedDate not supplied

DNA microarrays: experimental issues, data analysis, and application to bacterial systems.

DNA microarrays are currently used to study the transcriptional response of many organisms to genetic and environmental perturbations. Although there is much room for improvement of this technology, its potential has been clearly demonstrated in the past 5 years. The general consensus is that the bottleneck is now located in the processing and analysis of transcriptome data and its use for purposes other than the quantification of changes in gene expression levels. In this article we discuss technological aspects of DNA microarrays, statistical and biological issues pertinent to the design of microarray experiments, and statistical tools for microarray data analysis. A review on applications of DNA microarrays in the study of bacterial systems is presented. Special attention is given to studies in the following areas: (1) bacterial response to environmental changes; (2) gene identification, genome organization, and transcriptional regulation; and (3) genetic and metabolic engineering. Soon, the use of DNA microarray technologies in conjunction with other genome/system-wide analyses (e.g., proteomics, metabolomics, fluxomics, phenomics, etc.) will provide a better assessment of genotype-phenotype relationships in bacteria, which serve as a basis for understanding similar processes in more complex organisms.

Algorithms↗
PubMed2026

An introductory practical guide to secondary data analysis in pediatric urology.

INTRODUCTION: Secondary data analysis (SDA) has become an increasingly important approach in pediatric urology, enabling the study of long-term outcomes, care variation, and disparities in populations with chronic or congenital urologic conditions. With the growing availability of large datasets, a structured approach to designing and conducting SDA studies is increasingly relevant. OBJECTIVES: To provide an introductory, practical guide to SDA in pediatric urology by (1) summarizing commonly used data sources with representative studies, (2) outlining a stepwise approach to designing and executing SDA studies, and (3) highlighting key methodological considerations, limitations, and opportunities for future work. STUDY DESIGN: Narrative review of existing literature and commonly used datasets relevant to pediatric urology, including administrative claims, hospital encounter databases, clinical registries, electronic health record networks, and population-based surveys. RESULTS: Data sources differ in scope, clinical granularity, longitudinal follow-up, and representativeness, and each is suited to specific research questions. We present a practical workflow for SDA, including dataset selection, cohort definition, and analytic planning. Linkage across datasets can provide a more comprehensive view of care patterns and outcomes, although feasibility is influenced by legal, technical, and data-quality constraints. DISCUSSION: SDA enables population-level analyses and the study of rare conditions that are challenging to evaluate through single-center or prospective designs. However, careful cohort definition, feasibility assessment, and awareness of data limitations are essential to ensure validity and interpretability. CONCLUSION: SDA provides a scalable, cost-efficient framework for generating meaningful evidence in pediatric urology. Continued efforts to harmonize data elements, improve linkage infrastructure, and support cross-institution collaboration will enhance the quality and impact of future research. This article provides a practical framework and examples to support the design and execution of SDA studies.

Humans↗
PubMed1981

Limiting dilution assays for the determination of immunocompetent cell frequencies. I. Data analysis.

A statistical method was developed for the analysis of experimental data from limiting dilution assays. Formulas for the estimation of the frequency of immunocompetent cells within a test population were derived by the statistical methods of weighted averaging, likelihood maximization, and X2 minimization. Equations for the latter 2 were solved by Newton's method of iterative approximation. Estimates obtained by these methods were found to be more valid than those obtained by least squares (LS) fitting as judged by the X2 test and as established by Monte Carlo experiments. X2 minimization was chosen as the preferable estimation method with maximum accuracy and precision (minimum bias and variance) for the standard determination of frequencies; likelihood maximization was used only for the confirmation of results. When data from previously published experiments were reanalyzed, both results and conclusions were found to differ significantly from those originally obtained by LS fitting, thus demonstrating the importance of using proper data analysis methods. In conjunction with the use of available calculators or microcomputers, the method presented here provides a simple and rapid procedure for the valid determination of immunocompetent cell frequencies.

Immunity, Cellular↗
PubMed1994

Decision trees: helping the postanesthesia nurse to plan data analysis.

An important step in the research process is statistical analysis of the data. Data analysis should be planned as variables are identified, as level of measurement is determined, and as research questions or hypotheses are generated. The use of decision trees can assist the PACU nurse in determining which statistical tests are appropriate for the level of variable measurement in the study. The use of decision trees also can assist the PACU nurse when consulting with experts regarding appropriate statistical analysis.

Decision Trees↗