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PubMed2006

Frequency estimates from prescription drug datasets (revision of #04-11-066A).

PURPOSE: Accurate information about the number of times a drug is prescribed or dispensed annually is important to marketers, pharmacoepidemiologists, and patient safety researchers. Yet there is no standard reference for prescribing frequency data. The multiple sources that do exist vary in their sampling methods, target populations, nomenclature, and methods of tallying individual medications prescribed or dispensed. These differences are likely to create ambiguity and contradictions in the scientific literature, but they are not well understood. METHODS: We conducted a descriptive study to examine the similarities and differences between five well-known sources of prescribing frequency data: the National Ambulatory Medical Care Survey (NAMCS), the National Hospital Ambulatory Medical Care Survey (NHAMCS, emergency department and outpatient department), the IMS National Prescription Drug Audit, the Solucient outpatient dataset, and the Solucient inpatient dataset. We compared survey methods, costs, overall frequencies, number of unique names in each database, correlations between frequency estimates from different databases, the extent of overlap in the databases, and nomenclature differences between and within datasets. RESULTS: All the correlations between frequency estimates derived from different datasets were significant, but the frequency estimates differed considerably. The lowest correlation (0.17) was found between the IMS and emergency department of the NHAMCS, and the highest correlation (0.93) was between IMS and Solucient outpatient data. CONCLUSIONS: Although there were significant correlations between frequency estimates for comparable datasets, sampling methods and nomenclature choices resulted in important differences both for individual drug products and for overall frequency statistics. Researchers need to be aware of the differences when deriving drug frequency with these datasets.

Databases as Topic↗
PubMed2004

Utilisation of proteomics datasets generated via multidimensional protein identification technology (MudPIT).

Technological developments in proteomics have had a dramatic impact on biology in recent years. One of these developments--named multidimensional protein identification technology (MudPIT)--couples two-dimensional chromatography of peptides in mass spectrometry-compatible solutions directly to tandem mass spectrometry, allowing for the identification of proteins from highly complex mixtures. Since the initial descriptions of MudPIT, this approach has been implemented in the analysis of whole proteomes, organelles and protein complexes. Key aspects of many of the analyses are the validation of MudPIT datasets with alternate strategies and the integration of MudPIT datasets with other biochemical, cell biology or molecular biology approaches. This paper presents strategies for validating MudPIT datasets and incorporating these datasets into biologically driven experimental design.

Automation↗
PubMed2000

[Interim analysis in clinical trials: a methodological guide].

Interim analysis of data accumulated in clinical trials is one aspect of the monitoring of the study progress. It is usually done to assess whether there are significant differences in efficacy between the experimental and control treatment groups, in order to decide whether to stop or no the trial prematurely. Among many reasons for early interruption of a trial is the ethical consideration that subjects should not be exposed to an unsafe, inferior or ineffective treatment. Statistical methods suited for doing interim analysis, that allow to control the probability of incorrectly rejecting the null hypothesis of no treatment differences, are often not well understood by researchers. In this article we present an intuitive, non-mathematical explanation and review of the statistical methods for doing interim analysis in clinical trials along with an illustrative example of the application of the methods on a hypothetical dataset.

Clinical Protocols↗
PubMed2002

Comparing measures of injury severity for use with large databases.

BACKGROUND: After recent debate about the best measure of anatomic injury severity, this study aimed to compare four measures based on Abbreviated Injury Scale scores derived using ICDMAP-90-the Modified Anatomic Profile (ICD/mAP), Anatomic Profile Score (ICD/APS), Injury Severity Score (ICD/ISS), and New Injury Severity Score (ICD/NISS)-with the International Classification of Diseases-based Injury Severity Score (ICISS). METHODS: Data were selected from New Zealand public hospital discharges from 1989 to 1998. There were 349,409 patients in the dataset, of whom 3,871 had died. Models were compared in terms of their discrimination and calibration using logistic regression. Age was included as a covariate. RESULTS: The ICISS and ICD/mAP were the best performing measures. Adding age significantly improved the discrimination and calibration of almost all the models. CONCLUSION: The ICISS is a viable alternative to ICDMAP-based measures for coding anatomic injury severity on large datasets.

Calibration↗
PubMed2001

A random-effects mixture model for classifying treatment response in longitudinal clinical trials.

A random-effects regression model that allows the random coefficients to have a multivariate normal mixture distribution is described for classifying treatment response in longitudinal clinical trials. The proposed model is capable of dealing with longitudinal data from unknown heterogeneous populations. As applied to longitudinal clinical trials, for example, the model can distinguish subgroups of treatment response. Use of the proposed model is illustrated by analyzing data from two psychiatric clinical trials. The first includes depressed patients assigned to drug treatment who are repeatedly measured in terms of their level of depression. The second trial examined schizophrenic patients longitudinally who were assigned to either a drug or placebo condition. For both, the random-effects mixture model allows an assessment of whether patients comprise distinct populations in terms of their treatment response. Based on parameter estimates of the mixture model, ample evidence for a mixture of response to treatment is observed for both datasets.

Algorithms↗
PubMed2026

Understanding and making sense of epigenetic age misalignment across different aging clocks.

The output of an epigenetic aging clock can vary depending on the training method utilized, cell type composition, the nature of the training dataset, the technology used to generate the methylomic data, acute stressors, and other factors. On an individual level, epigenetic age can fluctuate across different clocks purely due to differences in model training. Among aging clock researchers, it is well-known that the epigenetic age of a single sample can vary across different models. Based on our observations and conversations with longevity scientists and stakeholders, however, this fact is often unappreciated among non-aging clock experts. To help bring more awareness to this important topic, we highlight key literature and, as an illustrative example, use eight blood-trained clocks to show that epigenetic age is frequently misaligned in a publicly available whole blood dataset. Our simple analysis revealed that the average sample difference between the youngest and oldest predicted ages across these clocks was 17 years. The smallest and largest individual-level differences observed were 4 and 45 years, respectively. Clock misalignment has implications for choosing which clock to utilize, interpreting the impact of an intervention on epigenetic age, personalized tracking, and relating epigenetic age to the abstract concept of biological age.

Humans↗
PubMedDate not supplied

Evaluation of five protocols for quantification of two-dimensional MR spectroscopic images.

Five protocols were tested for quantification on in vivo two-dimensional spectroscopic imaging datasets. The datasets are duplo measurements from three individuals. The peaks for N-acetyl aspartate, creatines, and cholines were quantified. The first of the protocols is conventional integration of a selected interval around the peak. The others are various implementations of a Gauss-Newton-based least-squares time-domain fitting algorithm. Fitting a half-echo (free-induction decay curve, FID) to a full echo was compared; this was combined with or without filtering out the water signal using the Lanczos-Hankel singular-value decomposition (LHSVD). It appears that conventional integration yields consistent and accurate results in comparison to the fitting methods. The combination of LHSVD and echo fitting performs equally well; advantages and disadvantages are discussed. The protocols using FID fitting perform poorly with these experimental datasets.

Adult↗
PubMed1991

An overview of statistical issues and methods of meta-analysis.

A meta-analysis is a statistical analysis of the data from some collection of studies in order to synthesize the results. In this paper we discuss issues that frequently arise in meta-analysis and give an overview of the methods used, with particular attention to the use of fixed- and random-effects approaches. The methods are then applied to two sample datasets.

Biopharmaceutics↗
PubMed2025

Demixer: a probabilistic generative model to delineate different strains of a microbial species in a mixed infection sample.

MOTIVATION: Multi-drug resistant or hetero-resistant tuberculosis (TB) hinders the successful treatment of TB. Hetero-resistant TB occurs when multiple strains of the TB-causing bacterium with varying degrees of drug susceptibility are present in an individual. Existing studies predicting the proportion and identity of strains in a mixed infection sample rely on a reference database of known strains. A main challenge then is to identify de novo strains not present in the reference database, while quantifying the proportion of known strains. RESULTS: We present Demixer, a probabilistic generative model that uses a combination of reference-based and reference-free techniques to delineate mixed infection strains in whole genome sequencing (WGS) data. Demixer extends a topic model widely used in text mining to represent known mutations and discover novel ones. Parallelization and other heuristics enabled Demixer to process large datasets like CRyPTIC (Comprehensive Resistance Prediction for Tuberculosis: an International Consortium). In both synthetic and experimental benchmark datasets, our proposed method precisely detected the identity (e.g. 91.67% accuracy on the experimental in vitro dataset) as well as the proportions of the mixed strains. In real-world applications, Demixer revealed novel high confidence mixed infections (101 out of 1963 Malawi samples analysed), and new insights into the global frequency of mixed infection (2% at the most stringent threshold in the CRyPTIC dataset) and its significant association to drug resistance. Our approach is generalizable and hence applicable to any bacterial and viral WGS data. AVAILABILITY AND IMPLEMENTATION: All code relevant to Demixer is available at https://github.com/BIRDSgroup/Demixer.

Mycobacterium tuberculosis↗
PubMed1999

UK monitoring of problem drug users: the drug misuse database - a system based on regional centres.

UK drug treatment policy aims to reduce the harm to individuals and communities resulting from drug misuse through multidisciplinary community services, prescribing, detoxification and rehabilitation facilities. Drug Misuse Databases (DMD) were set up in England, Wales and Scotland in 1990 to monitor treatment demand; that in Northern Ireland is under development. The regional base of these DMDs provides good local context and data specificity, while periodic collation of regional data by the Department of Health provides national analyses. DMD will be able to deliver the TDI dataset with only minor adjustments, following harmonisation between the constituent UK countries.

Databases as Topic↗
PubMed2002

Evaluation of a risk factor survey with three assessment methods.

This paper describes the evaluation of questions on a cancer risk factor survey using three different methods: dataset response patterns, qualitative feedback, and questionnaire appraisal. These methods addressed the survey data, procedures and questions. The three methods identified similar issues but also made unique contributions. Dataset response patterns showed missing and out-of-range data, an order effect, and mixed coding. Qualitative feedback revealed lack of clarity, sensitive topics, technical or undefined terms, failure to hear all response options, overlapping response options (as perceived by respondents), coding problems and recall difficulties. Questionnaire appraisal showed technical or undefined terms, complex syntax, hidden definitions, and ambiguous wording. The survey assessment methods described here can improve data quality, especially when limited time and resources preclude in-depth questionnaire development.

Data Collection↗
PubMed1993

A method to quantify deviations from assay linearity.

We present a statistical method to quantify deviations from linearity for assays that veer from linear assay responses. Our procedure handles the common case of unequally spaced analyte levels and nonconstant variance and provides a least-squares estimate with a confidence interval for the amount of deviation from assay linearity at a specified analyte concentration. This estimate of assay bias due to nonlinearity goes beyond the NCCLS EP6 lack-of-fit test, which tests for only the presence of nonlinearity. Knowing that nonlinearity is present is insufficient; users need to know the magnitude of the bias caused by nonlinearity. Our method can also be used with multifactor designs that estimate other systematic assay effects such as drift and carryover, thus obviating the need for a separate protocol to assess linearity. The procedure is carried out by adding extra columns to the design matrix corresponding to the concentration level(s) of interest. The extra columns, which replace the quadratic column, are orthogonal to all other columns. We describe a general method of constructing the new columns, and illustrate the procedure with a manual ammonia assay example dataset from EP6.

Ammonia↗
PubMed2026

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses.

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Spatial Transcriptomics↗
PubMed2001

Visualization of computer simulated heart temperature during topical cooling.

A local hypothermia is often used during cardiac operations to slow down the tissue metabolism. The heart is cooled down with cold cardioplegic solution. Sometimes even the topical cooling with or without ice slush is used. Different heart models have been used earlier to simulate heart temperature distribution, but the resolution of the model has always been limited by the speed of the computer. We used the computer heart model derived from Visible Human Dataset with the spatial resolution of 1 mm. In this article, the step-by-step generation of 3D computer heart model is described. The heat transfer was modeled by a diffusion equation that was discretised in space and time and solved by a parallel algorithm. Finally, the cooling of heart during an operation, with and without topical ice slush, was simulated on a cluster of 9 PC-based computers, which assured sufficient computing power. The simulated results are presented as images of cross sections in different planes with temperatures shown by different colors.

Body Temperature↗
PubMed1994

On closed test procedures for dose-response analysis.

This paper concerns the testing of a dose-response effect in medical studies. We consider two situations. The first is when the drug effect on a parameter of interest is likely to increase (or decrease) with increasing doses. We propose a procedure based on the closure principle of Peritz combined with an application of a closed testing procedure of Marcus et al. to a test proposed by Turkey et al. The second situation is the analysis of a drug effect where one might observe a reversal at higher doses. For this situation, we propose simultaneous examination of contrasts among the doses at each of several stages of the testing scheme. We calculate critical values for this procedure incorporating the correlation structure among the contrasts. Both procedures strongly control the family-wise error rate at the pre-determined alpha level. They also provide information about the shape of the dose response lacking in other commonly used procedures for testing against an ordered alternative hypothesis. We illustrate the procedures on two datasets.

Algorithms↗
PubMedDate not supplied

Adaptive topological tree structure for document organisation and visualisation.

The self-organising map (SOM) is finding more and more applications in a wide range of fields, such as clustering, pattern recognition and visualisation. It has also been employed in knowledge management and information retrieval. We propose an alternative to existing 2-dimensional SOM based methods for document analysis. The method, termed Adaptive Topological Tree Structure (ATTS), generates a taxonomy of underlying topics from a set of unclassified, unstructured documents. The ATTS consists of a hierarchy of adaptive self-organising chains, each of which is validated independently using a proposed entropy-based Bayesian information criterion. A node meeting the expansion criterion spans a child chain, with reduced vocabulary and increased specialisation. The ATTS creates a topological tree of topics, which can be browsed like a content hierarchy and reflects the connections between related topics at each level. A review is also given on the existing neural network based methods for document clustering and organisation. Experimental results on real-world datasets using the proposed ATTS method are presented and compared with other approaches. The results demonstrate the advantages of the proposed validation criteria and the efficiency of the ATTS approach for document organisation, visualisation and search. It shows that the proposed methods not only improve the clustering results but also boost the retrieval.

Bayes Theorem↗
PubMedDate not supplied

Combining National Health Interview Survey Datasets: issues and approaches.

This paper identifies issues, special problems, and approaches in preparing estimates when combining National Health Interview Survey (NHIS) datasets. Such datasets can be used to produce estimates jointly based on individual NHIS survey components. This paper illustrates several issues associated with the analysis of multiple related datasets.

Analysis of Variance↗
PubMed2005

Web content management by self-organization.

We present a new method for content management and knowledge discovery using a topology-preserving neural network. The method, termed topological organization of content (TOC), can generate a taxonomy of topics from a set of unannotated, unstructured documents. The TOC consists of a hierarchy of self-organizing growing chains (GCs), each of which can develop independently in terms of size and topics. The dynamic development process is validated continuously using a proposed entropy-based Bayesian information criterion (BIC). Each chain meeting the criterion spans child chains, with reduced vocabularies and increased specializations. This results in a topological tree hierarchy, which can be browsed like a table of contents directory or web portal. A brief review is given on existing methods for document clustering and organization, and clustering validation measures. The proposed approach has been tested and compared with several existing methods on real world web page datasets. The results have clearly demonstrated the advantages and efficiency in content organization of the proposed method in terms of computational cost and representation. The TOC can be easily adapted for large-scale applications. The topology provides a unique, additional feature for retrieving related topics and confining the search space.

Abstracting and Indexing↗