[Prognostic studies in cancer of the breast: optimal use of current statistical tools].
Explore the source record for details and available documents.
Breast Neoplasms↗
SEARCH · PubMed Health
Results for “Data Interpretation, Statistical”
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.
[Statistics--lying, but properly].
Explore the source record for details and available documents.
Acquired Immunodeficiency Syndrome↗
Signal generation in the New Zealand Intensive Medicines Monitoring Programme: a combined clinical and statistical approach.
The New Zealand Intensive Medicines Monitoring Programme (IMMP) undertakes prospective observational cohort studies on selected new drugs in the early postmarketing period using prescription-event monitoring (PEM) methodology with the purpose of identifying signals of previously unrecognised ADRs and establishing risk profiles for each drug. Events are reviewed by a physician and a relationship is established between each event and the drug. The events are then sorted into reactions and incidents. The latter are used to assist signal detection and control for bias. Rates for reports, reactions and incidents are used to assess the adequacy of reporting, signal detection and identification of confounders. Most signals are identified by clinical evaluation of the reports at a stage when statistical analyses are unlikely to have the power to detect them with confidence. The incident group is used for signal detection and controlling for bias. A low reporting rate indicates that certain types of event are unlikely to be reported. A systematic review of the original case reports at the site of collection provides the best opportunity for early signal detection. More resources need to be invested in the training and support of clinical evaluators. Categorising events into reactions and incidents gives added value to the data. Rates of reporting should be quoted with the results of cohort studies to facilitate assessment of their power to detect new signals.
Adverse Drug Reaction Reporting Systems↗
Statistical analysis of RNA backbone.
Local conformation is an important determinant of RNA catalysis and binding. The analysis of RNA conformation is particularly difficult due to the large number of degrees of freedom (torsion angles) per residue. Proteins, by comparison, have many fewer degrees of freedom per residue. In this work, we use and extend classical tools from statistics and signal processing to search for clusters in RNA conformational space. Results are reported both for scalar analysis, where each torsion angle is separately studied, and for vectorial analysis, where several angles are simultaneously clustered. Adapting techniques from vector quantization and clustering to the RNA structure, we find torsion angle clusters and RNA conformational motifs. We validate the technique using well-known conformational motifs, showing that the simultaneous study of the total torsion angle space leads to results consistent with known motifs reported in the literature and also to the finding of new ones.
Base Sequence↗
[Statistic pitfalls or how should we interprete numbers in the evaluation of a new treatment].
Are cholesterol lowering drugs useful? Do they increase life expectancy? Do third generation oral contraceptives increase the risk of venous thromboembolism? Is there a worldwide decline in semen quality over the last 50 years? Do vitamin supplements improve your child's IQ? Does homeopathy work better than placebo? These questions illustrate some statistical problems and some bias encountered during clinical studies, which can lead to erroneous results. Type I and II errors, surveillance, prescription or publication bias as well as the healthy user effect are described. Problems of regression to the mean, limits of meta-analysis validity and other statistical problems are discussed.
Bias↗
Clinical trials, epidemiology, and public confidence.
Critics in the media have become wary of exaggerated research claims from clinical trials and epidemiological studies. Closer to home, reviews of published studies find a high frequency of poor quality in research methods, including those used for statistical analysis. The statistical literature has long recognized that questionable research findings can occur when investigators fail to set aside their own outcome preferences as they analyse and interpret data. These preferences can be related to financial interests, a concern for patients, peer recognition, and commitment to a hypothesis. Several analyses of published papers provide evidence of an association between financial conflicts of interest and reported results. If we are to regain professional and lay confidence in research findings some changes are required. Clinical journals need to develop more competence in the review of analytic methods and provide space for thorough discussion of published papers whose results are challenged. Graduate schools need to prepare students for the conflicting interests that surround the practice of statistics. Above all, each of us must recognize our responsibility to use analytic procedures that illuminate the research issues rather than those serving special interests.
Bias↗
Pharmacology and statistics: recommendations to strengthen a productive partnership.
Critical to the discovery, development and rational use of drugs and vaccines are the foundational principles and proper application of statistics. However, in too many cases, there has been misuse of statistics and/or overemphasis on statistical significance (p < 0.05), as though this criterion possessed truth-guaranteeing properties. To clarify confusion about the proper use of statistics in pharmacology, we summarize briefly the foundational principles of probability; the role of statistics in assessment of causality; the three basic uses of statistical methods, especially those employed in hypothesis testing; and current statistical issues in pharmacological research. We then review and provide examples of the meaning of statistical significance, the consequences of lack of randomization in epidemiology/observation studies, the criteria for measurement instrument validation, the problems with subgroup analyses, the need for multiple comparison statistical methods, and how to handle dropouts and missing data. Finally, based on sound experimental and statistical principles, we make a series of recommendations to both experimentalists and journal editors to improve published pharmacological experiments. These include widespread use of blinding and randomization and/or random selection of subjects in both basic and clinical pharmacology, mandatory use of rigorous evidentiary criteria in epidemiology/observation studies claiming causal associations, proper interpretation of statistical versus clinical/pharmacological significance, appropriate interpretation of meta-analyses, meaningful validation of methods, and a more rational statistical approach to subgroup analyses and genetic association studies.
Bias↗
Cancer mortality statistics: availability and quality aspects of mortality data worldwide.
Explore the source record for details and available documents.
Data Collection↗
Statistical management of periodontal data.
Explore the source record for details and available documents.
Cross-Sectional Studies↗
A comparison of goodness of fit tests for the logistic GEE model.
Generalized estimating equations have become a popular regression method for analysing clustered binary data. Methods to assess the goodness of fit of the fitted models have recently been developed. However, evaluations and comparisons of these methods are limited. We discuss these methods and develop two additional statistics to evaluate goodness of fit. We evaluate the performance of each of the statistics with respect to type I error rates and power in a simulation study. Guidance is provided regarding appropriate use of the statistics under various scenarios.
Biometry↗
A comparison of ANB, WITS, AF-BF, and APDI measurements.
In the present study, the relationships among ANB, Wits, AF-BF, and APDI measurements used in the assessment of the anteroposterior jaw disorders were examined on the cephalometric radiographs of 63 male and 82 female subjects, and high correlations were found among them. Furthermore, relationships were explored between these parameters and some measurements that were thought to have influenced them. The results of the geometric studies could not be proved on the basis of statistical evaluation.
Adolescent↗
Rethinking John Snow's South London study: a Bayesian evaluation and recalculation.
Famously, John Snow attempted to convince a critical professional audience that public water supplied to South London residents by private companies was a principal vector for the transmission of cholera. The result has been called the sine qua non of the "epidemiological imagination," a landmark study still taught today. In fact, Snow twice attempted to prove public water supplies spread cholera to the South London population. His first, published in 1855, suffered from an incomplete data set that limited its descriptive and predictive import. In 1856, armed with new data, Snow published a more definitive study. This paper describes a previously unacknowledged methodological and conceptual problem in Snow's 1856 argument. We review the context of the South London study, identify the problem and then correct it with an empirical Bayes estimation (EBE) approach. The result hopefully revitalizes Snow's research as a teaching case through the application of a contemporary statistical approach.
Bayes Theorem↗
An online novel adaptive filter for denoising time series measurements.
A nonstationary form of the Wiener filter based on a principal components analysis is described for filtering time series data possibly derived from noisy instrumentation. The theory of the filter is developed, implementation details are presented and two examples are given. The filter operates online, approximating the maximum a posteriori optimal Bayes reconstruction of a signal with arbitrarily distributed and non stationary statistics.
Algorithms↗
Sensitivity and selectivity in protein structure comparison.
Seven protein structure comparison methods and two sequence comparison programs were evaluated on their ability to detect either protein homologs or domains with the same topology (fold) as defined by the CATH structure database. The structure alignment programs Dali, Structal, Combinatorial Extension (CE), VAST, and Matras were tested along with SGM and PRIDE, which calculate a structural distance between two domains without aligning them. We also tested two sequence alignment programs, SSEARCH and PSI-BLAST. Depending upon the level of selectivity and error model, structure alignment programs can detect roughly twice as many homologous domains in CATH as sequence alignment programs. Dali finds the most homologs, 321-533 of 1120 possible true positives (28.7%-45.7%), at an error rate of 0.1 errors per query (EPQ), whereas PSI-BLAST finds 365 true positives (32.6%), regardless of the error model. At an EPQ of 1.0, Dali finds 42%-70% of possible homologs, whereas Matras finds 49%-57%; PSI-BLAST finds 36.9%. However, Dali achieves >84% coverage before the first error for half of the families tested. Dali and PSI-BLAST find 9.2% and 5.2%, respectively, of the 7056 possible topology pairs at an EPQ of 0.1 and 19.5, and 5.9% at an EPQ of 1.0. Most statistical significance estimates reported by the structural alignment programs overestimate the significance of an alignment by orders of magnitude when compared with the actual distribution of errors. These results help quantify the statistical distinction between analogous and homologous structures, and provide a benchmark for structure comparison statistics.
Computational Biology↗
Ten categories of statistical errors: a guide for research in endocrinology and metabolism.
A simple framework is introduced that defines ten categories of statistical errors on the basis of type of error, bias or imprecision, and source: sampling, measurement, estimation, hypothesis testing, and reporting. Each of these ten categories is illustrated with examples pertinent to research and publication in the disciplines of endocrinology and metabolism. Some suggested remedies are discussed, where appropriate. A review of recent issues of American Journal of Physiology: Endocrinology and Metabolism and of Endocrinology finds that very small sample sizes may be the most prevalent cause of statistical error in this literature.
Bias↗
The use of statistics in the British Journal of Psychiatry.
BACKGROUND: Statistical error rates in the medical literature are generally high. METHOD: All papers published in the British Journal of Psychiatry in 1993 which presented numerical results were reviewed by the author for statistical errors. RESULTS: A total of 248 papers were published, of which 164 (66%) presented numerical results. Sixty-five (40% of 164) papers contained statistical errors. Many errors were not serious in nature, but some were serious enough to cast doubt on conclusions. The error rates are similar to those found in an earlier study. CONCLUSIONS: The statistical error rate is unacceptably high. There is no evidence of a change in the statistical error rate over time.
Bias↗
[Application of the time-series method to analyse the seasonal distribution of epidemic encephalitis B incidence in Guangdong province in the years of 1984-1993].
This paper analysed the data of epidemic encephalitis B incidence that was constituted to time- series and the two-ordered contingency table in Guangdong province from 1984 to 1993. Two different kinds of statistical methods of run- test and ordered-log-ratio test were applied to study the characteristics of seasonal distribution. Consistent conclusion was obtained to show that there appeared an obvious seasonal cyclic characteristic of a peak every year (June or July) for the incidence of epidemic encephalitis B. It is helpful for the epidemiologists to understand the principle and method of these statistical tests.
China↗
Random assignment of available cases: bootstrap standard errors and confidence intervals.
A frequently used experimental design in psychological research randomly divides a set of available cases, a local population, between 2 treatments and then applies an independent-samples t test to either test a hypothesis about or estimate a confidence interval (CI) for the population mean difference in treatment response. C. S. Reichardt and H. F. Gollob (1999) established that the t test can be conservative for this design-yielding hypothesis test P values that are too large or CIs that are too wide for the relevant local population. This article develops a less conservative approach to local population inference, one based on the logic of B. Efron's (1979) nonparametric bootstrap. The resulting randomization bootstrap is then compared with an established approach to local population inference, that based on randomization or permutation tests. Finally, the importance of local population inference is established by reference to the distinction between statistical and scientific inference.
Confidence Intervals↗