Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “STATISTICS”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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

At least 433 records · Page 24Linked to original sources
PubMedDate not supplied

Is risk-adjustor selection more important than statistical approach for provider profiling? Asthma as an example.

OBJECTIVES: To examine how the selections of different risk adjustors and statistical approaches affect the profiles of physician groups on patient satisfaction. DATA SOURCES: Mailed patient surveys. Patients with asthma were selected randomly from each of 20 California physician groups between July 1998 and February 1999. A total of 2515 patients responded. RESEARCH DESIGN: A cross-sectional study. Patient satisfaction with asthma care was the performance indicator for physician group profiling. Candidate variables for risk-adjustment model development included sociodemographic, clinical characteristics, and self-reported health status. Statistical strategies were the ratio of observed-to-expected rate (OE), fixed effects (FE), and the random effects (RE) approaches. Model performance was evaluated using indicators of discrimination (C-statistic) and calibration (Hosmer-Lemeshow chi2). Ranking impact of using different risk adjustors and statistical approaches was based on the changes in absolute ranking (AR) and quintile ranking (QR) of physician group performance and the weighted kappa for quintile ranking. RESULTS: Variables that added significantly to the discriminative power of risk-adjustment models included sociodemographic (age, sex, prescription drug coverage), clinical (asthma severity), and health status (SF-36 PCS and MCS). Based on an acceptable goodness-of-fit (P > 0.1)and higher C-statistics, models adjusting for sociodemographic, clinical, and health status variables (Model S-C-H) using either the FE or RE approach were more favorable. However, the C-statistic (=0.68) was only fair for both models. The influence of risk-adjustor selection on change of performance ranking was more salient than choice of statistical strategy (AR: 50%-80% v. 20%-55%; QR: 10%-30% v. 0%-10%). Compared to the model adjusting for sociodemographic and clinical variables only and using OE approach, the Model S-C-H using RE approach resulted in 70% of groups changing in AR and 25% changing in QR (weighted kappa: 0.88). Compared to the Consumer Assessment of Health Plans model, the Model S-C-H using RE approach resulted in 65% of groups changing in AR and 20% changing in QR (weighted kappa: 0.88). CONCLUSIONS: In comparing the performance of physician groups on patient satisfaction with asthma care, the use of sociodemographic, clinical, and health status variables maximized risk-adjustment model performance. Selection of risk adjustors had more influence on ranking profiles than choice of statistical strategies. Stakeholders employing provider profiling should pay careful attention to the selection of both variables and statistical approach used in risk-adjustment.

Adult↗
PubMed2000

A critique of Rasch residual fit statistics.

In test analysis involving the Rasch model, a large degree of importance is placed on the "objective" measurement of individual abilities and item difficulties. The degree to which the objectivity properties are attained, of course, depends on the degree to which the data fit the Rasch model. It is therefore important to utilize fit statistics that accurately and reliably detect the person-item response inconsistencies that threaten the measurement objectivity of persons and items. Given this argument, it is somewhat surprising that there is far more emphasis placed in the objective measurement of person and items than there is in the measurement quality of Rasch fit statistics. This paper provides a critical analysis of the residual fit statistics of the Rasch model, arguably the most often used fit statistics, in an effort to illustrate that the task of Rasch fit analysis is not as simple and straightforward as it appears to be. The faulty statistical properties of the residual fit statistics do not allow either a convenient or a straightforward approach to Rasch fit analysis. For instance, given a residual fit statistic, the use of a single minimum critical value for misfit diagnosis across different testing situations, where the situations vary in sample and test properties, leads to both the overdetection and underdetection of misfit. To improve this situation, it is argued that psychometricians need to implement residual-free Rasch fit statistics that are based on the number of Guttman response errors, or use indices that are statistically optimal in detecting measurement disturbances.

Aptitude Tests↗
PubMed2007

Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: research needs.

The objective of this report is to provide a basis to inform decisions about priorities for developing statistical research initiatives in the field of public health surveillance for emerging threats. Rapid information system advances have created a vast opportunity of secondary data sources for information to enhance the situational and health status awareness of populations. While the field of medical informatics and initiatives to standardize healthcare-seeking encounter records continue accelerating, it is necessary to adapt analytic and statistical methodologies to mature in sync with sibling information science technologies. One major right-of-passage for statistical inference is to advance the optimal application of analytic methodologies for using multiple data streams in detecting and characterizing public health population events of importance. This report first describes the problem in general and the data context, then delineates more specifically the practical nature of the problem and the related issues. Approaches currently applied to data with time-series, statistical process control and traditional inference concepts are described with examples in the section on Statistics and the Role of the Analytic Surveillance Data Monitor. These are the techniques that are providing substance to surveillance professionals and enabling use of multiple data streams. The next section describes use of a more complex approach that takes temporal as well as spatial dimensions into consideration for detection and situational awareness regarding event distributions. The space-time statistic has successfully been used to detect and track public health events of interest. Important research questions which are summarized at the end of this report are described in more detail with respect to the methodological application in the respective sections. This was thought to help elucidate the research requirements as summarized later in the report. Following the description of the space-time scan statistical application; this report extends to a less traditional area of promise given what has been observed in recent application of analytic methods. Bayesian networks (BNs) represent a conceptual step with advantages of flexibility for the public health surveillance community. Progression from traditional to the more extending statistical concepts in the context of the dynamic status quo of responsibility and challenge, leads to a conclusion consisting of categorical research needs. The report is structured by design to inform judgment about how to build on practical systems to achieve better analytic outcomes for public health surveillance. There are references to research issues throughout the sections with a summarization at the end, which also includes items previously unmentioned in the report.

Algorithms↗
PubMed2006

Pixel-based statistical analysis by a 3D clustering approach: application to autoradiographic images.

Statistical analysis of medical images in experimental laboratories plays an important role in confirming scientific findings and in guiding potential clinical applications. In experimental neuroscience studies, autoradiographic images taken under differing physiological or pathological conditions from replicate animals are often compared in order to detect any significant change in glucose utilization or blood flow and to localize these changes. For these comparisons to be valid and informative, proper statistical procedures are in order. Conventional methods include statistic parametric mapping (SPM) analysis, non-parametric analysis and cluster-analysis. Each method of comparison has a specific purpose. This paper describes an approach that combines these conventional methods and presents a non-parametric statistical procedure based on cluster-analysis for localizing significant differences in autoradiographic data sets. By thresholding cluster sizes rather than pixel values to reject false positives, this approach enhances statistical power. By re-shuffling the data sets to produce the null distribution of a cluster size statistic, the test makes few assumptions as to the statistical properties of the SPM, and thus it is valid under a broad range of conditions. The designed method was tested on autoradiographic images of rats subjected to moderate traumatic brain injury (TBI). Different methods were also performed on the same data sets. Comparison among these methods shows that this method is suitable for the statistical analysis of autoradiographic images.

Animals↗
PubMed1998

Statistical analysis of data pertaining to complex state systems by stepwise regression with reformulated parameters; application to spectroscopically monitored hemoglobin oxygen binding data.

A method is described for the statistical analysis of data pertaining to complex state systems, based on the concept of reformulating the parameters describing the system as a hierarchy of interactions, and this method demonstrated on the analysis of spectroscopically monitored hemoglobin oxygen binding data [K. Imai, Biophys. Chem. 37 (1990) 197-210]. The concept of reformulation was first extended to state parameters other than delta G degree s, such as the extinction coefficients (epsilon s) associated with different ligation states during hemoglobin oxygen binding. The reformulated parameters are incrementally allowed to vary in the data fitting procedure, and the statistical significance of the added parameters tested by F and Kolmogorov-Smirnov tests. The result of this method is the minimal set of statistically significant parameters required to describe the data. The hierarchical nature of reformulated parameters allows the physical significance of the subset of statistically significant parameters to be discussed even when all reformulated terms may not be statistically significant. Applying this method to hemoglobin oxygen binding data with the reformulated Adair model demonstrated that at least two, and at most three, of the four reformulated Adair constants are statistically significant. A reformulated square model was found to give a statistically indistinguishable fit from the Adair model, with the statistically significant thermodynamic terms essentially those proposed by Linus Pauling in 1935. A change in delta epsilon with subsequent oxygen binding events was found to be significant in both models. These results are consistent with a model for hemoglobin oxygen binding where a subunit changes its conformation upon oxygen binding, and affects the conformation of adjacent subunits.

Data Interpretation, Statistical↗
PubMed1994

Accuracy of statistical methods in TRANSFUSION: a review of articles from July/August 1992 through June 1993.

BACKGROUND: Statistical errors have been noted in a large percentage of articles appearing in medical journals. Their incidence in a blood banking journal, however, has not been studied. STUDY DESIGN AND METHODS: Original articles appearing in the journal TRANSFUSION from July 1992 through June 1993 were analyzed for correctness of statistical methods. Each article was reviewed by both a transfusion medicine physician and a statistician. RESULTS: There were 122 original articles, of which 59 contained statistical methods and were reviewed. Of these, 23 (39%) contained an error in the statistical description of their data, 47 (80%) failed to describe clearly the statistical tests performed, and 44 (75%) involved an incorrect statistical test or contained an error in test calculation or interpretation. As a result of these errors, 13 (22%) of the 55 articles analyzed reported conclusions not supported by the data. CONCLUSION: Misuse of statistical methodology may not be uncommon in transfusion medicine research, and it would be prudent to give more attention to statistical methodology in such research.

Blood Transfusion↗
PubMed2005

Improved statistical tests for differential gene expression by shrinking variance components estimates.

Combining information across genes in the statistical analysis of microarray data is desirable because of the relatively small number of data points obtained for each individual gene. Here we develop an estimator of the error variance that can borrow information across genes using the James-Stein shrinkage concept. A new test statistic (FS) is constructed using this estimator. The new statistic is compared with other statistics used to test for differential expression: the gene-specific F test (F1), the pooled-variance F statistic (F3), a hybrid statistic (F2) that uses the average of the individual and pooled variances, the regularized t-statistic, the posterior odds statistic B, and the SAM t-test. The FS-test shows best or nearly best power for detecting differentially expressed genes over a wide range of simulated data in which the variance components associated with individual genes are either homogeneous or heterogeneous. Thus FS provides a powerful and robust approach to test differential expression of genes that utilizes information not available in individual gene testing approaches and does not suffer from biases of the pooled variance approach.

Animals↗
PubMed2003

Tutorials in clinical research: VII. Understanding comparative statistics (contrast)--part B: application of T-test, Mann-Whitney U, and chi-square.

OBJECTIVE: This tutorial on comparative statistics has been written in two complementary segments. The first paper (part A) focused on explaining the general concepts of the null hypothesis and statistical significance. This second article (part B) addresses the application of three specific statistical tests. These two articles should be read sequentially and the first article should be available for reference while one reads the second. STUDY DESIGN: Tutorial. METHODS: The authors met weekly for 10 months to discuss clinical research articles and the applied statistics. The difficulty was not the material but the effort to make it easy to read and as short as possible. RESULTS: The article discusses the application of three common statistical indexes of contrast, chi2, Mann-Whitney U, and Student t-test and other concepts, such as sample size, degrees of freedom, errors, power, and confidence intervals. CONCLUSIONS: Statistical tests generate a number known as a statistic (chi2, U, t), which is sometimes called a "critical ratio" because it helps us to make a decision. This number is then associated with a probability, or P value. Sample size is a crucial element in the initial design of a research project and in the subsequent ability of the results to show statistical significance if the difference is clinically important. The example data used in this paper demonstrate the application of the three specific tests and illustrate the effect of sample size on the results.

Biomedical Research↗
PubMed2004

[A review of statistical analysis methods in measurement data].

Enumeration data is consisted of categorical variable. It is obtained by counting the frequency of the inhomogeneous data. In clinical research, the collected data cover quite of categorical variables. Composed of those categorical variables, the enumeration data had to be correspondingly analyzed with special statistical methods on the ground of their design methods. The statistical methods of measurement data include descriptive statistics and inferential statistics. Descriptive statistics is mainly used to measure the relative number such as rate, proportion and ratio. Inferential statistics is mainly employed to estimate the confidence limit such as estimating the 95% confidence interval of rate and to have the hypothesis testing in the example of having chi-square test etc. By highlighting the specific statistical methods of enumeration data, this review intends to help the clinicians and researchers select correct statistical methods in accordance with enumeration data.

Data Interpretation, Statistical↗
PubMed1992

[What is the role of statistics in medical science?--a critical essay].

In the present paper, the fundamental problems of statistics used in medical science are discussed, based on radical criticism and thorough examination of statistical ideas and methods which are popularly used in the medical field of today. The formal applications of statistical test and estimation theories in mathematical statistics and further belief in double-blind experimental design are frequently found in medical science, where the meaning of population is not made clear and the true characteristics of "statistical test and estimation" and of "double-blind experimental design" are either not understood or are misunderstood. These facts bring many evil influences on medical ideas leading to medical trials. Radical criticism and thorough examination are necessary for the return of medical research to the right path. New statistical ideas and methods must be developed on the basis of the considerations mentioned above. The use of statistics must be made with a modest attitude and exploratory or detective ideas must be adopted, in so far as medical trials are administered, with human mind, to individuals who are intricate and multifarions in reaction. Medical trials and statistics are to be unified in order that desirable results may emerge in human world, enhancing the validity of their contents each other. Here, for example, the exploratory method of multidimensional data analysis and the idea of optimal control with QOL of individuals in medical trials are explained.

Humans↗
PubMed1992

[Quality of data provided by VESKA medical statistics: the case of the fractured proximal femur].

Within the framework of a retrospective study of the incidence of hip fractures in the canton of Vaud (Switzerland), all cases of hip fracture occurring among the resident population in 1986 and treated in the hospitals of the canton were identified from among five different information sources. Relevant data were then extracted from the medical records. At least two sources of information were used to identify cases in each hospital, among them the statistics of the Swiss Hospital Association (VESKA). These statistics were available for 9 of the 18 hospitals in the canton that participated in the study. The number of cases identified from the VESKA statistics was compared to the total number of cases for each hospital. For the 9 hospitals the number of cases in the VESKA statistics was 407, whereas, after having excluded diagnoses that were actually "status after fracture" and double entries, the total for these hospitals was 392, that is 4% less than the VESKA statistics indicate. It is concluded that the VESKA statistics provide a good approximation of the actual number of cases treated in these hospitals, with a tendency to overestimate this number. In order to use these statistics for calculating incidence figures, however, it is imperative that a greater proportion of all hospitals (50% presently in the canton, 35% nationwide) participate in these statistics.

Data Interpretation, Statistical↗
PubMed1989

Statistical uncertainty in the no-observed-adverse-effect level.

The no-observed-adverse-effect level (NOAEL) is a dose value that U.S. EPA reduces by uncertainty factors (UF) and modifying factors (MF) to obtain a reference dose (RfD) for input to regulatory decision making. Whether the true added risk at the NOAEL is below an acceptable level, however, is a source of statistical uncertainty itself. As several authors have previously noted, the probability that added risk at the NOAEL is not negligibly small increases as sample sizes decrease. This is because the definition of the NOAEL statistically controls for the chance of a false-positive error, but not for a false-negative error. The false-positive rate is the test level set by the user in testing for a statistically significant dose effect, typically 0.05. When it is held fixed, the increase in statistical uncertainty as sample size decreases produces an increase in the false-negative rate. Hence, the fewer data available for statistical inference, the higher the expected value of the NOAEL and the less toxic an agent is likely to appear. The solution lies in calculating the probability that a statistical procedure used will detect the maximum added risk acceptable for health regulation (the "power" at that added risk). If the observed response in a dose group is not significantly elevated relative to the control group, and the power for detecting a difference is low as well, then the statistical evidence is inconclusive. In such a case, additional data or other sources of information are needed for evaluating added risk. These concepts are illustrated for examples from the literature with dichotomous (quantal response) data and categorical (severity) data, using a new statistical procedure.

Research Design↗
PubMed2006

Interpreting statistics in the urological literature.

PURPOSE: Knowledge of statistical terminology and the ability to critically interpret research findings are critical skills in the practice of evidence based medicine. MATERIALS AND METHODS: We provide a series of nontechnical explanations of basic statistical concepts commonly encountered in the urological literature. In addition, we provide examples of common statistical pitfalls to increase awareness of limitations to consider when applying research findings to practice. RESULTS: Statistical goals encountered in the urological literature can be broadly categorized as summarizing outcome variables, comparing 2 or more groups, measuring association among variables or predicting 1 variable from another. Errors frequently include the use of an inappropriate test for the data type of interest or using statistical testing in a manner that increases the likelihood of false-positive results. Such errors pose a threat to the validity of research findings and they may undermine study conclusions. CONCLUSIONS: Editors and reviewers alike should strive for high standards of statistical analysis and reporting, and promote the publication of high quality evidence in the urological literature. The understanding of basic statistical concepts and the principles of the hypothesis testing framework is essential to the critical appraisal process and, therefore, important to all urologists. Statistical literacy should be fostered through educational materials and courses in the urological community.

Data Interpretation, Statistical↗
PubMed1996

Statistical methods in rehabilitation literature: a survey of recent publications.

OBJECTIVE: To document the use of statistical methods in the recent rehabilitation research literature. DESIGN: Descriptive survey study. METHODS: All 1,039 articles published between January 1990 and December 1993 in the American Journal of Physical Medicine and Rehabilitation and the Archives of Physical Medicine and Rehabilitation were reviewed for the use of statistical methods. RESULTS: There were 682 (66%) research articles in the sample that included systematic data collection and analysis. The most frequently encountered analytic tests and techniques included: (1) analysis of variance, (2) t tests, (3) correlation analysis, (4) contingency table analysis, (5) regression, and (6) nonparametric tests. Results were compared with results of a review of the 1982 rehabilitation literature by Wainapel and Kayne. Our study showed an increased sophistication in statistical methodology as well as a more intense use of such methods. In addition, there was a large number of relatively obscure and poorly documented tests encountered in the more recent literature. There was also a lack of adherence to a standardized format for describing statistical methods. CONCLUSIONS: The following recommendations are made: (1) Training curricula for rehabilitation professionals should provide instruction in the most commonly-encountered statistical methods and should be revised as needed to reflect changes in statistical method usage. (2) When less common statistical tests are applied, the responsibility of the authors to fully describe and justify their methods should be recognized. (3) Critical assessment of the literature is facilitated when statistical methods are reported in a standardized format.

Periodicals as Topic↗
PubMedDate not supplied

Comparison of statistical approaches for the analysis of proteome expression data of differentiating neural stem cells.

Comparative proteomic studies often use statistical tests included in the software for the analysis of digitized images of two-dimensional electrophoresis gels. As these programs include only limited capabilities for statistical analysis, many studies do not further describe their statistical approach. To find potential differences produced by different data processing, we compared the results of (1) Student's t-test using a spreadsheet program, (2) the intrinsic algorithms implemented in the Phoretix 2D gel analysis software, and (3) the SAM algorithm originally developed for microarray analysis. We applied the algorithms to proteome data of undifferentiated neural stem cells versus in vitro differentiated neural stem cells. We found (1) 367 spots differentially expressed using Student's t-test, (2) 203 spots using the algorithms in Phoretix 2D, and (3) 119 spots using the algorithms in SAM, respectively, with an overlap of 42 spots detected by all three algorithms. Applying different statistical approaches on the same dataset resulted in divergent set of protein spots labeled as statistically "significant". Currently, there is no agreement on statistical data processing of 2DE datasets, but the statistical tests applied in 2DE studies should be documented. Tools for the statistical analysis of proteome data should be implemented and documented in the existing 2DE software.

Animals↗
PubMed2000

Bayesian statistics in medical research: an intuitive alternative to conventional data analysis.

Statistical analysis of both experimental and observational data is central to medical research. Unfortunately, the process of conventional statistical analysis is poorly understood by many medical scientists. This is due, in part, to the counter-intuitive nature of the basic tools of traditional (frequency-based) statistical inference. For example, the proper definition of a conventional 95% confidence interval is quite confusing. It is based upon the imaginary results of a series of hypothetical repetitions of the data generation process and subsequent analysis. Not surprisingly, this formal definition is often ignored and a 95% confidence interval is widely taken to represent a range of values that is associated with a 95% probability of containing the true value of the parameter being estimated. Working within the traditional framework of frequency-based statistics, this interpretation is fundamentally incorrect. It is perfectly valid, however, if one works within the framework of Bayesian statistics and assumes a 'prior distribution' that is uniform on the scale of the main outcome variable. This reflects a limited equivalence between conventional and Bayesian statistics that can be used to facilitate a simple Bayesian interpretation based on the results of a standard analysis. Such inferences provide direct and understandable answers to many important types of question in medical research. For example, they can be used to assist decision making based upon studies with unavoidably low statistical power, where non-significant results are all too often, and wrongly, interpreted as implying 'no effect'. They can also be used to overcome the confusion that can result when statistically significant effects are too small to be clinically relevant. This paper describes the theoretical basis of the Bayesian-based approach and illustrates its application with a practical example that investigates the prevalence of major cardiac defects in a cohort of children born using the assisted reproduction technique known as ICSI (intracytoplasmic sperm injection).

Bayes Theorem↗
PubMed1983

Use of statistical analysis in the New England Journal of Medicine.

A sorting of the statistical methods used by authors of the 760 research and review articles in Volumes 298 to 301 of The New England Journal of Medicine indicates that a reader who is conversant with descriptive statistics (percentages, means, and standard deviations) has statistical access to 58 per cent of the articles. Understanding t-tests increases this access to 67 per cent. The addition of contingency tables gives statistical access to 73 per cent of the articles. Familiarity with each additional statistical method gradually increases the percentage of accessible articles. Original Articles use statistical techniques more extensively than other articles in the Journal. Research studies based on a longitudinal design make heavier use of statistics than do those using a cross-sectional design. The tabulations in this study should aid clinicians and medical investigators who are planning their continuing education in statistical methods, and faculty who design or teach courses in quantitative methods for medical and health professionals.

Education, Medical, Continuing↗
PubMed1984

An evaluation of the use of statistical methodology in the Journal of Infectious Diseases.

One hundred fourteen articles published in the Journal of Infectious Diseases in 1982 were evaluated for the occurrence of eight commonly made statistical errors. Seventy-one percent of Original Articles and 50% of presentations in the Data Forum used statistical methods to analyze results. Almost all of the articles that used statistics contained at least one statistical error. The most common inadequacy, which occurred in 95% of the articles with statistical data, was the statement of a probability value without a complete summary of the statistical results. The most common error was the failure to include a correction for multiple comparisons. These results suggest that a more clearly stated statistical policy, a more explicit set of instructions to authors, and closer editorial attention to statistical methodology, perhaps at the prepublication phase, would improve the validity of articles published in the Journal.

Communicable Diseases↗