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PubMed1997

Scoring and analysis of performance examinations: a comparison of methods and interpretations.

The purpose of this study was to compare the results and interpretation of the data from a performance examination when four methods of analysis are used. Methods are 1) traditional summary statistics, 2) inter-judge correlations, 3) generalizability theory, and 4) the multi-facet Rasch model. Results indicated that similar sources of variance were identified using each method; however, the multi-facet Rasch model is the only method that linearized the scores and accounts for differences in the particular examination challenged by a candidate before ability estimates are calculated.

Data Interpretation, Statistical↗
PubMed2002

Proteome analysis based on motif statistics.

MOTIVATION: Even for the amino acid motifs collected in the Prosite database there may be chance occurences as opposed to those occurences where the motif is involved in fold or function of a protein. With recent mathematical advances in assessing the significance of observing such a motif a particular number of times, we can now study the over- or under-representation of particular motifs in a complete genome and attempt to make functional deductions. RESULTS: We demonstrate that statistical over- or under-representation of motifs in complete proteomes may be an indicator of whether, in that organism, we are looking at chance occurrences of the motif or whether the occurrences are sufficiently numerous to suggest a systematic, and thus functionally important occurrence. This has important implications on databank annotations. AVAILABILITY: The complete dataset comprising the plotted statistics of 266 Prosite motifs on 42 proteomes is available at http://algo.inria.fr/nicodeme/proteomes/proteocomp.html. The software used to compute this data has been described by Nicodème (2000, 2001). They are available either by web access as mentioned in these articles or by direct request from Pierre Nicodème.

Amino Acid Motifs↗
PubMed2006

Random data set generation to support microarray analysis.

As microarray analyses become increasingly routine, involving the simultaneous investigation of huge numbers of genes, researchers can easily search for and uncover what appear to be promising patterns in their data. In such circumstances tools are needed to help decide the extent to which these patterns are meaningful or can be explained by chance alone. The purpose of this chapter is to describe examples of the use of microarray analysis for inferential purposes and how validation of inference is addressed by Monte-Carlo techniques, which essentially amounts to investigation of statistical methods on synthetic or random data sets.

Animals↗
PubMed1992

Analytical method comparison based upon statistical power calculations.

Testing for the equivalence of results generated by different analytical methodology is a common practice in the pharmaceutical sciences. Methodology changes are implemented for both scientific and economic reasons during a scientific study. Thus, the need to demonstrate the appropriateness of considering data generated by distinct methods as part of a single information population arises. This paper describes a rapid and simple approach to the statistical design and interpretation of method comparison experiments. The approach presented is based upon a statistical power calculation technique, a knowledge of the variability associated with the methods to be compared and the criteria for equivalence (the limits within which differences become immeasurable or, for practical purposes, insignificant). Reference tables are included which show necessary sample sizes for comparison experiments for common combinations of these three variables.

Chemistry Techniques, Analytical↗
PubMed2003

Quantitative methods in pharmacovigilance: focus on signal detection.

Pharmacovigilance serves to detect previously unrecognised adverse events associated with the use of medicines. The simplest method for detecting signals of such events is crude inspection of lists of spontaneously reported drug-event combinations. Quantitative and automated numerator-based methods such as Bayesian data mining can supplement or supplant these methods. The theoretical basis and limitations of these methods should be understood by drug safety professionals, and automated methods should not be automatically accepted. Published evaluations of these techniques are mainly limited to large regulatory databases, and performance characteristics may differ in smaller safety databases of drug developers. Head-to-head comparisons of the major techniques have not been published. Regardless of previous statistical training, pharmacovigilance practitioners should understand how these methods work. The mathematical basis of these techniques should not obscure the numerous confounders and biases inherent in the data. This article seeks to make automated signal detection methods transparent to drug safety professionals of various backgrounds. This is accomplished by first providing a brief overview of the evolution of signal detection followed by a series of sections devoted to the methods with the greatest utilisation and evidentiary support: proportional reporting rations, the Bayesian Confidence Propagation Neural Network and empirical Bayes screening. Sophisticated yet intuitive explanations are provided for each method, supported by figures in which the underlying statistical concepts are explored. Finally the strengths, limitations, pitfalls and outstanding unresolved issues are discussed. Pharmacovigilance specialists should not be intimidated by the mathematics. Understanding the theoretical basis of these methods should enhance the effective assessment and possible implementation of these techniques by drug safety professionals.

Adverse Drug Reaction Reporting Systems↗
PubMed1989

Effect of crossover on the statistical power of randomized studies.

Randomized studies involving long-term follow-up are vulnerable to the effects of unplanned crossover. In surgical studies, such crossover usually occurs when control patients become more symptomatic and undergo operation. In several large studies of coronary bypass grafting, crossover ranged from 25% to 38%. The most common way of dealing with this problem is to apply the "intention-to-treat" principle, which analyzes such crossovers with their originally assigned groups. Besides the logical problem of counting a control patient who actually undergoes operation as "nonsurgical," a more subtle problem arises in terms of statistical power. When statistical power is low, a truly effective treatment may be mistakenly labeled as no better than control, causing a potentially valuable form of therapy to be ignored or discarded. This analysis demonstrates that crossover may have a profound effect on the statistical power of randomized studies and presents a method for predicting the effect of such crossover on statistical power.

Coronary Artery Bypass↗
PubMed2000

Toward a more meaningful in vitro fertilization success rate.

PURPOSE: The objective was to explore the variability in in vitro fertilization (IVF) success rates. METHODS: Published success rates from IVF clinics in North America were investigated to establish types of biases and potential inaccuracies. RESULTS: Success rates reported by IVF clinics vary with regard to the indices and patient populations used to compute them. Selection bias and misunderstood statistics are major factors contributing to the inappropriateness of certain rates. CONCLUSIONS: The influence of privatization and market forces also may contribute to the need to oversimplify IVF statistics.

Data Interpretation, Statistical↗
PubMed1998

Exploring individual change.

In the analysis of the impact of clinical interventions, the received wisdom has been that posttreatment scores, with pretreatment scores equated by random assignment or statistically partialed out, should be used to evaluate treatment outcomes. However, posttreatment scores are not generally more reliable than, nor equivalent to, change scores, even with pretreatment scores partialed out of both. Moreover, there are data-analytic methods that indicate how individual patients change, in terms of response curves over time, rather than indicate only how much groups change on the average. These methods take researchers back to the individual data that they ought to use for choosing the specific models of change to be used. To maximize relevance for clinical practice, the results of treatment research should always be reported at this most disaggregated or individual change level, as well as, when appropriate, at more aggregated statistical levels.

Data Interpretation, Statistical↗
PubMed1991

Discussion of PET workshop reports, including recommendations of PET Data Analysis Working Group.

On May 1-2, 1989, a PET Data Analysis Working Group convened to consider positron emission tomography (PET) methodology and data analysis. The papers presented and the recommendations of the Group are reviewed. The Group recommended that a standard phantom of the human brain be used by different institutions to examine machine and data reconstruction PET variables. Interinstitutional comparisons could be aided by using a standard three-dimensional coordinate system. Deformations within individual diseased or atypical brains would require nonlinear as well as linear transformations to the standard space, using magnetic resonance images in register with the PET images. Methods for intersubject averaging of pixel-by-pixel or region-of-interest data, as well as appropriate statistical methods, need to be developed. PET data may first be exploratory and hypothesis-generating (with less stringent statistical theory), then later used to test hypotheses (with more stringent statistical criteria). Common databases, obtained by computer simulation models with known inherent structure, or directly by PET measurements on different groups, could be used to compare analytical and statistical methods among institutions.

Brain↗
PubMed1996

Basic statistical testing, including interim analysis.

Clinical trials, due to the randomization process, require statistical methods for testing key hypotheses that are straightforward and involve simple comparisons of group proportions or group means. Differences between group proportions are tested using a chi 2 statistic or a Fisher's exact test. Differences between group means are tested using a two-sample t statistic. When there are more than two groups, the F statistic is calculated. When ordinal data or data not normally distributed are analyzed, nonparametric testing is performed. A critical decision prior to analysis is the choice of the endpoint, which must be clearly defined and consistently applied. Monitoring a clinical trial is important, and may lead to interim analysis. This should be done by independent study monitors. Interim results play an important role in analyzing clinical trials; however, they create problems due to multiple testing. An early stoppage rule is frequently applied when independent interim results are obtained. The statistical methods for interim analysis are discussed.

Data Interpretation, Statistical↗
PubMedDate not supplied

The use of regularity as estimated by approximate entropy to distinguish saltatory growth.

A nonlinear dynamics metric, approximate entropy (ApEn), is investigated as a diagnostic method for distinguishing between mathematical models, and the underlying mechanistic hypotheses that purport to describe the same time series experimental observations. ApEn measures the occurrence of pattern regularity within a time series, and is used here to investigate growth patterns in daily length growth. The notion investigated is that ApEn distributions for competing time series patterns expressed as mathematical formulations can be modelled by Monte Carlo and bootstrap methods and compared to the ApEn values for an original experimental data series. If the ApEn values for the different models do not overlap, then it is expected that ApEn can be utilized to distinguish these models and hypotheses, and to provide statistical assessment for the underlying biological patterns in experimental data. The conclusion is that the ApEn metric is successful as a time series diagnostic tool. It is a model-independent statistic that clearly differentiates saltatory growth from slowly varying continuous models of growth and serves to further document the saltatory nature of growth. This is a unique application of approximate entropy, illustrating the broad applicability of ApEn to biological time series, with the specific example of discriminating a saltatory growth process in longitudinal growth data. Future investigations of regularity in longitudinal time series in human biology with ApEn statistics are suggested.

Data Interpretation, Statistical↗
PubMed1998

Hypothesis testing and confidence interval construction in 2 x 2 tables of correlated proportions.

The 2 x 2 table is an invaluable tool for displaying bivariate binary data. It is easy to find examples of correlated binary response in biopharmaceutical experiments and clinical research and analysis of these data is a current research topic. The most common hypothesis tested for 2 x 2 tables of correlated proportions is that of homogeneity of the marginal proportions or, equivalently, the hypothesis of table symmetry. The 2 x 2 table of correlated proportions is rich with information and we present a survey of some of the analyses relevant for these data. Using asymptotic theory, we develop estimators of relevant parameters and associated test statistics that are of interest. We discuss interval estimation using arguments proposed by Quesenberry and Hurst (1) and Goodman (2). These interval estimators do not rely on estimation of the covariance matrix and are not necessarily equivalent to those obtained using modified chi-square statistics.

Analysis of Variance↗
PubMed1989

Telephone-mediated lightning injury: an Australian survey.

Each year in Australia, about 60 people report injuries attributable to lightning surges while using a telephone during nearby thunderstorms. This paper presents information about such incidents and describes a retrospective survey of more than 300 telephone users reporting injuries possibly attributable to lightning. Questionnaires yielded 132 usable responses, and the results were analysed to identify the extent and nature of the lightning injuries. These are compared with direct strike injuries. Three distinct telephone-mediated lightning strike syndromes are identified (statistically) among the victims.

Australia↗
PubMed1991

Large database management in clinical dental research.

Previously the management and analysis of large databases for longitudinal clinical dental research has been severely restricted by the costs of custom-made software and access to suitable computing equipment. However, the recent availability of powerful personal computers and the use of the Scientific Information Retrieval Database Management System (SIR/DBMS) in association with BMDP Statistical Software has now created an enormously powerful tool for extensive and fast data manipulation requiring relatively few commands and the capability to easily perform detailed statistical analyses.

Computer Graphics↗
PubMed1997

EM algorithms without missing data.

Most problems in computational statistics involve optimization of an objective function such as a loglikelihood, a sum of squares, or a log posterior function. The EM algorithm is one of the most effective algorithms for maximization because it iteratively transfers maximization from a complex function to a simple, surrogate function. This theoretical perspective clarifies the operation of the EM algorithm and suggests novel generalizations. Besides simplifying maximization, optimization transfer usually leads to highly stable algorithms with well-understood local and global convergence properties. Although convergence can be excruciatingly slow, various devices exist for accelerating it. Beginning with the EM algorithm, we review in this paper several optimization transfer algorithms of substantial utility in medical statistics.

Algorithms↗