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PubMed1992

Risk, statistical inference, and the law of evidence: the use of epidemiological data in toxic tort cases.

Toxic torts are product liability cases dealing with alleged injuries due to chemical or biological hazards such as radiation, thalidomide, or Agent Orange. Toxic tort cases typically rely more heavily than other product liability cases on indirect or statistical proof of injury. There have been numerous theoretical analyses of statistical proof of injury in toxic tort cases. However, there have been only a handful of actual legal decisions regarding the use of such statistical evidence, and most of those decisions have been inconclusive. Recently, a major case from the Fifth Circuit, involving allegations that Benedectin (a morning sickness drug) caused birth defects, was decided entirely on the basis of statistical inference. This paper examines both the conceptual basis of that decision, and also the relationships among statistical inference, scientific evidence, and the rules of product liability in general.

Abnormalities, Drug-Induced↗

PubMed2000

Research fundamentals: statistical considerations in research design: a simple person's approach.

A basic understanding of statistical methodology is essential, both for designing quality research projects and for evaluating the medical literature. Careful statistical planning, including the selection of study endpoints, the determination of the required sample size, and the selection of statistical tests to be used in the data analysis, is important to ensure a successful research project. The purpose of this article is to provide a basic review of statistical terms and methods for both the researcher and the clinician, as well as to clarify questions that need to be answered prior to embarking on an experimental study. The advantages of collaborating with statistical consultants, and some guidelines for such collaborations, are discussed as well.

Cooperative Behavior↗

PubMed1994

Outcome measures in split mouth caries trials and their statistical evaluation.

The split mouth study design in trials of fissure sealants and restorative materials neatly controls for confounding by many of the variables associated with poor retention of sealants and occurrence of dental caries. Traditionally, the outcome measures used in split mouth trials have been material retention, (per cent) effectiveness and net gain. A survey of the literature revealed that a large proportion of split mouth studies report no statistical evaluation of outcome measures. In those studies in which statistical evaluation had been conducted, McNemar's X2 was the test most frequently used. This statistic is appropriate for comparing differences between "success" and "failure" tooth pairs (or "positives" and "negatives" in split mouth terminology) but it cannot evaluate directly effectiveness and net gain. The distributions of effectiveness and net gain are different and it would be desirable to estimate confidence intervals for them. In this paper, we consider these statistics, suggest methods by which confidence intervals may be calculated, and provide examples of the calculations. We demonstrate the close relationship between effectiveness (as used in split mouth trials) and relative effect and relative risk (as used in general epidemiological analysis) and recommend that relative risk should be the preferred outcome measure for split mouth trials. Whatever outcome measure is chosen in split mouth trials should always be subjected to statistical evaluation, preferably by the calculation of confidence intervals.

Bias↗

PubMed1988

What do dentists know about statistics?

A multiple choice test with nine statistical questions was sent to a random sample of Danish dentists to assess their knowledge of elementary statistical expressions (SD, SE, P less than 0.05, P greater than 0.05 and r). Of 250 dentists, 129 (52%) answered the questions. The test was also completed by 27 (71%) of 38 dental students immediately after the last lecture in statistics. The median number of correct answers was 2.2 among the dentists and 3.4 among the dental students. It is concluded that the statistical knowledge of most dentists, and to a lesser degree also dental students, is so limited that they cannot be expected to be critically against or to draw the right conclusions from those statistical analyses with which they are confronted. Only 35% of the dentists stated that it is very important that this problem be raised.

Attitude of Health Personnel↗

PubMedDate not supplied

The use of statistical methodology for the analysis of QA data.

Modern statistical methods offer the quality assurance professional a powerful set of tools for the analysis of health care data. By using well-established procedures the quality assurance professional can isolate and quantify the relative degree of problems, determine statistical significance, and allocate resources towards problem resolution. This article briefly covers the concepts of hypothesis testing and statistical significance and their role in assisting the health care professional to assign priorities for use in problem resolution. Several statistical tests are described in the context of health care data analysis. The distinction between parametric, and non-parametric tests is made and a set of guidelines included on when and where the respective tests should be applied. An outline for the application of these statistical tests to several example situations is presented. Some details of population identification, sampling, and data organization are covered. Interpretation of results in the context of different problem situations is also covered.

Data Collection↗

PubMedDate not supplied

Can't miss: conquer any number task by making important statistics simple. Part 4. Confidence intervals with t distributions, standard error, and confidence intervals for proportions.

Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies, all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics, as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data. The statistical principles are equally applicable to quality improvement and healthcare research studies. This article, Part 4, starts with a review of the information contained in Parts 1, 2, and 3, which appeared in the July/August 2003 issue of the Journal for Healthcare Quality. This article describes t distributions and how these are used to calculate confidence intervals for estimating a population mean based on a sample mean of a continuous variable. Part 4 concludes with a discussion of standard error, margin of error, and confidence intervals for estimating a population proportion based on a sample proportion from a binomial variable.

Confidence Intervals↗

PubMed2003

Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier.

A multichannel statistical classifier for detecting prostate cancer was developed and validated by combining information from three different magnetic resonance (MR) methodologies: T2-weighted, T2-mapping, and line scan diffusion imaging (LSDI). From these MR sequences, four different sets of image intensities were obtained: T2-weighted (T2W) from T2-weighted imaging, Apparent Diffusion Coefficient (ADC) from LSDI, and proton density (PD) and T2 (T2 Map) from T2-mapping imaging. Manually segmented tumor labels from a radiologist, which were validated by biopsy results, served as tumor "ground truth." Textural features were extracted from the images using co-occurrence matrix (CM) and discrete cosine transform (DCT). Anatomical location of voxels was described by a cylindrical coordinate system. A statistical jack-knife approach was used to evaluate our classifiers. Single-channel maximum likelihood (ML) classifiers were based on 1 of the 4 basic image intensities. Our multichannel classifiers: support vector machine (SVM) and Fisher linear discriminant (FLD), utilized five different sets of derived features. Each classifier generated a summary statistical map that indicated tumor likelihood in the peripheral zone (PZ) of the prostate gland. To assess classifier accuracy, the average areas under the receiver operator characteristic (ROC) curves over all subjects were compared. Our best FLD classifier achieved an average ROC area of 0.839(+/-0.064), and our best SVM classifier achieved an average ROC area of 0.761(+/-0.043). The T2W ML classifier, our best single-channel classifier, only achieved an average ROC area of 0.599(+/-0.146). Compared to the best single-channel ML classifier, our best multichannel FLD and SVM classifiers have statistically superior ROC performance (P=0.0003 and 0.0017, respectively) from pairwise two-sided t-test. By integrating the information from multiple images and capturing the textural and anatomical features in tumor areas, summary statistical maps can potentially aid in image-guided prostate biopsy and assist in guiding and controlling delivery of localized therapy under image guidance.

Algorithms↗

PubMed2004

Suppression of intensity transition artifacts in statistical x-ray computer tomography reconstruction through radon inversion initialization.

Statistical reconstruction (SR) methods provide a general and flexible framework for obtaining tomographic images from projections. For several applications SR has been shown to outperform analytical algorithms in terms of resolution-noise trade-off achieved in the reconstructions. A disadvantage of SR is the long computational time required to obtain the reconstructions, in particular when large data sets characteristic for x-ray computer tomography (CT) are involved. As was shown recently, by combining statistical methods with block iterative acceleration schemes [e.g., like in the ordered subsets convex (OSC) algorithm], the reconstruction time for x-ray CT applications can be reduced by about two orders of magnitude. There are, however, some factors lengthening the reconstruction process that hamper both accelerated and standard statistical algorithms to similar degree. In this simulation study based on monoenergetic and scatter-free projection data, we demonstrate that one of these factors is the extremely high number of iterations needed to remove artifacts that can appear around high-contrast structures. We also show (using the OSC method) that these artifacts can be adequately suppressed if statistical reconstruction is initialized with images generated by means of Radon inversion algorithms like filtered back projection (FBP). This allows the reconstruction time to be shortened by even as much as one order of magnitude. Although the initialization of the statistical algorithm with FBP image introduces some additional noise into the first iteration of OSC reconstruction, the resolution-noise trade-off and the contrast-to-noise ratio of final images are not markedly compromised.

Algorithms↗

PubMed1996

Statistics of the log-compressed echo envelope.

Log compression of A lines to produce B-scan images in clinical ultrasound imaging systems is a standard procedure to control the dynamic range of the images. The statistics of such compressed images in terms of underlying scatterer statistics have not been derived. The statistics are analyzed for partially formed speckle using a general K distribution model of envelope statistics to derive the density function for the log-compressed envelope. This density function is used to elucidate the relation between the moments of the compressed envelope, the compression parameters, and the statistics of the scatterers. The analysis shows that the mean of the log-compressed envelope is an increasing function of both the backscattered energy and the effective scatterer density. The variance of the log-compressed envelope is a decreasing function of the effective scatterer density and is independent of the backscattered energy.

Computer Simulation↗

PubMed1983

Is the statistical assessment of papers submitted to the "British Medical Journal" effective?

A study was performed to judge the effectiveness of the statistical assessment scheme for papers submitted to the "British Medical Journal." Statistical criticism of the content of 200 submitted papers which had already been seen by a subject referee led directly or indirectly to 73 (37%) being rejected for publication. In most cases (53 out of 73) serious problems requiring more than minor revision were identified. A comparison of reports on subsequently unpublished and published papers showed that adverse statistical assessments--suggesting major problems--were more common in the papers that were not accepted for publication. Moderate, or less, revision was recommended for 63% of published papers but 39% of the remainder. A checklist of relevant questions was used in making a detailed comparison of 12 published papers, six of which had been statistically assessed and six of which had not. This comparison yielded little evidence that the papers that had been assessed were statistically more acceptable than those that had not been assessed but re-emphasised the subjectivity of refereeing and assessment.

Periodicals as Topic↗

PubMed1986

Use of check lists in assessing the statistical content of medical studies.

Two check lists are used routinely in the statistical assessment of manuscripts submitted to the "BMJ." One is for papers of a general nature and the other specifically for reports on clinical trials. Each check list includes questions on the design, conduct, analysis, and presentation of studies, and answers to these contribute to the overall statistical evaluation. Only a small proportion of submitted papers are assessed statistically, and these are selected at the refereeing or editorial stage. Examination of the use of the check lists showed that most papers contained statistical failings, many of which could easily be remedied. It is recommended that the check lists should be used by statistical referees, editorial staff, and authors and also during the design stage of studies.

Clinical Trials as Topic↗

PubMed2005

Was Rodney Ledward a statistical outlier? Retrospective analysis using routine hospital data to identify gynaecologists' performance.

OBJECTIVES: To investigate whether routinely collected data from hospital episode statistics could be used to identify the gynaecologist Rodney Ledward, who was suspended in 1966 and was the subject of the Ritchie inquiry into quality and practice within the NHS. DESIGN: A mixed scanning approach was used to identify seven variables from hospital episode statistics that were likely to be associated with potentially poor performance. A blinded multivariate analysis was undertaken to determine the distance (known as the Mahalanobis distance) in the seven indicator multidimensional space that each consultant was from the average consultant in each year. The change in Mahalanobis distance over time was also investigated by using a mixed effects model. SETTING: NHS hospital trusts in two English regions, in the five years from 1991-2 to 1995-6. Population Gynaecology consultants (n = 143) and their hospital episode statistics data. MAIN OUTCOME MEASURE: Whether Ledward was a statistical outlier at the 95% level. RESULTS: The proportion of consultants who were outliers in any one year (at the 95% significance level) ranged from 9% to 20%. Ledward appeared as an outlier in three of the five years. Our mixed effects (multi-year) model identified nine high outlier consultants, including Ledward. CONCLUSION: It was possible to identify Ledward as an outlier by using hospital episode statistics data. Although our method found other outlier consultants, we strongly caution that these outliers should not be overinterpreted as indicative of "poor" performance. Instead, a scientific search for a credible explanation should be undertaken, but this was outside the remit of our study. The set of indicators used means that cancer specialists, for example, are likely to have high values for several indicators, and the approach needs to be refined to deal with case mix variation. Even after allowing for that, the interpretation of outlier status is still as yet unclear. Further prospective evaluation of our method is warranted, but our overall approach may be potentially useful in other settings, especially where performance entails several indicator variables.

Clinical Competence↗

PubMedDate not supplied

Statistical process control methods for expert system performance monitoring.

The literature on the performance evaluation of medical expert system is extensive, yet most of the techniques used in the early stages of system development are inappropriate for deployed expert systems. Because extensive clinical and informatics expertise and resources are required to perform evaluations, efficient yet effective methods of monitoring performance during the long-term maintenance phase of the expert system life cycle must be devised. Statistical process control techniques provide a well-established methodology that can be used to define policies and procedures for continuous, concurrent performance evaluation. Although the field of statistical process control has been developed for monitoring industrial processes, its tools, techniques, and theory are easily transferred to the evaluation of expert systems. Statistical process tools provide convenient visual methods and heuristic guidelines for detecting meaningful changes in expert system performance. The underlying statistical theory provides estimates of the detection capabilities of alternative evaluation strategies. This paper describes a set of statistical process control tools that can be used to monitor the performance of a number of deployed medical expert systems. It describes how p-charts are used in practice to monitor the GermWatcher expert system. The case volume and error rate of GermWatcher are then used to demonstrate how different inspection strategies would perform.

Evaluation Studies as Topic↗

PubMed2005

Haplotype sharing analysis using mantel statistics.

OBJECTIVE: The potential value of haplotypes has attracted widespread interest in the mapping of complex traits. Haplotype sharing methods take the linkage disequilibrium information between multiple markers into account, and may have good power to detect predisposing genes. We present a new approach based on Mantel statistics for spacetime clustering, which is developed in order to improve the power of haplotype sharing analysis for gene mapping in complex disease. METHODS: The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes for case-only and case-control studies. The genetic similarity is measured as the shared length between haplotypes around a putative disease locus. The phenotypic similarity is measured as the mean-corrected cross-product based on the respective phenotypes. We analyzed two tests for statistical significance with respect to type I error: (1) assuming asymptotic normality, and (2) using a Monte Carlo permutation procedure. The results were compared to the chi(2) test for association based on 3-marker haplotypes. RESULTS: The results of the type I error rates for the Mantel statistics using the permutational procedure yielded pointwise valid tests. The approach based on the assumption of asymptotic normality was seriously liberal. CONCLUSION: Power comparisons showed that the Mantel statistics were better than or equal to the chi(2) test for all simulated disease models.

Case-Control Studies↗

PubMedDate not supplied

The use of bootstrap resampling to assess the uncertainty of cooper statistics.

The predictive abilities of two-group classification models (CMs) are often expressed in terms of their Cooper statistics. These statistics are often reported without any indication of their uncertainty, making it impossible to judge whether the predicted classifications are significantly better than the predictions made by a different CM, or whether the predictive performance of the CM exceeds predefined performance criteria in a statistically significant way. Bootstrap resampling routines are reported that provide a means of expressing the uncertainty associated with Cooper statistics. The usefulness of the bootstrapping routines is illustrated by constructing 95% confidence intervals for the Cooper statistics of four alternative skin-corrosivity tests (the rat skin transcutaneous electrical resistance assay, EPISKIN, Skin(2) and CORROSITEX), and four two-step sequences in which each in vitro test is used in combination with a physicochemical test for skin corrosion based on pH measurements.

Animal Testing Alternatives↗

PubMed2003

EasyGene--a prokaryotic gene finder that ranks ORFs by statistical significance.

BACKGROUND: Contrary to other areas of sequence analysis, a measure of statistical significance of a putative gene has not been devised to help in discriminating real genes from the masses of random Open Reading Frames (ORFs) in prokaryotic genomes. Therefore, many genomes have too many short ORFs annotated as genes. RESULTS: In this paper, we present a new automated gene-finding method, EasyGene, which estimates the statistical significance of a predicted gene. The gene finder is based on a hidden Markov model (HMM) that is automatically estimated for a new genome. Using extensions of similarities in Swiss-Prot, a high quality training set of genes is automatically extracted from the genome and used to estimate the HMM. Putative genes are then scored with the HMM, and based on score and length of an ORF, the statistical significance is calculated. The measure of statistical significance for an ORF is the expected number of ORFs in one megabase of random sequence at the same significance level or better, where the random sequence has the same statistics as the genome in the sense of a third order Markov chain. CONCLUSIONS: The result is a flexible gene finder whose overall performance matches or exceeds other methods. The entire pipeline of computer processing from the raw input of a genome or set of contigs to a list of putative genes with significance is automated, making it easy to apply EasyGene to newly sequenced organisms. EasyGene with pre-trained models can be accessed at http://www.cbs.dtu.dk/services/EasyGene.

Bacillus subtilis↗

PubMed2001

The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study.

BACKGROUND: Many randomized trials involve measuring a continuous outcome - such as pain, body weight or blood pressure - at baseline and after treatment. In this paper, I compare four possibilities for how such trials can be analyzed: post-treatment; change between baseline and post-treatment; percentage change between baseline and post-treatment and analysis of covariance (ANCOVA) with baseline score as a covariate. The statistical power of each method was determined for a hypothetical randomized trial under a range of correlations between baseline and post-treatment scores. RESULTS: ANCOVA has the highest statistical power. Change from baseline has acceptable power when correlation between baseline and post-treatment scores is high;when correlation is low, analyzing only post-treatment scores has reasonable power. Percentage change from baseline has the lowest statistical power and was highly sensitive to changes in variance. Theoretical considerations suggest that percentage change from baseline will also fail to protect from bias in the case of baseline imbalance and will lead to an excess of trials with non-normally distributed outcome data. CONCLUSIONS: Percentage change from baseline should not be used in statistical analysis. Trialists wishing to report this statistic should use another method, such as ANCOVA, and convert the results to a percentage change by using mean baseline scores.

Computer Simulation↗

PubMed2006

Statistical methods for analysing Barthel scores in trials of poststroke interventions: a review and computer simulations.

BACKGROUND AND PURPOSE: Arguments persist as to whether parametric or non-parametric methods should be used to analyse ordinal data in trials. This paper aims to assess methods used for presenting and analysing an ordinal scale, the Barthel Index, in trials of poststroke interventions. METHODS: All randomized controlled trials (RCTs) of poststroke interventions published from 1995 to 2004 in two journals (Stroke and Clinical Rehabilitation) were scrutinized for methods used to present and analyse Barthel scores. Computer simulations were used to compare the type I errors and the statistical power of different statistical methods under a range of assumed circumstances. RESULTS: One hundred and fifty-six RCTs were identified within the two journals. The central tendency of Barthel scores was measured by the median in 47 trials and by the mean in 35 trials. Non-parametric analyses of Barthel scores were conducted in 47 trials and parametric methods used in 18 trials. The results of computer simulations demonstrate that the t-test has a similar type I error rate and statistical power when compared with the rank sum test. However, when a zero final Barthel score is assigned to patients who have died, the statistical power of the t-test is much reduced. The possible maximal statistical power of dichotomization and ordinal regression is usually much lower than that of the rank sum test. CONCLUSIONS: To facilitate comparison and meta-analysis, we recommend that mean values (with standard deviations or standard errors) of Barthel scores should be routinely reported in trials of poststroke interventions. The rank sum test appears the most powerful inferential technique for detecting differences in Barthel scores.

Computer Simulation↗