Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems.

A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period.

Evaluating the Contributions of Individual Variables to a Quadratic Form.

Quadratic forms capture multivariate information in a single number, making them useful, for example, in hypothesis testing. When a quadratic form is large and hence interesting, it might be informative to partition the quadratic form into contributions of individual variables. In this paper it is argued that meaningful partitions can be formed, though the precise partition that is determined will depend on the criterion used to select it.

On point estimation of the abnormality of a Mahalanobis index.

Mahalanobis distance may be used as a measure of the disparity between an individual's profile of scores and the average profile of a population of controls. The degree to which the individual's profile is unusual can then be equated to the proportion of the population who would have a larger Mahalanobis distance than the individual. Several estimators of this proportion are examined.

Automated biosurveillance data from England and Wales, 1991-2011.

Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991-2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude.

Point and interval estimates of percentile ranks for scores on the Texas Functional Living Scale.

Point and interval estimates of percentile ranks are useful tools in assisting with the interpretation of neurocognitive test results. We provide percentile ranks for raw subscale scores on the Texas Functional Living Scale (TFLS; Cullum, Weiner, & Saine, 2009) using the TFLS standardization sample data (N = 800). Percentile ranks with interval estimates are also provided for the overall TFLS T score.

An improved algorithm for outbreak detection in multiple surveillance systems.

In England and Wales, a large-scale multiple statistical surveillance system for infectious disease outbreaks has been in operation for nearly two decades. This system uses a robust quasi-Poisson regression algorithm to identify abberrances in weekly counts of isolates reported to the Health Protection Agency. In this paper, we review the performance of the system with a view to reducing the number of false reports, while retaining good power to detect genuine outbreaks.

Using regression equations built from summary data in the psychological assessment of the individual case: extension to multiple regression.

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all psychologists are aware that regression equations can be built not only from raw data but also using only basic summary data for a sample, and (b) the computations involved are tedious and prone to error.

Some supplementary methods for the analysis of WAIS-IV index scores in neuropsychological assessment.

Objectives: To develop supplementary methods for the analysis of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) in neuropsychological assessment.

Design And Methods: Psychometric.

Results: The following methods are made available: (a) provision of traditional confidence intervals (CIs) on index scores, (b) expression of the endpoints of CIs as percentile ranks; (c) quantification of the number of abnormally low index scores exhibited by a case and accompanying estimate of the percentage of the normative population expected to exhibit at least this number of low scores; (d) quantification of the reliability and abnormality of index score deviations from an individual's index score mean (thereby offering an alternative to the pairwise approach to index score comparisons available in the WAIS-IV manual); (e) provision of CIs on an individual's deviation scores or pairwise difference scores, (f) estimation of the percentage of the normative population expected to exhibit at least as many abnormal deviations or abnormal pairwise differences as a case; and (g) calculation of a case's Mahalanobis distance index (MDI), thereby providing a multivariate estimate of the overall abnormality of an index score profile.

Some supplementary methods for the analysis of the RBANS.

Supplementary methods for the analysis of the Repeatable Battery for the Assessment of Neuropsychological Status are made available, including (a) quantifying the number of abnormally low Index scores and abnormally large differences exhibited by a case and accompanying this with estimates of the percentages of the normative population expected to exhibit at least this number of low scores and large differences, (b) estimating the overall abnormality of a case's Index score profile using the Mahalanobis Distance Index (MDI), (c) reporting confidence limits on differences between a case's Index scores, and (d) offering the option of applying a sequential Bonferroni correction when testing for reliable differences. With the exception of the MDI, all the methods can be obtained using the formulas and tables provided in this article. However, for the convenience of clinicians, and to reduce the possibility of clerical error, the methods have also been implemented in a computer program.

Single-case research in neuropsychology: a comparison of five forms of t-test for comparing a case to controls.

Five inferential methods employed in single-case studies to compare a case to controls are examined; all of these make use of a t-distribution. It is shown that three of these ostensibly different methods are in fact strictly equivalent and are not fit for purpose; they are associated with grossly inflated Type I errors (these exceed even the error rate obtained when a case's score is converted to a z score and the latter used as a test statistic). When used as significance tests, the two remaining methods (Crawford and Howell's method and a prediction interval method first used by Barton and colleagues) are also equivalent and achieve control of the Type I error rate (the two methods do differ however in other important aspects).

Inferential methods for comparing two single cases.

In neuropsychological single-case studies, it is not uncommon for researchers to compare the scores of two single cases. Classical (and Bayesian) statistical methods are developed for such problems, which, unlike existing methods, refer the scores of the two single cases to a control sample. These methods allow researchers to compare two cases' scores, with or without allowing for the effects of covariates.

Some supplementary methods for the analysis of the Delis-Kaplan Executive Function System.

Supplementary methods for the analysis of the Delis-Kaplan Executive Function System (Delis, Kaplan, & Kramer, 2001) are made available, including (a) quantifying the number of abnormally low achievement scores exhibited by an individual and accompanying this with an estimate of the percentage of the normative population expected to exhibit at least this number of low scores; (b) estimating the overall abnormality of an individual's achievement score profile with the Mahalanobis distance index; (c) calculating a composite executive function index score for an individual and providing accompanying confidence limits; and (d) providing the percentile ranks for an individual's achievement scores and executive index score (in the latter case, confidence limits on scores are also expressed as percentile ranks). With the exception of the Mahalanobis distance index, all the methods can be obtained with the equations and tables provided in this article. However, for the convenience of clinicians and to reduce the possibility of clerical error, the methods have also been implemented in a computer program.

Comparing a single case to a control sample: testing for neuropsychological deficits and dissociations in the presence of covariates.

Existing inferential methods of testing for a deficit or dissociation in the single case are extended to allow researchers to control for the effects of covariates. The new (bayesian) methods provide a significance test, point and interval estimates of the effect size for the difference between the case and controls, and point and interval estimates of the abnormality of a case's score, or standardized score difference. The methods have a wide range of potential applications, e.

Point and interval estimates of effect sizes for the case-controls design in neuropsychology: rationale, methods, implementations, and proposed reporting standards.

It is increasingly common for group studies in neuropsychology to report effect sizes. In contrast this is rarely done in single-case studies (at least in those studies that employ a case-controls design). The present paper sets out the advantages of reporting effect sizes, derives suitable effect size indexes for use in single-case studies, and develops methods of supplementing point estimates of effect sizes with interval estimates.

On comparing a single case with a control sample: an alternative perspective.

Corballis [Corballis, M. C. (2009).

On percentile norms in neuropsychology: proposed reporting standards and methods for quantifying the uncertainty over the percentile ranks of test scores.

Normative data for neuropsychological tests are often presented in the form of percentiles. One problem when using percentile norms stems from uncertainty over the definitional formula for a percentile. (There are three co-existing definitions and these can produce substantially different results.

Bayes' theorem and diagnostic tests in neuropsychology: interval estimates for post-test probabilities.

Most neuropsychologists are aware that, given the specificity and sensitivity of a test and an estimate of the base rate of a disorder, Bayes' theorem can be used to provide a post-test probability for the presence of the disorder given a positive test result (and a post-test probability for the absence of a disorder given a negative result). However, in the standard application of Bayes' theorem the three quantities (sensitivity, specificity, and the base rate) are all treated as fixed, known quantities. This is very unrealistic as there may be considerable uncertainty over these quantities and therefore even greater uncertainty over the post-test probability.

A convenient method of obtaining percentile norms and accompanying interval estimates for self-report mood scales (DASS, DASS-21, HADS, PANAS, and sAD).

Background: A series of recent papers have reported normative data from the general adult population for commonly used self-report mood scales.

Aims: To bring together and supplement these data in order to provide a convenient means of obtaining percentile norms for the mood scales.

Method: A computer program was developed that provides point and interval estimates of the percentile rank corresponding to raw scores on the various self-report scales.

On the reliability and standard errors of measurement of contrast measures from the D-KEFS.

A formula for the reliability of difference scores was used to estimate the reliability of Delis-Kaplan Executive Function System (D-KEFS; Delis et al., 2001) contrast measures from the reliabilities and correlations of their components. In turn these reliabilities were used to calculate standard errors of measurement.

Percentiles please: the case for expressing neuropsychological test scores and accompanying confidence limits as percentile ranks.

Many commentators on neuropsychological assessment stress the disadvantages of expressing test scores in the form of percentile ranks. As a result, there is a danger of losing sight of the fundamentals: percentile ranks express scores in a form that is of greater relevance to the neuropsychologist than any alternative metric because they tell us directly how common or uncommon such scores are in the normative population. We advocate that, in addition to expressing scores on a standard metric, neuropsychologists should also routinely record the percentile rank of all test scores so that the latter are available when attempting to reach a formulation.

Use of expert knowledge in evaluating costs and benefits of alternative service provisions: a case study.

Objectives: A treatment pathway model was developed to examine the costs and benefits of the current bowel cancer service in England and to evaluate potential alternatives in service provision. To use the pathway model, various parameters and probability distributions had to be specified. They could not all be determined from empirical evidence and, instead, expert opinion was elicited in the form of statistical quantities that gave the required information.

Comparison of a single case to a control or normative sample in neuropsychology: development of a Bayesian approach.

Frequentist methods are available for comparison of a patient's test score (or score difference) to a control or normative sample; these methods also provide a point estimate of the percentage of the population that would obtain a more extreme score (or score difference) and, for some problems, an accompanying interval estimate (i.e., confidence limits) on this percentage.

On the "optimal" size for normative samples in neuropsychology: capturing the uncertainty when normative data are used to quantify the standing of a neuropsychological test score.

Bridges and Holler (2007) have provided a useful reminder that normative data are fallible. Unfortunately, however, their paper misleads neuropsychologists as to the nature and extent of the problem. We show that the uncertainty attached to the estimated z score and percentile rank of a given raw score is much larger than they report and that it varies as a function of the extremity of the raw score.

Using regression equations built from summary data in the neuropsychological assessment of the individual case.

Regression equations have many useful roles in neuropsychological assessment. This article is based on the premise that there is a large reservoir of published data that could be used to build regression equations; these equations could then be used to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all neuropsychologists are aware that equations can be built with only basic summary data for a sample and (b) the computations involved are tedious and prone to error.

Estimating the percentage of the population with abnormally low scores (or abnormally large score differences) on standardized neuropsychological test batteries: a generic method with applications.

Information on the rarity or abnormality of an individual's test scores (or test score differences) is fundamental in interpreting the results of a neuropsychological assessment. If a standardized battery of tests is administered, the question arises as to what percentage of the healthy population would be expected to exhibit one or more abnormally low test scores (and, in general, j or more abnormally low scores). Similar issues arise when the concern is with the number of abnormal pairwise differences between an individual's scores on the battery, or when an individual's scores on each component of the battery are compared with the individual's mean score.