Biomedical subjects

Paul H Garthwaite

Publications and source records attributed to Paul H Garthwaite.

12 recordsLinked to original sources

PubMed2006

Detecting dissociations in single-case studies: Type I errors, statistical power and the classical versus strong distinction.

Dissociations observed in single-case studies play an important role in building and testing theory in neuropsychology; therefore the criteria used to identify their presence should be subjected to empirical scrutiny. Extending work on classical dissociations, Monte Carlo simulation is used to examine the Type I error rate for two methods of detecting strong dissociations. When a Type I error was defined as misclassifying a healthy control, error rates were low for both methods. When Type I errors were defined as misclassifying patients with strictly equivalent deficits on two tasks, error rates for strong dissociations were high for the "conventional" criteria and were very high when cases misclassified as exhibiting either form of dissociation (strong or classical) were combined (maximum = 55.1%). The power to detect strong and classical dissociations was generally low-to-moderate, but was moderate-to-high in most scenarios when power was defined as the ability to detect either form of dissociation. In most scenarios patients with strong dissociations were more likely to be classified as exhibiting classical dissociations. The results question the practical utility of the distinction between strong and classical dissociations regardless of the criteria employed to test for their presence.

Case-Control Studies↗

PubMed2006

Comparing patients' predicted test scores from a regression equation with their obtained scores: a significance test and point estimate of abnormality with accompanying confidence limits.

In contrast to the standard use of regression, in which an individual's score on the dependent variable is unknown, neuropsychologists are often interested in comparing a predicted score with a known obtained score. Existing inferential methods use the standard error for a new case (s-subN+1) to provide confidence limits on a predicted score and hence are tailored to the standard usage. However, s-subN+1 can be used to test whether the discrepancy between a patient's predicted and obtained scores was drawn from the distribution of discrepancies in a control population. This method simultaneously provides a point estimate of the percentage of the control population that would exhibit a larger discrepancy. A method for obtaining confidence limits on this percentage is also developed. These methods can be used with existing regression equations and are particularly useful when the sample used to generate a regression equation is modest in size. Monte Carlo simulations confirm the validity of the methods, and computer programs that implement them are described and made available.

Confidence Intervals↗

PubMed2005

Testing for a deficit in single-case studies: effects of departures from normality.

In neuropsychological single-case research inferences concerning a patient's cognitive status are often based on referring the patient's test score to those obtained from a modestly sized control sample. Two methods of testing for a deficit (z and a method proposed by Crawford and Howell [Crawford, J. R. & Howell, D. C. (1998). Comparing an individual's test score against norms derived from small samples. The Clinical Neuropsychologist, 12, 482-486]) both assume the control distribution is normal but this assumption will often be violated in practice. Monte Carlo simulation was employed to study the effects of leptokurtosis and the combination of skew and leptokurtosis on the Type I error rates for these two methods. For Crawford and Howell's method, leptokurtosis produced only a modest inflation of the Type I error rate when the control sample N was small-to-modest in size and error rates were lower than the specified rates at larger N. In contrast, the combination of leptokurtosis and skew produced marked inflation of error rates for small Ns. With a specified error rate of 5%, actual error rates as high as 14.31% and 9.96% were observed for z and Crawford and Howell's method respectively. Potential solutions to the problem of non-normal data are evaluated.

Bias↗

PubMed2005

Bayesian analysis of misclassified binary data from a matched case-control study with a validation sub-study.

Bayesian methods are proposed for analysing matched case-control studies in which a binary exposure variable is sometimes measured with error, but whose correct values have been validated for a random sample of the matched case-control sets. Three models are considered. Model 1 makes few assumptions other than randomness and independence between matched sets, while Models 2 and 3 are logistic models, with Model 3 making additional distributional assumptions about the variation between matched sets. With Models 1 and 2 the data are examined in two stages. The first stage analyses data from the validation sample and is easy to perform; the second stage analyses the main body of data and requires MCMC methods. All relevant information is transferred between the stages by using the posterior distributions from the first stage as the prior distributions for the second stage. With Model 3, a hierarchical structure is used to model the relationship between the exposure probabilities of the matched sets, which gives the potential to extract more information from the data. All the methods that are proposed are generalized to studies in which there is more than one control for each case. The Bayesian methods and a maximum likelihood method are applied to a data set for which the exposure of every patient was measured using both an imperfect measure that is subject to misclassification, and a much better measure whose classifications may be treated as correct. To test methods, the latter information was suppressed for all but a random sample of matched sets.

Bayes Theorem↗

PubMed2005

Testing for suspected impairments and dissociations in single-case studies in neuropsychology: evaluation of alternatives using monte carlo simulations and revised tests for dissociations.

In neuropsychological single-case studies, a patient is compared with a small control sample. Methods of testing for a deficit on Task X, or a significant difference between Tasks X and Y, either treat the control sample statistics as parameters (using z and zD) or use modified t tests. Monte Carlo simulations demonstrated that if z is used to test for a deficit, the Type I error rate is high for small control samples, whereas control of the error rate is essentially perfect for a modified t test. Simulations on tests for differences revealed that error rates were very high for zD. A new method of testing for a difference (the revised standardized difference test) achieved good control of the error rate, even with very small sample sizes. A computer program that implements this new test (and applies criteria to test for classical and strong dissociations) is made available.

Computer Simulation↗

PubMed2005

Evaluation of criteria for classical dissociations in single-case studies by Monte Carlo simulation.

The conventional criteria for a classical dissociation in single-case studies require that a patient be impaired on one task and within normal limits on another. J. R. Crawford and P. H. Garthwaite (2005) proposed an additional criterion, namely, that the patient's (standardized) difference on the two tasks should differ from the distribution of differences in controls. Monte Carlo simulation was used to evaluate these criteria. When Type I errors were defined as falsely concluding that a control case exhibited a dissociation, error rates were high for the conventional criteria but low for Crawford and Garthwaite's criteria. When Type I error rates were defined as falsely concluding that a patient with equivalent deficits on the two tasks exhibited a dissociation, error rates were very high for the conventional criteria but acceptable for the latter criteria. These latter criteria were robust in the face of nonnormal control data. The power to detect classical dissociations was studied.

Dissociative Disorders↗

PubMed2004

Statistical methods for single-case studies in neuropsychology: comparing the slope of a patient's regression line with those of a control sample.

Performance on some neuropsychological tests is best expressed as the slope of a regression line. Examples include the quantification of performance on tests designed to assess the accuracy of time estimation or distance estimation. The present paper presents methods for comparing a patient's performance with a control or normative sample when performance is expressed as slope. The methods test if there is a significant difference between a patient's slope and those obtained from controls, yield an estimate of the abnormality of the patient's slope, and provide confidence limits on the level of abnormality. The methods can be used with control samples of any size and will therefore be of particular relevance to single-case researchers. A method for comparing the difference between a patient's scores on two measures with the differences observed in controls is also described (one or both measures can be slopes). The methods require only summary statistics (rather than the raw data from the normative or control sample); it is hoped that this feature will encourage the development of norms for tasks that use slopes to quantify performance. Worked examples of the statistical methods are provided using neuropsychological data and a computer program (for PCs) that implements the methods is described and made available.

Controlled Clinical Trials as Topic↗

PubMed2003

Wanted: fully operational definitions of dissociations in single-case studies.

In contrast to the careful consideration given to the issue of what we can infer from dissociations in single-case studies, the more basic question of how we decide whether a dissociation is present has been relatively neglected. Proposals are made for fully operational definitions of a deficit, classical and strong dissociations, and double dissociations. In developing these definitions it was assumed that they should be based on the use of inferential rather than descriptive statistical methods. The scope of these definitions is limited to typical single-case studies in which patients are compared to control samples of a modest size. The operational definition of a classical dissociation incorporates a requirement that a patient's performance on Task X is significantly different from Task Y, in addition to the "standard" requirement that the patient has a deficit on Task X and is within normal limits on Task Y. We ran a simulation to estimate the Type I error rates when the criteria for dissociations are applied and found these to be low (Type I errors were defined as identifying an individual from the control population as having a dissociation). The inferential methods for testing whether the various criteria are met make use of t-distributions. These methods are contrasted with the widespread use of z to test for a deficit or a difference between tasks. In the latter approach the statistics of the control sample are treated as parameters; this is not appropriate when, as is normally the case, the control sample size is modest in size.

Dissociative Disorders↗

PubMed2003

Intra-individual measures of association in neuropsychology: inferential methods for comparing a single case with a control or normative sample.

Performance on some neuropsychological tests is best expressed as an intra-individual measure of association (such as a parametric or non-parametric correlation coefficient or the slope of a regression line). Examples of the use of intra-individual measures of association (IIMAs) include the quantification of performance on tests designed to assess temporal order memory or the accuracy of time estimation. The present paper presents methods for comparing a patient's performance with a control or normative sample when performance is expressed as an IIMA. The methods test if there is a significant difference between a patient's IIMA and those obtained from controls, yield an estimate of the abnormality of the patient's IIMA, and provide confidence limits on the level of abnormality. The methods can be used with normative or control samples of any size and will therefore be of particular relevance to single-case researchers. A method for comparing the difference between a patient's scores on two measures with the differences observed in controls is also described (one or both measures can be IIMAs). All the methods require only summary statistics (rather than the raw data from the normative or control sample); it is hoped that this feature will encourage the development of norms for tasks that use IIMAs to quantify performance. Worked examples of the statistical methods are provided using data from a clinical case and controls. A computer program (for PCs) that implements the methods is described and made available.

Adult↗

PubMed2002

Investigation of the single case in neuropsychology: confidence limits on the abnormality of test scores and test score differences.

Neuropsychologists often need to estimate the abnormality of an individual patient's test score, or test score discrepancies, when the normative or control sample against which the patient is compared is modest in size. Crawford and Howell [The Clinical Neuropsychologist 12 (1998) 482] and Crawford et al. [Journal of Clinical and Experimental Neuropsychology 20 (1998) 898] presented methods for obtaining point estimates of the abnormality of test scores and test score discrepancies in this situation. In the present study, we extend this work by developing methods of setting confidence limits on the estimates of abnormality. Although these limits can be used with data from normative or control samples of any size, they will be most useful when the sample sizes are modest. We also develop a method for obtaining point estimates and confidence limits on the abnormality of a discrepancy between a patient's mean score on k-tests and a test entering into that mean. Computer programs that implement the formulae for the confidence limits (and point estimates) are described and made available.

Brain Damage, Chronic↗

PubMed2002

A simple Bayesian analysis of misclassified binary data with a validation substudy.

A two-stage Bayesian method is presented for analyzing case-control studies in which a binary variable is sometimes measured with error but the correct values of the variable are known for a random subset of the study group. The first stage of the method is analytically tractable and MCMC methods are used for the second stage. The posterior distribution from the first stage becomes the prior distribution for the second stage, thus transferring all relevant information between the stages. The method makes few distributional assumptions and requires no asymptotic approximations. It is computationally fast and can be run using standard software. It is applied to two data sets that have been analyzed by other methods, and results are compared.

Anti-Bacterial Agents↗

PubMed2002

Assessing the learning curve effect in health technologies. Lessons from the nonclinical literature.

INTRODUCTION: Many health technologies exhibit some from of learning effect, and this represents a barrier to rigorous assessment. It has been shown that the statistical methods used are relatively crude. Methods to describe learning curves in fields outside medicine, for example, psychology and engineering, may be better. METHODS: To systematically search non-health technology assessment literature (for example, PsycLit and Econlit databases) to identify novel statistical techniques applied to learning curves. RESULTS: The search retrieved 9,431 abstracts for assessment, of which 18 used a statistical technique for analyzing learning effects that had not previously been identified in the clinical literature. The newly identified methods were combined with those previously used in health technology assessment, and categorized into four groups of increasing complexity: a) exploratory data analysis; b) simple data analysis; c) complex data analysis; and d) generic methods. All the complex structured data techniques for analyzing learning effects were identified in the nonclinical literature, and these emphasized the importance of estimating intra- and interindividual learning effects. CONCLUSION: A good dividend of more sophisticated methods was obtained by searching in nonclinical fields. These methods now require formal testing on health technology data sets.

Biomedical Technology↗