Statistics in medicine
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Statistics in medicine · Aug 2000
Comparative StudyFirst steps in analysing NHS waiting times: avoiding the 'stationary and closed population' fallacy.
The aim of this paper is to demonstrate the effect of excluding incomplete observations and competing events when calculating cross-sectional measures of NHS waiting times, and to obtain a more accurate estimate of the 'time-to-admission' of those listed on NHS waiting lists using life-table methods. The official 'times-since-enrollment' of all elective 'admissions' in England, 1 July to 31 December 1994 inclusive, were extracted from Hospital Episode Statistics. The official 'times-to-census' of all those on a waiting list in England at 30 September 1994 were obtained from aggregated KH07 data. ⋯ The Department of Health currently presents the 'time-since-enrollment' of those admitted as though it indicates how long all patients can expect to wait for admission. The consequent bias in published summary statistics incorrectly quantifies the real experience of patients. It is recommended that calculation of waiting times from KH07 census counts and Hospital Episode Statistics be reconsidered in the light of what patients, clinicians, managers and politicians need to know about treatment delay.
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Statistics in medicine · Jul 2000
Review Comparative StudyHeterogeneity and statistical significance in meta-analysis: an empirical study of 125 meta-analyses.
For meta-analysis, substantial uncertainty remains about the most appropriate statistical methods for combining the results of separate trials. An important issue for meta-analysis is how to incorporate heterogeneity, defined as variation among the results of individual trials beyond that expected from chance, into summary estimates of treatment effect. Another consideration is which 'metric' to use to measure treatment effect; for trials with binary outcomes, there are several possible metrics, including the odds ratio (a relative measure) and risk difference (an absolute measure). ⋯ We present two exceptions to these observations, which derive from the weights assigned to individual trial estimates. We discuss the implications of these findings for selection of a metric for meta-analysis and incorporation of heterogeneity into summary estimates. Published in 2000 by John Wiley & Sons, Ltd.
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Statistics in medicine · Jun 2000
Modern psychometric methods for detection of differential item functioning: application to cognitive assessment measures.
Cognitive screening tests and items have been found to perform differently across groups that differ in terms of education, ethnicity and race. Despite the profound implications that such bias holds for studies in the epidemiology of dementia, little research has been conducted in this area. Using the methods of modern psychometric theory (in addition to those of classical test theory), we examined the performance of the Attention subscale of the Mattis Dementia Rating Scale. ⋯ These methods have been used previously to examine bias in screening measures across education and ethnic and racial subgroups. In addition to the important epidemiological applications of ensuring that screening measures and neuropsychological tests used in diagnoses are free of bias so that more culture-fair classifications will result, these methods are also useful for the examination of site differences in large multi-site clinical trials. It is recommended that these methods receive wider attention in the medical statistical literature.
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Statistics in medicine · Apr 2000
Review Comparative StudyStrategies for comparing treatments on a binary response with multi-centre data.
This paper surveys methods for comparing treatments on a binary response when observations occur for several strata. A common application is multi-centre clinical trials, in which the strata refer to a sample of centres or sites of some type. Questions of interest include how one should summarize the difference between the treatments, how one should make inferential comparisons, how one should investigate whether treatment-by-centre interaction exists, how one should describe effects when interaction exists, whether one should treat centres and centre-specific treatment effects as fixed or random, and whether centres that have either 0 successes or 0 failures should contribute to the analysis. This article discusses these matters in the context of various strategies for analysing such data, in particular focusing on special problems presented by sparse data.
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Analyses of dose response studies should separate the question of the existence of a dose response relationship from questions of functional form and finding the optimal dose. A well-chosen contrast among the estimated effects of the studied doses can make a powerful test for detecting the existence of a dose response relationship. A contrast-based test attains its greatest power when the pattern of the coefficients has the same shape as the true dose response relationship. ⋯ Thus, a primary test based on a single contrast is often risky. Two (or more) appropriately chosen contrasts can assure sufficient power to justify the cost of a multiplicity adjustment. An example shows the success of a two-contrast procedure in detecting dose response, which had frustrated several standard procedures.