Statistical methods in medical research
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Stat Methods Med Res · Dec 2003
ReviewMeta-analyses involving cluster randomization trials: a review of published literature in health care.
Throughout the 1980s and 1990s cluster randomization trials have been increasingly used to evaluate effectiveness of health care intervention. Such trials have raised several methodologic challenges in analysis. Meta-analyses involving cluster randomization trials are becoming common in the area of health care intervention. ⋯ In conclusion, some difficulties in the quantitative synthesis procedures were found in the meta-analyses involving cluster randomization trials. Recommendations in the applications of approaches to some specific situations in a binary outcome variable have also been provided. There are still, however, several methodologic issues of the meta-analyses involving cluster randomization trials that need to be investigated further.
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Stat Methods Med Res · Oct 2001
Statistical methods for the meta-analysis of cluster randomization trials.
Cluster randomization trials have become a very attractive research strategy, particularly for the evaluation of health service interventions. The need to conduct meta-analyses of such trials is also becoming more common. ⋯ Statistical methods for constructing inferences for a summary intervention odds ratio include those based on Mantel-Haenszel procedures, the ratio estimator approach, Woolf procedures and generalized estimating equations using robust variance estimation. The advantages and disadvantages of each method are discussed in the context of an example.
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Stat Methods Med Res · Oct 2000
ReviewStatistical description of interrater variability in ordinal ratings.
Ordinal categorical assessments are common in medical practice and in research. Variability in such measurements amongst raters making the assessments can be problematic. In this paper we consider how such variability can be described statistically. ⋯ The method enables description of interrater variability when raters are a random sample from some population as opposed to the traditional setting in which only a few selected raters provide assessments. Advantages of this approach relative to current approaches include the following: (1) it provides a simple visual summary of the rating data, (2) description is closely linked to familiar methods for describing variability in continuous measurements, (3) interpretation is straightforward, and (4) a large sample of raters can be accommodated with ease. We illustrate the method on simulated ordinal data representing radiologists' ratings of mammography images and on rating data from a national image reading study of mammography screening.
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Meta-analysis is now a widely used technique for summarizing evidence from multiple studies. Publication bias, the bias induced by the fact that research with statistically significant results is potentially more likely to be submitted and published than work with null or non-significant results, poses a threat to the validity of such analyses. The implication of this is that combining only the identified published studies uncritically may lead to an incorrect, usually over optimistic, conclusion. ⋯ While statistical methods to test for its presence are starting be used, they do not address the problem of how to proceed if publication bias is suspected. This paper provides a review of methods, which can be employed as a sensitivity analysis to assess the likely impact of publication bias on a meta-analysis. It is hoped that this will raise awareness of such methods, and promote their use and development, as well as provide an agenda for future research.
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Stat Methods Med Res · Jun 2000
ReviewDisease surveillance and data collection issues in epidemic modelling.
This paper is founded on a tutorial session given to the School on Modern Statistical Methods in Medical Research which was held at the International Centre for Theoretical Physics, Trieste in September 1999. We review the aims, scope and purposes of infectious disease surveillance including determining transmission information to underpin model structure and parameterization in epidemic modelling. The practical problems inherent in collecting surveillance data are illustrated by a study of HIV/AIDS in Cambodia. We also review the basic elements of mathematical models developed to represent the transmission dynamics of infectious diseases, and discuss reasons for the gap between mathematical epidemic models and available data.