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Patrick J Twomey

Publications and source records attributed to Patrick J Twomey.

14 recordsLinked to original sources
PubMed2006

How to use difference plots in quantitative method comparison studies.

Quantitative method comparison studies are fundamental to clinical biochemistry. The interpretation of quantitative method comparison studies relied heavily on correlation and regression methods until Bland and Altman first described the concept of absolute difference plots. Since then, many clinical biochemistry journals advocate the use of difference plots; however, there is a lot of ignorance about the validity as well as the pros and cons of the various difference plots. The most important issue in quantitative method comparisons studies is to determine limits of agreement that are valid across the whole range of values in the study so that correct data interpretation and conclusions occur. This article discusses validity as well as the pros and cons of difference plots and provides means to determine limits of agreement that are valid across the whole range of values in method comparison studies. Accordingly, correct data interpretation will be more likely and better conclusions should be arrived as a result.

Biomedical Research↗
PubMed2004

Concordance evaluation of coronary risk scores: implications for cardiovascular risk screening.

OBJECTIVE: To assess the similarities and differences in predicted high-risk individuals identified by different cardiovascular risk calculation algorithms Research design and methods: A representative population of 10000 individuals was modelled in a computer using baseline data from the National Health Survey for England. The effects of biological groups identified by each calculator depend on the variation in each major model parameters were then applied to each hypothetical individual. The predictive capacities of 3 different risk identification systems based on computer calculation (the Framingham algorithm), or on tabular methods (the Sheffield tables and the General Rule to Enable Atheroma Treatment) were evaluated. RESULTS: All three models predict that similar numbers would receive treatment with 2.9 and 10% receiving treatment at 30 and 15% 10 year risk thresholds, respectively. However, concordance is limited as 0.3 or 6.8% are positive on all three systems; 1.6 or 9.7% on any two calculators at the 30 and 15% thresholds, respectively. The risk baseline assumptions in each model. CONCLUSION: Care needs to be taken with applying risk calculators to populations different from which they were derived. Any cardiovascular risk scoring system needs to be thoroughly evaluated against epidemiological data before it is introduced and also needs to be updated in line with changing trends in risk factors.

Algorithms↗
PubMed2003

Issues to consider when attempting to achieve the American Diabetes Association clinical quality requirement for haemoglobin A1c.

Diabetes mellitus is a chronic disease that is monitored by measurement of haemoglobin A1c (A1C) as an index of glycaemic control. The limitations of using A1C, given the consensus clinical practice recommendations made by the American Diabetes Association, need to be better understood by clinicians. These include bias between DCCT-aligned methods, analytical variation and intra-individual variation. As intra-individual variation is the principal factor determining variation in A1C in rolling means of the last four A1C results and to stable patients, clinicians may need to monitor A1C more frequently to achieve precise results. Laboratories need to report current values and the analyse six internal quality control specimens for each analytical run. 'Delta check' criteria ought to be applied and results reported to highlight acute deviations in A1C. Such procedures will aid the attainment of the clinical quality requirements and give appropriate results for audit purposes.

Bias↗