Biomedical subjects

Terry N Flynn

Publications and source records attributed to Terry N Flynn.

4 recordsLinked to original sources

PubMed2006

Best--worst scaling: What it can do for health care research and how to do it.

Statements like "quality of care is more highly valued than waiting time" can neither be supported nor refuted by comparisons of utility parameters from a traditional discrete choice experiment (DCE). Best--worst scaling can overcome this problem because it asks respondents to perform a different choice task. However, whilst the nature of the best--worst task is generally understood, there are a number of issues relating to the design and analysis of a best--worst choice experiment that require further exposition. This paper illustrates how to aggregate and analyse such data and using a quality of life pilot study demonstrates how richer insights can be drawn by the use of best--worst tasks.

Consumer Behavior↗

PubMed2005

Cluster randomized trials: another problem for cost-effectiveness ratios.

OBJECTIVES: This work has investigated under what conditions cost-effectiveness data from a cluster randomized trial (CRT) are suitable for analysis using a cluster-adjusted nonparametric bootstrap. The bootstrap's main advantages are in dealing with skewed data and its ability to take correlations between costs and effects into account. However, there are known theoretical problems with a commonly used cluster bootstrap procedure, and the practical implications of these require investigation. METHODS: Simulations were used to estimate the coverage of confidence intervals around incremental cost-effectiveness ratios from CRTs using two bootstrap methods. RESULTS: The bootstrap gave excessively narrow confidence intervals, but there was evidence to suggest that, when the number of clusters per treatment arm exceeded 24, it might give acceptable results. The method that resampled individuals as well as clusters did not perform well when cost and effectiveness data were correlated. CONCLUSIONS: If economic data from such trials are to be analyzed adequately, then there is a need for further investigations of more complex bootstrap procedures. Similarly, further research is required on methods such as the net benefit approach.

Cluster Analysis↗

PubMed2004

Use of the bootstrap in analysing cost data from cluster randomised trials: some simulation results.

BACKGROUND: This work has investigated under what conditions confidence intervals around the differences in mean costs from a cluster RCT are suitable for estimation using a commonly used cluster-adjusted bootstrap in preference to methods that utilise the Huber-White robust estimator of variance. The bootstrap's main advantage is in dealing with skewed data, which often characterise patient costs. However, it is insufficiently well recognised that one method of adjusting the bootstrap to deal with clustered data is only valid in large samples. In particular, the requirement that the number of clusters randomised should be large would not be satisfied in many cluster RCTs performed to date. METHODS: The performances of confidence intervals for simple differences in mean costs utilising a robust (cluster-adjusted) standard error and from two cluster-adjusted bootstrap procedures were compared in terms of confidence interval coverage in a large number of simulations. Parameters varied included the intracluster correlation coefficient, the sample size and the distributions used to generate the data. RESULTS: The bootstrap's advantage in dealing with skewed data was found to be outweighed by its poor confidence interval coverage when the number of clusters was at the level frequently found in cluster RCTs in practice. Simulations showed that confidence intervals based on robust methods of standard error estimation achieved coverage rates between 93.5% and 94.8% for a 95% nominal level whereas those for the bootstrap ranged between 86.4% and 93.8%. CONCLUSION: In general, 24 clusters per treatment arm is probably the minimum number for which one would even begin to consider the bootstrap in preference to traditional robust methods, for the parameter combinations investigated here. At least this number of clusters and extremely skewed data would be necessary for the bootstrap to be considered in favour of the robust method. There is a need for further investigation of more complex bootstrap procedures if economic data from cluster RCTs are to be analysed appropriately.

Analysis of Variance↗

PubMed2002

Recruitment strategies in a cluster randomized trial--cost implications.

The presence of a non-zero intracluster correlation coefficient in cluster randomized trial data has well-known statistical implications for trial design, in particular, inflating the required sample size for given specifications. However, problems in recruitment are common in such trials and there may be different costs of recruitment resulting from different recruitment strategies. Examples of how such differences arise are taken from cluster randomized trials and more intuitive methods of describing clustering are summarized which, in conjunction with such cost issues, may provide a framework that enables triallists to consider explicitly the trade-off between power and cost that is often inherent in such trials.

Adolescent↗