A new, fast LDI for assessment of burns: a multi-centre clinical evaluation.

INTRODUCTION: Laser Doppler imaging (LDI) provides early accurate determination of wound healing potential. LDI can scan large areas of up to 2500 cm2 within 2 min. This duration may require additional sedation in a mobile, uncooperative child. In five burn centres a faster Laser Doppler Line Scanner (LDLS) was assessed. This new imager scans 300 cm2 in 4s with potential benefit for patients and operators. The aim of this study was to assess the accuracy and convenience of the LDLS and to compare this with an established LDI imager. METHODS: Outpatients and admitted patients were included. LDI and LDLS images were obtained between 2 and 5 days post burn (PB). Photographs and records of wound and healing were obtained on day of scan and at 14 and 21 days PB. This provided data on three categories of burn wounds: healing within 14 days, 14-21 days and not healed within 21 days. RESULTS: The analysis included 596 burn areas from 204 burns patients. An accuracy of 94.2% was found with use of the LDLS compared with 94.4% for the original LDI imager. CONCLUSIONS: The high accuracy of the new line-scan imager was comparable to that of the traditional LDI. Its size and mobility enabled easier ward and outpatient use. The higher scan speed was particularly beneficial for scans in paediatric patients.

Meta-analysis inside and outside particle physics: two traditions that should converge?

The use of meta-analysis in medicine and epidemiology really took off in the 1970s. However, in high-energy physics, the Particle Data Group has been carrying out meta-analyses of measurements of particle masses and other properties since 1957. Curiously, there has been virtually no interaction between those working inside and outside particle physics. In this paper, we use statistical models to study two major differences in practice. The first is the usefulness of systematic errors, which physicists are now beginning to quote in addition to statistical errors. The second is whether it is better to treat heterogeneity by scaling up errors as do the Particle Data Group or by adding a random effect as does the rest of the community. Besides fitting models, we derive and use an exact test of the error-scaling hypothesis. We also discuss the other methodological differences between the two streams of meta-analysis. Our conclusion is that systematic errors are not currently very useful and that the conventional random effects model, as routinely used in meta-analysis, has a useful role to play in particle physics. The moral we draw for statisticians is that we should be more willing to explore 'grassroots' areas of statistical application, so that good statistical practice can flow both from and back to the statistical mainstream. Copyright © 2012 John Wiley & Sons, Ltd.

Using ordinal logistic regression to evaluate the performance of laser-Doppler predictions of burn-healing time.

BACKGROUND: Laser-Doppler imaging (LDI) of cutaneous blood flow is beginning to be used by burn surgeons to predict the healing time of burn wounds; predicted healing time is used to determine wound treatment as either dressings or surgery. In this paper, we do a statistical analysis of the performance of the technique. METHODS: We used data from a study carried out by five burn centers: LDI was done once between days 2 to 5 post burn, and healing was assessed at both 14 days and 21 days post burn. Random-effects ordinal logistic regression and other models such as the continuation ratio model were used to model healing-time as a function of the LDI data, and of demographic and wound history variables. Statistical methods were also used to study the false-color palette, which enables the laser-Doppler imager to be used by clinicians as a decision-support tool. RESULTS: Overall performance is that diagnoses are over 90% correct. Related questions addressed were what was the best blood flow summary statistic and whether, given the blood flow measurements, demographic and observational variables had any additional predictive power (age, sex, race, % total body surface area burned (%TBSA), site and cause of burn, day of LDI scan, burn center). It was found that mean laser-Doppler flux over a wound area was the best statistic, and that, given the same mean flux, women recover slightly more slowly than men. Further, the likely degradation in predictive performance on moving to a patient group with larger %TBSA than those in the data sample was studied, and shown to be small. CONCLUSION: Modeling healing time is a complex statistical problem, with random effects due to multiple burn areas per individual, and censoring caused by patients missing hospital visits and undergoing surgery. This analysis applies state-of-the art statistical methods such as the bootstrap and permutation tests to a medical problem of topical interest. New medical findings are that age and %TBSA are not important predictors of healing time when the LDI results are known, whereas gender does influence recovery time, even when blood flow is controlled for.The conclusion regarding the palette is that an optimum three-color palette can be chosen 'automatically', but the optimum choice of a 5-color palette cannot be made solely by optimizing the percentage of correct diagnoses.

Identifying space-time disease clusters.

A cluster of cases of disease that are close both in space and in time is suggestive of an infectious aetiology. We present statistical tests for space-time clusters of disease for the two situations where the population at risk is either known or unknown as a function of space and time. The tests are derived using standard statistical methodology from a simple mathematical model of disease spread, i.e. they are derived as score tests from a likelihood function in which the infection process is modelled as a point process whose intensity becomes greater near an infector. A problem for such tests is that, when investigating whether or not a disease may be of infectious origin, the space and time distances characterising closeness to an infection are very likely to be unknown. The proposed methodology copes with this difficulty in a statistically acceptable way, without requiring multiple tests whose interpretation would be doubtful. When the underlying population size is unknown, the test reduces to a modification of the Knox test. An example of its use is given as epidemiology, risk, space-time cluster, likelihood and Knox test.

Sensitivity analysis for healthcare models fitted to data by statistical methods.

After fitting complex models to data using statistical methods, a sensitivity analysis can be carried out. This determines which parts of a model are causing the bulk of the uncertainty in the model predictions (model "output"), and is a decision-support tool for the modeller who contemplates refining a model further or collecting additional data. A simple methodology for carrying out a sensitivity analysis is described. It is envisaged that such a relatively quick insight-generating step would precede the use of a more formal decision-theoretic approach that would address specific questions. Its use is illustrated using a model for breast cancer screening previously published in this journal. A simpler 3-parameter screening model is used in a simulation study of the error of the method as a function of sample size.

Comment on the paper, "Growth of yeast cultures as in vitro model for investigationg homoeopathic remedies"

A recent paper by Steffen repeating earlier work by Jones et al. failed to confirm any effect of potencies of Pulsatilla on the growth rate of cultures of Saccharomyces cerevisiae which had previously been reported. We find that using a more specific statistical test on the results given by Steffen it can be shown that these results contain almost the same periodicity with potency that Jones et al. reported, although only corresponding to a variation of +or- l.5//about the mean value