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Anthony C Davison

Publications and source records attributed to Anthony C Davison.

2 recordsLinked to original sources

Assessment and analysis of mechanical allodynia-like behavior induced by spared nerve injury (SNI) in the mouse.

Experimental models of peripheral nerve injury have been developed to study mechanisms of neuropathic pain. In the spared nerve injury (SNI) model in rats, the common peroneal and tibial nerves are injured, producing consistent and reproducible pain hypersensitivity in the territory of the spared sural nerve. In this study, we investigated whether SNI in mice is also a valid model system for neuropathic pain. SNI results in a significant decrease in withdrawal threshold in SNI-operated mice. The effect is very consistent between animals and persists for the four weeks of the study. We also determined the relative frequency of paw withdrawal for each of a series of 11 von Frey hairs. Analysis of response frequency using a mixed-effects model that integrates all variables (nerve injury, paw, gender, and time) shows a very stable effect of SNI over time and also reveals subtle divergences between variables, including gender-based differences in mechanical sensitivity. We tested two variants of the SNI model and found that injuring the tibial nerve alone induces mechanical hypersensitivity, while injuring the common peroneal and sural nerves together does not induce any significant increase in mechanical sensitivity in the territory of the spared tibial nerve. SNI induces a mechanical allodynia-like response in mice and we believe that our improved method of assessment and data analysis will reveal additional internal and external variability factors in models of persistent pain. Use of this model in genetically altered mice should be very effective for determining the mechanisms involved in neuropathic pain.

Animals↗

Non-parametric bootstrap confidence intervals for the intraclass correlation coefficient.

The intraclass correlation coefficient rho plays a key role in the design of cluster randomized trials. Estimates of rho obtained from previous cluster trials and used to inform sample size calculation in planned trials may be imprecise due to the typically small numbers of clusters in such studies. It may be useful to quantify this imprecision. This study used simulation to compare different methods for assigning bootstrap confidence intervals to rho for continuous outcomes from a balanced design. Data were simulated for combinations of numbers of clusters (10, 30, 50), intraclass correlation coefficients (0.001, 0.01, 0.05, 0.3) and outcome distributions (normal, non-normal continuous). The basic, bootstrap-t, percentile, bias corrected and bias corrected accelerated bootstrap intervals were compared with new methods using the basic and bootstrap-t intervals applied to a variance stabilizing transformation of rho. The standard bootstrap methods provided coverage levels for 95 per cent intervals that were markedly lower than the nominal level for data sets with only 10 clusters, and only provided close to 95 per cent coverage when there were 50 clusters. Application of the bootstrap-t method to the variance stabilizing transformation of rho improved upon the performance of the standard bootstrap methods, providing close to nominal coverage.

Cluster Analysis↗