Two methods of quantifying heterogeneity between studies in meta-analysis were studied. One method quantified the proportion of the total variance of the effect estimate due to variation between studies (RI), and the other calibrated the variance between studies to the size of the effect itself through a between-study coefficient of variation (CVB). Bootstrap and asymptotic confidence intervals for RI and CVB were derived and evaluated in an extensive simulation study that covered a wide range of scenarios likely to be encountered in practice. The best performance was given by asymptotic Wald confidence intervals developed for RI and CVB. The use of these heterogeneity measures together with their confidence intervals was illustrated in 5 typical meta-analyses. A new user-friendly SAS macro (SAS Institute, Inc., Cary, North Carolina) is provided to implement these methods for routine use and can be downloaded at the last author's website.

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With the limitations intrinsic to the potential biases in the studies included in this meta-analysis, we conclude that the minimally invasive lip biopsy technique for SS diagnosis induces fewer permanent neurological complications than conventional approaches with large linear incisions in the lower lip.

Electrical conductivity measurements for aqueous solutions of imipramine and clomipramine hydrochlorides have been carried out from (283 to 313) K. From these data, critical micelle concentrations (CMCs) have been obtained by application of the local polynomial regression method, which is based on a nonparametric estimation of the regression function. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. The variation of the CMC with temperature was a second-order polynomial, with different trends for imipramine (convex) and clomipramine (concave). This distinct behavior was analyzed by the effects of dehydration of the headgroups and thermal solubility of molecules.

The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found.

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