生物识别与生物统计学杂志

 This paper proposes and discusses a non-parametric statistical method for the analysis of paired or matched sample data based on ranks rather than on the raw scores themselves. The proposed method intrinsically and structurally adjusts the test statistic for the possible presence of tied observations between the sampled populations and hence obviates the need to require these populations to be continuous. The number k used in the ranking may be any real number and does not affect the result of the analysis. The proposed method can be used with both numeric and nonnumeric measurements on as low as the ordinal scale and is easily modified for use with one sample data. The method is illustrated with some data and shown to compare favorably with the usual sign test and the Wilcoxon signed rank sum test in cases where these two methods may be equally used in data analysis.

 This paper proposed a non parametric method for estimating partial correlation coefficient. An illustrative example was provided. Results generated using this method was compared with Siegel’s approach and found to be the same but is easier and less tedious to use in practical application.

 In the dementia area it is often of interest to study relationships among regional brain measures; however, it is often necessary to adjust for covariates. Partial correlations are frequently used to correlate two variables while adjusting for other variables. Complete case analysis is typically the analysis of choice for partial correlations with missing data. However, complete case analysis will lead to biased and inefficient results when the data are missing at random. We have extended the partial correlation coefficient in the presence of missing data using the expectation-maximization (EM) algorithm, and compared it with a multiple imputation method and complete case analysis using simulation studies. The EM approach performed the best of all methods with multiple imputation performing almost as well. These methods were illustrated with regional imaging data from an Alzheimer’s disease study.