Journal of Biometrics & Biostatistics

Patient test outcomes from diagnostic testing laboratories can be indicators of laboratory performance. One method of proficiency testing compares test results across laboratories to flag non-compliant laboratories. Under this type of proficiency test, the statistical approaches for estimating the test result and comparing these across laboratories have important implications. The proficiency test of fecal occult blood testing laboratories in Ontario compare estimates of pass rate for a particular test by laboratory based on data observed in the present (latest) month. Estimates by laboratory are compared to an acceptance interval determined by data across all laboratories. A laboratory is classified as non-compliant when its monthly rate is outside the acceptance interval. We note that monthly sample sizes have an important impact on the probability that a laboratory is classified in error and differences in the number of tests conducted across laboratories should be considered. We present an alternative approach (Weighted Estimating Equations, or WEE) for combining historical data to improve the precision of the estimate of present performance in the case that performance changes slowly over time. The WEE approach uses all available historical data through estimating functions that down-weight past data. We compare the WEE approach to current practice through a real dataset of patient fecal occult blood test outcomes at laboratories in Ontario as well as simulated data. The study approach improves precision of the estimates and the power of a hypothesis test to compare estimates in order to reduce the risk of classifying a laboratory as compliant or non-compliant in error.

It is necessary and useful to reveal the chromosomal distribution at whole-genome level. By studying the chromosomal distribution of human disease genes, we found that the known genes of single-gene diseases are significantly enriched in chromosome X, and depleted in 10p, 19q. And the genes of multi-gene diseases are significantly tended to be located in fewer chromosomes than random background. The chromosomal distribution at whole-genome level could promote future studies of human disease genes.