[Accuracy of Gender Distinction Based on Basic Laboratory Test Results: Comparison between Conventional Logistic...

Feasibility of gender distinction based on laboratory tests was explored by employing test results for 21 basic analytes which were obtained from 1,944 healthy Japanese (males = 856; females = 1,088: age = 20-64) in a multicenter reference interval (RI) study conducted in 2009. Two methods for the distinction were examined. One was logistic regression analysis with stepwise selection of analytes representing gender differences. The estimated probability (EP) of the sample's being from a female was calculated from the regression model. The other was the weighted-average log-likelihood (WALL) method, which is based on matching of an arbitrary set of lab test results with those of a given diagnostic category. In this study, WALLs belonging to distributions of male test results were computed with SDR (SD representing gender-difference divided by SD comprising RI) set as the weight for each analyte. WALL was computed by inclusion of either all analytes(WALL1), those with SDR ≥ 0.3(WALL2), or SDR ≥ 0.5(WALL3). The performance of EP and WALLs was evaluated as accuracy (% correct distinction) and area under the ROC curve (AUC). Because SDR decreased with age 46, EP and WALLs were computed for all-ages, age ≤ 45 and age ≥ 46. Analytes chosen in the logistic model were not necessarily those with high SDR. In all-age analysis, accuracy and AUC for EP vs. WALL1 were 94.2% and 0.986 vs. 91.1% and 0.973; respectively; in age ≤ 45 analysis, 95.1% and 0.990 vs. 93.2% and 0.981; in age 46 analysis, 92.6%, and 0.980 vs. 90.5% and 0.973. WALL2 showed slightly better performance in any age group. WALL analysis showed a little less performance than the conventional logistic regression analysis, but it has superior properties for the binary categorization with its nonparametric nature and flexibility in the selection of test items.