Lin K K, Reschke M F
Krug International, Houston, TX 77058.
Aviat Space Environ Med. 1987 Sep;58(9 Pt 2):A9-15.
The one-equation and the two-equation logistic models were used to predict tested subjects' susceptibility to motion sickness in KC-135 parabolic flights using data from other ground-based motion sickness tests. A data set containing data from 6 provocative tests, 2 vestibular function tests, and 1 motion sickness experience questionnaire from 162 subjects was used in this study. The prediction results from the logistic models were compared with those from the previously-used Bayes linear discriminant analysis procedures. The results based on this data set show that the logistic models correctly predicted substantially more cases (an average of 13%) in the data subset used for model building. In the data subset used for model cross-validation, the logistic models correctly predicted 4% and 5% more cases in the prediction of vomit or nonvomit, and of degree of susceptibility, respectively. Overall, the logistic models ranged from 53 to 65% predictions of the three endpoint parameters, whereas the Bayes linear discriminant procedure ranged from 48 to 65% correct for the cross validation sample.
单方程和双方程逻辑模型被用于利用其他地面晕动病测试的数据，预测在KC - 135抛物线飞行中测试对象患晕动病的易感性。本研究使用了一个数据集，该数据集包含来自162名受试者的6项激发试验、2项前庭功能测试和1份晕动病经历问卷的数据。将逻辑模型的预测结果与先前使用的贝叶斯线性判别分析程序的结果进行了比较。基于该数据集的结果表明，在用于模型构建的数据子集中，逻辑模型正确预测的病例显著更多（平均多13%）。在用于模型交叉验证的数据子集中，逻辑模型在预测呕吐或不呕吐以及易感性程度方面，正确预测的病例分别多4%和5%。总体而言，逻辑模型对三个终点参数的预测范围为53%至65%，而贝叶斯线性判别程序对交叉验证样本的正确预测范围为48%至65%。
