Pederson L L, Baskerville J C
Prev Med. 1983 May;12(3):430-6. doi: 10.1016/0091-7435(83)90251-7.
A multivariate predictive model was developed to classify patients with respiratory disease as to their smoking status following physician advice to quit (L.L. Pederson, J. C. Baskerville, and J. M. Wanklin, Prev. Med. 11, 536--549 (1982)). The purpose of this study was to validate this model on a new group of patients by comparing their predicted smoking behavior with their actual behavior. Using a probability of 0.50 as the cutoff for prediction, overall accuracy was 89.6%. However, the sensitivity for detecting those who would actually quit was low. By reducing the cutoff probability to 0.20, overall accuracy remained high and sensitivity was increased. A discussion of the implications of different types of classification errors is presented based on cost-effectiveness considerations. The clinical usefulness of prediction models is discussed.
开发了一种多变量预测模型，用于根据医生的戒烟建议对患有呼吸系统疾病的患者的吸烟状况进行分类（L.L. 佩德森、J.C. 巴斯克维尔和J.M. 万克林，《预防医学》11卷，第536 - 549页（1982年））。本研究的目的是通过将一组新患者的预测吸烟行为与其实际行为进行比较，来验证该模型。以0.50的概率作为预测临界值，总体准确率为89.6%。然而，检测实际会戒烟者的灵敏度较低。通过将临界概率降低到0.20，总体准确率仍然很高，并且灵敏度有所提高。基于成本效益考虑，对不同类型分类错误的影响进行了讨论。还讨论了预测模型的临床实用性。
