Feinglass J, Yarnold P R, McCarthy W J, Martin G J
Division of General Internal Medicine, Northwestern University Medical School, Chicago, Illinois, USA.
Med Care. 1998 May;36(5):740-7. doi: 10.1097/00005650-199805000-00013.
To study treatment bias in observational outcomes research, the authors present a nonlinear classification tree model of clinical and psychosocial factors influencing selection for interventional management (lower extremity bypass surgery or angioplasty) for patients with intermittent claudication.
The study sample includes 532 patients with mild to moderate lower extremity vascular disease, without prior peripheral revascularization procedures or symptoms of disease progression. All patients were enrolled in a prospective outcomes study at the time of an initial referral visit for claudication to one of the 16 Chicago-area vascular surgery offices or clinics in 1993-95. The influence of baseline sociodemographic, clinical, and patient self-reported health status data on subsequent treatment is analyzed. Study variables were derived from lower extremity blood flow records and patient questionnaires. Follow-up home health visits were used to ascertain the frequency of lower extremity revascularization procedures within 6 months of study enrollment. Hierarchically optimal classification tree analysis (CTA) was used to obtain a nonlinear model of treatment selection. The model retains attributes with the highest sensitivity at each node based on cutpoints that maximize classification accuracy. Experimentwise Type I error is ensured at P < 0.05 by the Bonferroni method and jackknife validity analysis is used to assess model stability.
Seventy-one of 532 patients (13.3%) underwent interventional procedures within 6 months. Ten patient attributes were used in the CTA model, which had an overall classification accuracy of 89.5% (67.6% sensitive and 92.9% specific), achieving 57.7% of the theoretical possible improvement in classification accuracy beyond chance. Eleven model prediction endpoints reflected a 33-fold difference in odds of undergoing lower extremity revascularization.
Initial ankle-brachial index (100%), leg symptom status over the previous six months (89%), self-reported community walking distance (74%) and prior willingness to undergo a lower extremity hospital procedure (39%) were used to classify most patients in the sample. These attributes are critical control variables for a valid observational study of treatment effectiveness.
为研究观察性结果研究中的治疗偏倚，作者提出了一种非线性分类树模型，用于分析影响间歇性跛行患者选择介入治疗（下肢搭桥手术或血管成形术）的临床和社会心理因素。
研究样本包括532例轻度至中度下肢血管疾病患者，这些患者此前未接受过外周血管重建手术，也没有疾病进展的症状。1993年至1995年期间，所有患者在因跛行首次转诊至芝加哥地区16个血管外科办公室或诊所之一时，均被纳入一项前瞻性结果研究。分析了基线社会人口统计学、临床和患者自我报告的健康状况数据对后续治疗的影响。研究变量来自下肢血流记录和患者问卷。通过随访家庭健康访视来确定研究入组后6个月内下肢血管重建手术的频率。采用分层最优分类树分析（CTA）来获得治疗选择的非线性模型。该模型基于能使分类准确性最大化的切点，在每个节点保留具有最高敏感性的属性。通过Bonferroni方法确保实验性I型错误在P<0.05水平，并用刀切法有效性分析来评估模型稳定性。
532例患者中有71例（13.3%）在6个月内接受了介入治疗。CTA模型使用了10个患者属性，其总体分类准确性为89.5%（敏感性为67.6%，特异性为92.9%），在分类准确性上比随机水平实现了理论上可能提高的57.7%。11个模型预测终点反映了接受下肢血管重建手术几率的33倍差异。
初始踝臂指数（100%）、过去六个月的腿部症状状态（89%）、自我报告的社区行走距离（74%）和先前接受下肢住院手术的意愿（39%）被用于对样本中的大多数患者进行分类。这些属性是有效观察治疗效果研究的关键控制变量。
