Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio 44195, USA.
Med Decis Making. 2010 Mar-Apr;30(2):267-74. doi: 10.1177/0272989X09342278. Epub 2009 Jul 31.
A difficulty with applying decision analysis at the bedside is that it generally requires computer software for the calculations, which may render the method impractical. The purpose of this study was to illustrate the feasibility of developing a regression model that approximates the results from a published decision-analytic model for prostate cancer and permits bedside generation of personalized decision-analytic recommendations with a paper nomogram.
The authors used the example of radical prostatectomy v. watchful waiting for patients with early-stage prostate cancer. First, they took a published decision analysis and generated recommendations using simulated data where patient baseline factors and preference scores for health states were systematically varied. Multivariable logistic regression was used to identify the parameters with strong associations with the recommendation. A reduced model was fit that excluded other preference scores except for watchful waiting. They compared the recommended management predictive accuracies from the full v. reduced model at the individual patient level for 63 men from another published study. Discrimination was assessed using receiver operating characteristic (ROC) curve analysis. A nomogram was constructed from the covariates in the reduced model.
The reduced logistic regression model predicted the recommendations accurately for the 63 patients, with an area under the ROC curve of 0.92. Discrimination was excellent as demonstrated by histograms.
The authors demonstrated that logistic regression modeling allows accurate reproduction of decision-analytic recommendations with simplified calculations, which can be accomplished using a graphic nomogram. This approach should facilitate clinical decision analysis at the bedside.
在床边应用决策分析的一个难点在于，它通常需要计算机软件进行计算，这可能会使该方法变得不切实际。本研究的目的是说明开发一种回归模型的可行性，该模型可以近似发表的前列腺癌决策分析模型的结果，并允许使用纸质列线图在床边生成个性化的决策分析建议。
作者以早期前列腺癌患者的根治性前列腺切除术与观察等待为例。首先，他们采用了已发表的决策分析，并使用模拟数据生成建议，其中患者的基线因素和健康状态的偏好评分被系统地改变。多变量逻辑回归用于确定与建议有强关联的参数。拟合一个排除其他偏好评分（除观察等待外）的简化模型。他们比较了该简化模型与完整模型在另一个发表的研究中的 63 名男性患者的个体水平上的推荐管理预测准确性。通过接收者操作特征（ROC）曲线分析评估判别能力。从简化模型的协变量中构建了一个列线图。
简化的逻辑回归模型对 63 名患者的建议预测准确，ROC 曲线下面积为 0.92。直方图显示判别能力极佳。
作者证明，逻辑回归模型可以通过简化计算准确再现决策分析建议，这可以通过图形列线图来完成。这种方法应该有助于在床边进行临床决策分析。
