Linden Ariel, Yarnold Paul R
Linden Consulting Group, LLC, Ann Arbor, MI, USA.
Division of General Medicine, Medical School, University of Michigan, Ann Arbor, MI, USA.
J Eval Clin Pract. 2016 Dec;22(6):864-870. doi: 10.1111/jep.12592. Epub 2016 Jun 29.
RATIONALE, AIMS AND OBJECTIVES: Program evaluations often utilize various matching approaches to emulate the randomization process for group assignment in experimental studies. Typically, the matching strategy is implemented, and then covariate balance is assessed before estimating treatment effects. This paper introduces a novel analytic framework utilizing a machine learning algorithm called optimal discriminant analysis (ODA) for assessing covariate balance and estimating treatment effects, once the matching strategy has been implemented. This framework holds several key advantages over the conventional approach: application to any variable metric and number of groups; insensitivity to skewed data or outliers; and use of accuracy measures applicable to all prognostic analyses. Moreover, ODA accepts analytic weights, thereby extending the methodology to any study design where weights are used for covariate adjustment or more precise (differential) outcome measurement.
One-to-one matching on the propensity score was used as the matching strategy. Covariate balance was assessed using standardized difference in means (conventional approach) and measures of classification accuracy (ODA). Treatment effects were estimated using ordinary least squares regression and ODA.
Using empirical data, ODA produced results highly consistent with those obtained via the conventional methodology for assessing covariate balance and estimating treatment effects.
When ODA is combined with matching techniques within a treatment effects framework, the results are consistent with conventional approaches. However, given that it provides additional dimensions and robustness to the analysis versus what can currently be achieved using conventional approaches, ODA offers an appealing alternative.
原理、目的和目标：项目评估通常采用各种匹配方法来模拟实验研究中组分配的随机化过程。通常，先实施匹配策略，然后在估计治疗效果之前评估协变量平衡。本文介绍了一种新颖的分析框架，在实施匹配策略后，利用一种称为最优判别分析（ODA）的机器学习算法来评估协变量平衡并估计治疗效果。该框架相对于传统方法具有几个关键优势：适用于任何变量度量和组数；对数据偏态或异常值不敏感；使用适用于所有预后分析的准确性度量。此外，ODA接受分析权重，从而将该方法扩展到任何使用权重进行协变量调整或更精确（差异）结果测量的研究设计。
将倾向得分一对一匹配用作匹配策略。使用均值的标准化差异（传统方法）和分类准确性度量（ODA）评估协变量平衡。使用普通最小二乘法回归和ODA估计治疗效果。
使用实证数据，ODA得出的结果与通过传统方法评估协变量平衡和估计治疗效果所获得的结果高度一致。
当在治疗效果框架内将ODA与匹配技术相结合时，结果与传统方法一致。然而，鉴于与目前使用传统方法所能实现的相比，它为分析提供了额外的维度和稳健性，ODA提供了一个有吸引力的替代方案。
