Lobo Francis S, Wagner Samuel, Gross Cynthia R, Schommer Jon C
Health Economics & Outcomes Research Group, East Hanover, NJ 07936, USA.
Res Social Adm Pharm. 2006 Mar;2(1):143-51. doi: 10.1016/j.sapharm.2005.12.001.
Randomized Clinical Trials (RCTs) remain the gold standard for determining the utility of pharmaceuticals especially from a safety and efficacy standpoint. However, restrictive entry criteria and stringent protocols can be barriers to generalizing RCT findings to real world practices and outcomes. Observational studies overcome these limitations of RCTs since they are representative of real world populations and practices. Nonetheless, attributing causality remains a major limitation in observational studies, due to the non-random assignment of subjects to treatment. Non-random assignment can lead to imbalances in risk-factors between the groups being compared and thus bias the estimates of the treatment effect. Non-random assignment can be particularly problematic in observational studies comparing older versus newer pharmaceuticals from similar therapeutic classes due to the phenomenon of channeling. Channeling occurs when drug therapies with similar indications are preferentially prescribed to groups of patients with varying baseline prognoses. In this manuscript we discuss the phenomenon of channeling and the use of a statistical technique known an propensity scores analysis which potentially adjusts for the effects of channeling. During the course of this manuscript we discuss tests for determining the quality of the derived propensity score, various techniques for utilizing propensity scores, and also the potential limitations of this technique. With the increasing availability of high quality pharmaceutical and medical claims data for use in observational studies, increased attention must be given to analytic techniques that adjust optimally for non-random assignment and resulting channeling bias. For research studies using observational study designs, propensity score analysis offers a reasonable solution to address the limitation of non-random assignment, especially when RCTs are too costly, time-consuming or not ethically feasible.
随机临床试验(RCTs)仍然是确定药物效用的金标准,尤其是从安全性和有效性的角度来看。然而,严格的纳入标准和严格的方案可能会成为将RCT结果推广到实际临床实践和结果的障碍。观察性研究克服了RCT的这些局限性,因为它们代表了真实世界的人群和实践。尽管如此,由于受试者并非随机分配接受治疗,因此在观察性研究中确定因果关系仍然是一个主要限制。非随机分配可能导致被比较组之间的风险因素失衡,从而使治疗效果的估计产生偏差。在比较来自相似治疗类别的新旧药物的观察性研究中,由于“渠道化”现象,非随机分配可能会带来特别的问题。当具有相似适应症的药物疗法被优先开给具有不同基线预后的患者群体时,就会出现“渠道化”。在本手稿中,我们讨论了“渠道化”现象以及一种称为倾向得分分析的统计技术的应用,该技术有可能对“渠道化”的影响进行调整。在本手稿的讨论过程中,我们探讨了用于确定导出倾向得分质量的检验方法、利用倾向得分的各种技术以及该技术的潜在局限性。随着用于观察性研究的高质量药品和医疗索赔数据越来越容易获得,必须更加关注能够针对非随机分配和由此产生的渠道化偏差进行最佳调整的分析技术。对于使用观察性研究设计的研究而言,倾向得分分析为解决非随机分配的局限性提供了一个合理的解决方案,尤其是在RCT成本过高、耗时过长或在伦理上不可行的情况下。