Kaufman Brystana G, Van Houtven Courtney H, Greiner Melissa A, Hammill Bradley G, Harker Matthew, Anderson David, Petry Sarah, Bull Janet, Taylor Donald H
Margolis Center for Health Policy, Duke University, Durham, North Carolina, USA; Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA; Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Health Care System, Durham, North Carolina, USA.
Margolis Center for Health Policy, Duke University, Durham, North Carolina, USA; Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA; Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham VA Health Care System, Durham, North Carolina, USA.
J Pain Symptom Manage. 2021 May;61(5):1002-1011.e2. doi: 10.1016/j.jpainsymman.2020.09.011. Epub 2020 Sep 15.
Palliative care (PC) programs are typically evaluated using observational data, raising concerns about selection bias.
To quantify selection bias because of observed and unobserved characteristics in a PC demonstration program.
Program administrative data and 100% Medicare claims data in two states and a 20% sample in eight states (2013-2017). The sample included 2983 Medicare fee-for-service beneficiaries aged 65+ participating in the PC program and three matched cohorts: regional; two states; and eight states. Confounding because of observed factors was measured by comparing patient baseline characteristics. Confounding because of unobserved factors was measured by comparing days of follow-up and six-month and one-year mortality rates.
After matching, evidence for observed confounding included differences in observable baseline characteristics, including race, morbidity, and utilization. Evidence for unobserved confounding included significantly longer mean follow-up in the regional, two-state, and eight-state comparison cohorts, with 207 (P < 0.001), 192 (P < 0.001), and 187 (P < 0.001) days, respectively, compared with the 162 days for the PC cohort. The PC cohort had higher six-month and one-year mortality rates of 53.5% and 64.5% compared with 43.5% and 48.0% in the regional comparison, 53.4% and 57.4% in the two-state comparison, and 55.0% and 59.0% in the eight-state comparison.
This case study demonstrates that selection of comparison groups impacts the magnitude of measured and unmeasured confounding, which may change effect estimates. The substantial impact of confounding on effect estimates in this study raises concerns about the evaluation of novel serious illness care models in the absence of randomization. We present key lessons learned for improving future evaluations of PC using observational study designs.
姑息治疗（PC）项目通常使用观察性数据进行评估，这引发了对选择偏倚的担忧。
量化PC示范项目中因观察到的和未观察到的特征导致的选择偏倚。
使用两个州的项目管理数据和100%的医疗保险索赔数据，以及八个州20%的样本（2013 - 2017年）。样本包括2983名年龄在65岁及以上参加PC项目的医疗保险按服务付费受益人，以及三个匹配队列：地区队列；两个州的队列；八个州的队列。通过比较患者基线特征来衡量因观察到的因素导致的混杂。通过比较随访天数以及六个月和一年的死亡率来衡量因未观察到的因素导致的混杂。
匹配后，观察到的混杂证据包括可观察到的基线特征存在差异，如种族、发病率和医疗服务利用率。未观察到的混杂证据包括地区、两个州和八个州的比较队列的平均随访时间显著更长，分别为207天（P < 0.001）、192天（P < 0.001）和187天（P < 0.001），而PC队列的随访时间为162天。PC队列的六个月和一年死亡率更高，分别为53.5%和64.5%，而地区比较队列分别为43.5%和48.0%，两个州比较队列分别为53.4%和57.4%，八个州比较队列分别为55.0%和59.0%。
本案例研究表明，比较组的选择会影响测量到的和未测量到的混杂程度，这可能会改变效应估计值。在本研究中，混杂对效应估计值的实质性影响引发了对在缺乏随机化的情况下新型重症护理模式评估的担忧。我们介绍了通过观察性研究设计改进未来PC评估的关键经验教训。
