Hernandez Mike, Lee J Jack, Yeap Beow Y, Ye Rong, Foote Robert L, Busse Paul, Patel Samir H, Dagan Roi, Snider James, Mohammed Nasiruddin, Lin Alexander, Blanchard Pierre, Cantor Scott B, Teferra Menna Y, Hutcheson Kate, Yepes Pablo, Mohan Radhe, Liao Zhongxing, DeLaney Thomas F, Frank Steven J
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts.
Adv Radiat Oncol. 2020 Dec 2;6(2):100635. doi: 10.1016/j.adro.2020.100635. eCollection 2021 Mar-Apr.
This study hypothesized that insurance denial would lead to bias and loss of statistical power when evaluating the results from an intent-to-treat (ITT), per-protocol, and as-treated analyses using a simulated randomized clinical trial comparing proton therapy to intensity modulated radiation therapy where patients incurred increasing rates of insurance denial.
Simulations used a binary endpoint to assess differences between treatment arms after applying ITT, per-protocol, and as-treated analyses. Two scenarios were developed: 1 with clinical success independent of age and another assuming dependence on age. Insurance denial was assumed possible for patients <65 years. All scenarios considered an age distribution with mean ± standard deviation: 55 ± 15 years, rates of insurance denial ranging from 0%-40%, and a sample of N = 300 patients (150 per arm). Clinical success rates were defined as 70% for proton therapy and 50% for intensity modulated radiation therapy. The average treatment effect, bias, and power were compared after applying 5000 simulations.
Increasing rates of insurance denial demonstrated inherent weaknesses among all 3 analytical approaches. With clinical success independent of age, a per-protocol analysis demonstrated the least bias and loss of power. When clinical success was dependent on age, the per-protocol and ITT analyses resulted in a similar trend with respect to bias and loss of power, with both outperforming the as-treated analysis.
Insurance denial leads to misclassification bias in the ITT analysis, a missing data problem in the per-protocol analysis, and covariate imbalance between treatment arms in the as-treated analysis. Moreover, insurance denial forces the critical appraisal of patient features (eg, age) affected by the denial and potentially influencing clinical success. In the presence of insurance denial, our study suggests cautious reporting of ITT and as-treated analyses, and placing primary emphasis on the results of the per-protocol analysis.
本研究假设,在使用模拟随机临床试验评估意向性治疗(ITT)、符合方案集和实际治疗分析结果时,保险拒付会导致偏倚并丧失统计效力。该模拟随机临床试验比较了质子治疗与调强放射治疗,其中患者的保险拒付率不断上升。
模拟使用二元终点来评估应用ITT、符合方案集和实际治疗分析后各治疗组之间的差异。制定了两种情景:一种情景中临床成功与年龄无关,另一种情景假设临床成功与年龄有关。假定65岁以下患者可能被保险拒付。所有情景均考虑年龄分布为均值±标准差:55±15岁,保险拒付率范围为0%-40%,样本量为N = 300例患者(每组150例)。质子治疗的临床成功率定义为70%,调强放射治疗的临床成功率定义为50%。在进行5000次模拟后比较平均治疗效果、偏倚和效力。
保险拒付率上升表明所有三种分析方法都存在固有弱点。在临床成功与年龄无关的情况下,符合方案集分析显示出最小的偏倚和效力丧失。当临床成功与年龄有关时,符合方案集和ITT分析在偏倚和效力丧失方面呈现相似趋势,两者均优于实际治疗分析。
保险拒付在ITT分析中导致错误分类偏倚,在符合方案集分析中导致缺失数据问题,并在实际治疗分析中导致各治疗组之间的协变量不平衡。此外,保险拒付促使对受拒付影响且可能影响临床成功的患者特征(如年龄)进行批判性评估。在存在保险拒付的情况下,我们的研究建议谨慎报告ITT和实际治疗分析结果,并将主要重点放在符合方案集分析的结果上。