TY - JOUR
T1 - The Reality of Randomized Controlled Trials for Assessing the Benefit of Proton Therapy
T2 - Critically Examining the Intent-to-Treat Principle in the Presence of Insurance Denial
AU - Hernandez, Mike
AU - Lee, J. Jack
AU - Yeap, Beow Y.
AU - Ye, Rong
AU - Foote, Robert L.
AU - Busse, Paul
AU - Patel, Samir H.
AU - Dagan, Roi
AU - Snider, James
AU - Mohammed, Nasiruddin
AU - Lin, Alexander
AU - Blanchard, Pierre
AU - Cantor, Scott B.
AU - Teferra, Menna Y.
AU - Hutcheson, Kate
AU - Yepes, Pablo
AU - Mohan, Radhe
AU - Liao, Zhongxing
AU - DeLaney, Thomas F.
AU - Frank, Steven J.
N1 - Publisher Copyright:
© 2020 The Authors
PY - 2021/3/1
Y1 - 2021/3/1
N2 - Purpose: 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. Methods and Materials: 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. Results: 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. Conclusions: 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.
AB - Purpose: 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. Methods and Materials: 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. Results: 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. Conclusions: 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.
UR - http://www.scopus.com/inward/record.url?scp=85101965900&partnerID=8YFLogxK
U2 - 10.1016/j.adro.2020.100635
DO - 10.1016/j.adro.2020.100635
M3 - Review article
AN - SCOPUS:85101965900
SN - 2452-1094
VL - 6
JO - Advances in Radiation Oncology
JF - Advances in Radiation Oncology
IS - 2
M1 - 100635
ER -