Evidence map›Paper›PMID 33732960›Full record

ReviewAdvances in radiation oncology

The Reality of Randomized Controlled Trials for Assessing the Benefit of Proton Therapy: Critically Examining the Intent-to-Treat Principle in the Presence of Insurance Denial.

Mike Hernandez, J Jack Lee, Beow Y Yeap, Rong Ye, Robert L Foote, Paul Busse, Samir H Patel, Roi Dagan, James Snider, Nasiruddin Mohammed and 10 more

Open access · goldAbstract readReview
In one paragraph

Review in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Trial
  2. Review
  3. ACR-ARS Practice Parameter for the Performance of Proton Beam Therapy.International journal of particle therapy · 2024
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors at 9 institutions in 2 countries.

Mike HernandezDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
J Jack LeeDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Beow Y YeapDepartment of Medicine, Massachusetts General Hospital, Boston, Massachusetts.
Rong YeDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Robert L FooteDepartment of Radiation Oncology, Mayo Clinic, Rochester, Minnesota.
Paul BusseDepartment of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts.
Samir H PatelDepartment of Radiation Oncology, Mayo Clinic, Phoenix, Arizona.
Roi DaganDepartment of Radiation Oncology, University of Florida Health, Gainesville, Florida.
James SniderDepartment of Radiation Oncology, University of Maryland Medical System, Baltimore, Maryland.
Nasiruddin MohammedDepartment of Radiation Oncology, Northwestern Medicine, Warrenville, Illinois.
Alexander LinDepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania.
Pierre BlanchardDepartment of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France.
Scott B CantorDepartment of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Menna Y TeferraDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Kate HutchesonDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Pablo YepesDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Radhe MohanDepartment of Physics and Astronomy, Rice University, Houston, Texas.
Zhongxing LiaoDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Thomas F DeLaneyDepartment of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts.
Steven J FrankDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
The University of Texas MD Anderson Cancer Center · USMassachusetts General Hospital · USMayo Clinic · USMayo Clinic in Florida · USNorthwestern Medicine · USRice University · USUniversity of Florida Health · USUniversity of Maryland, Baltimore · USUniversity of Pennsylvania · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

purposeThis 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.

resultsIncreasing 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.

conclusionsInsurance 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.

Identifiers

PMID33732960
PMCPMC7940795
OpenAlexW3108807922

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.