Differential frequency in imaging‐based outcome measurement: Bias in real‐world oncology comparative‐effectiveness studies

Background Comparative‐effectiveness studies using real‐world data (RWD) can be susceptible to surveillance bias. In solid tumor oncology studies, analyses of endpoints such as progression‐free survival (PFS) are based on progression events detected by imaging assessments. This study aimed to evalua...

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Published in:Pharmacoepidemiology and drug safety Vol. 31; no. 1; pp. 46 - 54
Main Authors: Adamson, Blythe J. S., Ma, Xinran, Griffith, Sandra D., Sweeney, Elizabeth M., Sarkar, Somnath, Bourla, Ariel B.
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Inc 01.01.2022
Wiley Subscription Services, Inc
Subjects:
Bias
Cancer
Carcinoma, Non-Small-Cell Lung - diagnostic imaging
Carcinoma, Non-Small-Cell Lung - epidemiology
comparative‐effectiveness analysis
Effectiveness studies
Electronic health records
Electronic medical records
Humans
imaging assessment timing
Lung cancer
Lung Neoplasms - diagnostic imaging
measurement bias
Non-small cell lung carcinoma
Oncology
Original
Patients
Progression-Free Survival
progression‐free survival (PFS)
real‐word data (RWD)
scan timing
simulation modeling
Solid tumors
real-word data (RWD)
simulation modeling
measurement bias
comparative-effectiveness analysis
cancer
scan timing
progression-free survival (PFS)
imaging assessment timing
ISSN:1053-8569, 1099-1557, 1099-1557
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Summary:Background Comparative‐effectiveness studies using real‐world data (RWD) can be susceptible to surveillance bias. In solid tumor oncology studies, analyses of endpoints such as progression‐free survival (PFS) are based on progression events detected by imaging assessments. This study aimed to evaluate the potential bias introduced by differential imaging assessment frequency when using electronic health record (EHR)‐derived data to investigate the comparative effectiveness of cancer therapies. Methods Using a nationwide de‐identified EHR‐derived database, we first analyzed imaging assessment frequency patterns in patients diagnosed with advanced non‐small cell lung cancer (aNSCLC). We used those RWD inputs to develop a discrete event simulation model of two treatments where disease progression was the outcome and PFS was the endpoint. Using this model, we induced bias with differential imaging assessment timing and quantified its effect on observed versus true treatment effectiveness. We assessed percent bias in the estimated hazard ratio (HR). Results The frequency of assessments differed by cancer treatment types. In simulated comparative‐effectiveness studies, PFS HRs estimated using real‐world imaging assessment frequencies differed from the true HR by less than 10% in all scenarios (range: 0.4% to −9.6%). The greatest risk of biased effect estimates was found comparing treatments with widely different imaging frequencies, most exaggerated in disease settings where time to progression is very short. Conclusions This study provided evidence that the frequency of imaging assessments to detect disease progression can differ by treatment type in real‐world patients with cancer and may induce some bias in comparative‐effectiveness studies in some situations.
Bibliography:Funding information
Flatiron Health, Inc.
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Funding information Flatiron Health, Inc.
ISSN:1053-8569
1099-1557
1099-1557
DOI:10.1002/pds.5323