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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Vydané v:	Pharmacoepidemiology and drug safety Ročník 31; číslo 1; s. 46 - 54
Hlavní autori:	Adamson, Blythe J. S., Ma, Xinran, Griffith, Sandra D., Sweeney, Elizabeth M., Sarkar, Somnath, Bourla, Ariel B.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Chichester, UK John Wiley & Sons, Inc 01.01.2022 Wiley Subscription Services, Inc
Predmet:	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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Abstract	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.
AbstractList	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.BACKGROUNDComparative-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.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).METHODSUsing 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).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.RESULTSThe 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.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.CONCLUSIONSThis 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. BackgroundComparative‐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.MethodsUsing 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).ResultsThe 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.ConclusionsThis 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. 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. 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. 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). 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. 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.
Author	Griffith, Sandra D. Sarkar, Somnath Sweeney, Elizabeth M. Adamson, Blythe J. S. Bourla, Ariel B. Ma, Xinran
AuthorAffiliation	1 Flatiron Health, Inc. New York New York USA 3 Cornell University New York New York USA 2 University of Washington Seattle Washington USA
AuthorAffiliation_xml	– name: 1 Flatiron Health, Inc. New York New York USA – name: 3 Cornell University New York New York USA – name: 2 University of Washington Seattle Washington USA
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Snippet	Background Comparative‐effectiveness studies using real‐world data (RWD) can be susceptible to surveillance bias. In solid tumor oncology studies, analyses of... Comparative-effectiveness studies using real-world data (RWD) can be susceptible to surveillance bias. In solid tumor oncology studies, analyses of endpoints... BackgroundComparative‐effectiveness studies using real‐world data (RWD) can be susceptible to surveillance bias. In solid tumor oncology studies, analyses of...
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SubjectTerms	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
Title	Differential frequency in imaging‐based outcome measurement: Bias in real‐world oncology comparative‐effectiveness studies
URI	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fpds.5323 https://www.ncbi.nlm.nih.gov/pubmed/34227170 https://www.proquest.com/docview/2611363345 https://www.proquest.com/docview/2548906688 https://pubmed.ncbi.nlm.nih.gov/PMC9290806
Volume	31
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