Reliability of Conclusions from Early Analyses of Real-World Data for Newly Approved Drugs in Advanced Gastric Cancer in the United States.

bias gastric cancer ramucirumab real-world data trastuzumab

Journal

Pragmatic and observational research
ISSN: 1179-7266
Titre abrégé: Pragmat Obs Res
Pays: New Zealand
ID NLM: 101688693

Informations de publication

Date de publication:
2020
Historique:
received: 07 12 2019
accepted: 16 03 2020
entrez: 21 5 2020
pubmed: 21 5 2020
medline: 21 5 2020
Statut: epublish

Résumé

As real-world data resources expand and improve, there will increasingly be opportunities to study the effectiveness of interventions. There is a need to ensure that study designs explore potential sources of bias and either acknowledge or mitigate them, in order to improve the accuracy of findings. The objective of this study was to understand newly approved drug utilization patterns in real-world clinical settings over time. This retrospective study included three sources of real-world data (claims, electronic health records, and recoded data from a quality care program) collected from patients diagnosed with gastric cancer who initiated therapy with either trastuzumab or ramucirumab. Linear regression was used to investigate trends in the use of these drugs for the care of patients with gastric cancer over time from Food and Drug Administration (FDA) approval. Eligible patients (n=1700) had consistent demographic and clinical characteristics over time. After regulatory approval, trastuzumab was used in later lines of therapy and then shifted to earlier lines (p=0.002), while ramucirumab utilization remained consistent over time after FDA approval (p=0.49). Ramucirumab augmentation, defined as the addition of the drug after initiation of a line of therapy, decreased over time (p=0.03), and trastuzumab augmentation remained consistent over time (p=0.58). Since treatment effectiveness may change across lines of treatment, bias may arise if there are changes in the use of the drug (such as line migration) during the time period of analysis using real-world data.

Sections du résumé

BACKGROUND BACKGROUND
As real-world data resources expand and improve, there will increasingly be opportunities to study the effectiveness of interventions. There is a need to ensure that study designs explore potential sources of bias and either acknowledge or mitigate them, in order to improve the accuracy of findings. The objective of this study was to understand newly approved drug utilization patterns in real-world clinical settings over time.
METHODS METHODS
This retrospective study included three sources of real-world data (claims, electronic health records, and recoded data from a quality care program) collected from patients diagnosed with gastric cancer who initiated therapy with either trastuzumab or ramucirumab. Linear regression was used to investigate trends in the use of these drugs for the care of patients with gastric cancer over time from Food and Drug Administration (FDA) approval.
RESULTS RESULTS
Eligible patients (n=1700) had consistent demographic and clinical characteristics over time. After regulatory approval, trastuzumab was used in later lines of therapy and then shifted to earlier lines (p=0.002), while ramucirumab utilization remained consistent over time after FDA approval (p=0.49). Ramucirumab augmentation, defined as the addition of the drug after initiation of a line of therapy, decreased over time (p=0.03), and trastuzumab augmentation remained consistent over time (p=0.58).
CONCLUSION CONCLUSIONS
Since treatment effectiveness may change across lines of treatment, bias may arise if there are changes in the use of the drug (such as line migration) during the time period of analysis using real-world data.

Identifiants

pubmed: 32431558
doi: 10.2147/POR.S241427
pii: 241427
pmc: PMC7205419
doi:

Types de publication

Journal Article

Langues

eng

Pagination

27-43

Informations de copyright

© 2020 Hess et al.

Déclaration de conflit d'intérêts

LMH, AML, XIL, ZLC, LB and WS are employees of Eli Lilly and Company. MG is an employee of HealthCore, Inc., an independent research organization that received funding from Eli Lilly and Company for the conduct of the study. LW was an employee of HealthCore, Inc. when the study was conducted. The authors report no other conflicts of interest in this work.

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Auteurs

Lisa M Hess (LM)

Global Patient Outcomes, Eli Lilly and Company, Indianapolis, IN, USA.

Michael Grabner (M)

Life Sciences Research, HealthCore Inc., Wilmington, DE, USA.

Liya Wang (L)

Life Sciences Research, HealthCore Inc., Wilmington, DE, USA.

Astra M Liepa (AM)

Global Patient Outcomes, Eli Lilly and Company, Indianapolis, IN, USA.

Xiaohong Ivy Li (XI)

Global Statistics, Eli Lilly and Company, Indianapolis, IN, USA.

Zhanglin Lin Cui (ZL)

Global Statistics, Eli Lilly and Company, Indianapolis, IN, USA.

Lee Bowman (L)

Global Patient Outcomes, Eli Lilly and Company, Indianapolis, IN, USA.

William R Schelman (WR)

Medical Affairs, Eli Lilly and Company, Indianapolis, IN, USA.

Classifications MeSH