Calibrating Observational Health Record Data Against a Randomized Trial.


Journal

JAMA network open
ISSN: 2574-3805
Titre abrégé: JAMA Netw Open
Pays: United States
ID NLM: 101729235

Informations de publication

Date de publication:
03 Sep 2024
Historique:
medline: 30 9 2024
pubmed: 30 9 2024
entrez: 30 9 2024
Statut: epublish

Résumé

The conditions required for health record data sources to accurately assess treatment effectiveness remain unclear. Emulation of randomized clinical trials (RCTs) with health record data and subsequent calibration of the results can help elucidate this. To pilot an emulation of the KEYNOTE-189 RCT using a commercially available electronic health record (EHR) data source. This retrospective cohort study used an EHR database spanning from April 2007 to February 2023. Follow-up began on treatment initiation and proceeded until an outcome event, loss to follow-up, end of data, or end of study period (640 days). The population-based cohort was ascertained from EHRs provided by 52 health systems across the US. Eligibility criteria were defined as closely as possible to the benchmark RCT. Patients with non-small cell lung cancer initiating first-line treatment for metastatic disease were included. Patients with evidence of squamous non-small cell lung cancer, primary nonlung malignant neoplasms, or identified EGFR/ALK variations were excluded. Data were analyzed from June to October 2023. Initiation of first-line pembrolizumab and chemotherapy and chemotherapy alone. Chemotherapy in both groups was defined as a combination of pemetrexed and platinum-based (carboplatin or cisplatin) therapy. Outcomes of interest were 12-month survival probability and mortality hazard ratio (HR). A total of 1854 patients (mean [SD] age, 63.7 [9.6] years; 971 [52.4%] men) were eligible, including 589 patients who initiated pembrolizumab and chemotherapy and 1265 patients who initiated chemotherapy only. The cohort included 364 Black patients (19.6%) and 1445 White patients (77.9%). The 12-month survival probabilities were 0.60 (95% CI, 0.54-0.65) in the pembrolizumab group and 0.58 (95% CI, 0.55-0.62) in the chemotherapy-only group, compared with 0.69 (95% CI, 0.64-0.74) in the KEYNOTE-189 pembrolizumab group and 0.49 (95% CI, 0.42-0.56) in the KEYNOTE-189 chemotherapy-only group. The mortality HR was 0.95 (95% CI, 0.78-1.16), compared with 0.49 (95% CI, 0.38-0.64) in the KEYNOTE-189 RCT. In this cohort study piloting an RCT emulation, results were incongruous with the benchmark trial. Differences in patient treatment and data capture between the RCT and EHR populations, confounding by indication, treatment crossover, and accuracy of captured diagnoses may explain these findings. Future feasibility assessments will require data sources to have important oncology-specific measures curated.

Identifiants

pubmed: 39348118
pii: 2824209
doi: 10.1001/jamanetworkopen.2024.36535
doi:

Substances chimiques

Antibodies, Monoclonal, Humanized 0
pembrolizumab DPT0O3T46P

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2436535

Auteurs

David Merola (D)

Aetion, New York, New York.

Ulka Campbell (U)

Aetion, New York, New York.

David Lenis (D)

Aetion, New York, New York.

Sebastian Schneeweiss (S)

Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.

Shirley Wang (S)

Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.

Ann Madsen (A)

Aetion, New York, New York.

Gillis Carrigan (G)

Center for Observational Research, Amgen, San Francisco, California.

Victoria Chia (V)

Center for Observational Research, Amgen, San Francisco, California.

Osayi E Ovbiosa (OE)

AbbVie, North Chicago, Illinois.

Simone Pinheiro (S)

AbbVie, North Chicago, Illinois.

Nelson Pace (N)

AbbVie, North Chicago, Illinois.

Amanda Bruno (A)

Bayer Pharmaceuticals, Philadelphia, Pennsylvania.

Mark Stewart (M)

Friends of Cancer Research, Washington, District of Columbia.

Sajan Khosla (S)

AstraZeneca, Gaithersburg, Maryland.

Yiduo Zhang (Y)

AstraZeneca, Gaithersburg, Maryland.

Mothaffar Rimawi (M)

Baylor College of Medicine, Houston, Texas.

Rachele Hendricks-Sturrup (R)

Duke-Margolis Center for Health Policy, Washington, District of Columbia.

Jenny Huang (J)

Gilead Sciences, Foster City, California.

Aliki Taylor (A)

Gilead Sciences, Foster City, California.

XiaoLong Jiao (X)

Pfizer, New York, New York.

Lauren Becnel (L)

Pfizer, New York, New York.

Lynn McRoy (L)

Pfizer, New York, New York.

Joy Eckert (J)

Reagan-Udall Foundation for the Food and Drug Administration, Washington, District of Columbia.

Carla Rodriguez (C)

Reagan-Udall Foundation for the Food and Drug Administration, Washington, District of Columbia.

Orsolya Lunacsek (O)

Bayer Pharmaceuticals, Whippany, New Jersey.

Raymond Harvey (R)

Johnson & Johnson, New Brunswick, New Jersey.

Joel Greshock (J)

Johnson & Johnson, New Brunswick, New Jersey.

Khaled Sarsour (K)

Johnson & Johnson, New Brunswick, New Jersey.

Andrew Belli (A)

COTA Healthcare, New York, New York.

C K Wang (CK)

COTA Healthcare, New York, New York.

Laura Fernandes (L)

COTA Healthcare, New York, New York.

James Chen (J)

Tempus, Chicago, Illinois.

Brian San Francisco (B)

Tempus, Chicago, Illinois.

Chithra Sangli (C)

Tempus, Chicago, Illinois.

Yana Natanzon (Y)

ConcertAI, Cambridge, Massachusetts.

K Arnold Chan (KA)

TriNetX, Cambridge, Massachusetts.

Neil Dhopeshwarkar (N)

TriNetX, Cambridge, Massachusetts.

Mark Shapiro (M)

xCures, Oakland, California.

Asher Wasserman (A)

xCures, Oakland, California.

Jameson Quinn (J)

xCures, Oakland, California.

Megan Rees (M)

Loopback Analytics, Dallas, Texas.

Travis Robinson (T)

Loopback Analytics, Dallas, Texas.

Ben Taylor (B)

Aetion, New York, New York.

Jennifer R Rider (JR)

Aetion, New York, New York.

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