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Showing content from https://pubmed.ncbi.nlm.nih.gov/32352530/ below:

Association of Patient Demographic Characteristics and Insurance Status With Survival in Cancer Randomized Clinical Trials With Positive Findings

Observational Study

. 2020 Apr 1;3(4):e203842. doi: 10.1001/jamanetworkopen.2020.3842. Association of Patient Demographic Characteristics and Insurance Status With Survival in Cancer Randomized Clinical Trials With Positive Findings

Affiliations

¹ SWOG Cancer Research Network Statistics and Data Management Center, Seattle, Washington.
² Fred Hutchinson Cancer Research Center, Seattle, Washington.
³ SWOG Cancer Research Network Group Chair's Office, Knight Cancer Institute, Oregon Health & Science University, Portland.
⁴ Columbia University Medical Center, New York, New York.

PMID: 32352530
PMCID: PMC7193331
DOI: 10.1001/jamanetworkopen.2020.3842

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Observational Study

Association of Patient Demographic Characteristics and Insurance Status With Survival in Cancer Randomized Clinical Trials With Positive Findings

Joseph M Unger et al. JAMA Netw Open. 2020.

. 2020 Apr 1;3(4):e203842. doi: 10.1001/jamanetworkopen.2020.3842. Affiliations

¹ SWOG Cancer Research Network Statistics and Data Management Center, Seattle, Washington.
² Fred Hutchinson Cancer Research Center, Seattle, Washington.
³ SWOG Cancer Research Network Group Chair's Office, Knight Cancer Institute, Oregon Health & Science University, Portland.
⁴ Columbia University Medical Center, New York, New York.

PMID: 32352530
PMCID: PMC7193331
DOI: 10.1001/jamanetworkopen.2020.3842

Item in Clipboard

Abstract

Importance: Few new treatments tested in phase 3 cancer randomized clinical trials show an overall survival benefit. Although understanding whether the benefits are consistent among all patient groups is critical for informing guideline care, individual trials are designed to assess the benefits of experimental treatments among all patients and are too small to reliably determine whether treatment benefits apply to demographic or insurance subgroups.

Objective: To systematically examine whether positive treatment effects in cancer randomized clinical trials apply to specific demographic or insurance subgroups.

Design, setting, and participants: Cohort study of pooled patient-level data from 10 804 patients in SWOG Cancer Research Network clinical treatment trials reported from 1985 onward with superior overall survival for those receiving experimental treatment. Patients were enrolled from 1984 to 2012. Maximum follow-up was 5 years.

Main outcomes and measures: Interaction tests were used to assess whether hazard ratios (HRs) for death comparing standard group vs experimental group treatments were associated with age (≥65 vs <65 years), race/ethnicity (minority vs nonminority populations), sex, or insurance status among patients younger than 65 years (Medicaid or no insurance vs private insurance) in multivariable Cox regression frailty models. Progression- or relapse-free survival was also examined. Data analyses were conducted from August 2019 to February 2020.

Results: In total, 19 trials including 10 804 patients were identified that reported superior overall survival for patients randomized to experimental treatment. Patients were predominantly younger than 65 years (67.3%) and female (66.3%); 11.4% were black patients, and 5.7% were Hispanic patients. There was evidence of added survival benefits associated with receipt of experimental therapy for all groups except for patients with Medicaid or no insurance (HR, 1.23; 95% CI, 0.97-1.56; P = .09) compared with those with private insurance (HR, 1.66; 95% CI, 1.44-1.92; P < .001; P = .03 for interaction). Receipt of experimental treatment was associated with reduced added overall survival benefits in patients 65 years or older (HR, 1.21; 95% CI, 1.11-1.32; P < .001) compared with patients younger than 65 years (HR, 1.41; 95% CI, 1.30-1.53; P < .001; P = .01 for interaction), although both older and younger patients appeared to strongly benefit from receipt of experimental treatment. The progression- or relapse-free survival HRs did not differ by age, sex, or race/ethnicity but differed between patients with Medicaid or no insurance (HR, 1.32; 95% CI, 1.06-1.64; P = .01) vs private insurance (HR, 1.74; 95% CI, 1.54-1.97; P < .001; P = .03 for interaction).

Conclusions and relevance: Patients with Medicaid or no insurance may have smaller added benefits from experimental therapies compared with standard treatments in clinical trials. A better understanding of the quality of survivorship care that patients with suboptimal insurance receive, including supportive care and posttreatment care, could help establish how external factors may affect outcomes for these patients.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Ramsey reported receiving personal consulting fees from Bayer, Bristol-Myers Squibb, Genentech, Kite Pharma, and Seattle Genetics; research funding from Bayer and from Bristol-Myers Squibb; and travel funding from Bayer Schering Pharma AG, Bristol-Myers Squibb, and Flatiron Health. No other disclosures were reported.

Figures
Figure 1.. Study Population for Each Sociodemographic…

Figure 1.. Study Population for Each Sociodemographic Variable Analysis

M/NI represents Medicaid or no insurance.

Figure 1.. Study Population for Each Sociodemographic Variable Analysis

M/NI represents Medicaid or no insurance.

Figure 2.. Association of Treatment With Overall…

Figure 2.. Association of Treatment With Overall Survival by Level of Demographic and Insurance Variables
Figure 2.. Association of Treatment With Overall Survival by Level of Demographic and Insurance Variables

Forest plot showing the hazard ratio (HR) of death for patients receiving standard arm vs experimental arm therapies. Boxes represent HRs; horizontal lines, 95% CIs; diamonds, overall average HR across subgroups; and diamond size, 95% CI. The vertical line is the line of equal hazard (ie, neither an increased or decreased benefit of experimental therapy). Results for each sociodemographic variable level are derived from a single-adjusted model controlling for the covariates specified in the Methods.

Figure 3.. Association Between Insurance Status, Treatment,…

Figure 3.. Association Between Insurance Status, Treatment, and Outcomes by Amount of Follow-up

The regression…

Figure 3.. Association Between Insurance Status, Treatment, and Outcomes by Amount of Follow-up

The regression coefficient (A), χ² statistic (B), and attributable variation (C) for the interaction between insurance status and treatment depending on the amount of follow-up are shown. The strength of the interaction of treatment and insurance status was insensitive to the designated amount of follow-up time if specified annually, with interaction P values for the association with overall survival (OS) and with progression- or relapse-free survival (PFS) shown (B). M/NI represents Medicaid or no insurance.