Effect of Clinical Trial Participation on Costs to Payers in Metastatic Non–Small-Cell Lung Cancer

2021 ◽  
Vol 17 (8) ◽  
pp. e1225-e1234
Author(s):  
Cristina Merkhofer ◽  
Shasank Chennupati ◽  
Qin Sun ◽  
Keith D. Eaton ◽  
Renato G. Martins ◽  
...  

PURPOSE: The costs associated with clinical trial enrollment remain uncertain. We hypothesized that trial participation is associated with decreased total direct medical costs to health care payers in metastatic non–small-cell lung cancer. METHODS: In this retrospective cohort study, we linked clinical data from electronic medical records to sociodemographic data from a cancer registry and claims data from Medicare and two private insurance plans. We used a difference-in-difference analysis to estimate mean per patient per month total direct medical costs for patients enrolled on a second-line (2L) trial versus patients receiving standard-of-care 2L systemic therapy. RESULTS: Among 70 eligible patients, the difference-in-difference of mean per patient per month total direct medical costs between 2L trial participants and nonparticipants was –$6,663 ( P = .01), for a mean savings of $45,308 per patient for the duration of 2L trial therapy. In a secondary analysis by primary insurance payer, this difference-in-difference was –$5,526 ( P = .26) for patients with commercial insurance and –$7,432 ( P = .01) for patients with Medicare. CONCLUSION: Participation in a 2L trial was associated with a $6,663 per month cost savings to health care payers for the duration of trial participation. Further studies are necessary to elucidate differences in cost savings from trial participation for Medicare and commercial payers. If confirmed, these results support health care payer investment in programs to improve clinical trial access and enrollment.

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0132568 ◽  
Author(s):  
Christine Rotonda ◽  
Amélie Anota ◽  
Mariette Mercier ◽  
Bérangère Bastien ◽  
Gisèle Lacoste ◽  
...  

1994 ◽  
Vol 12 (3) ◽  
pp. 243-249 ◽  
Author(s):  
Dong M. Shin ◽  
Paul Y. Holoye ◽  
Arthur Forman ◽  
Rodger Winn ◽  
Roman Perez-Soler ◽  
...  

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21110-e21110
Author(s):  
Yuval Shaked ◽  
Michal Harel ◽  
Coren Lahav ◽  
Eyal Jacob ◽  
Itamar Sela ◽  
...  

e21110 Background: Immune checkpoint inhibitor (ICI) therapy represents one of the most promising cancer treatments to date. However, despite unprecedented rates of durable response, only a small proportion of patients benefits from this approach. Major efforts are therefore required to characterize treatment resistance mechanisms, as well as to identify reliable biomarkers for response. We have previously shown that in response to various types of cancer therapy, including ICIs, the host may induce pro-tumorigenic processes that can promote therapy resistance. Here we examined systemic host-response proteomic profiles in non-small cell lung cancer (NSCLC) patients, aiming to discover biomarkers for response to ICI therapy and to unravel underlying resistance mechanisms. Methods: As part of our ongoing PROPHETIC clinical trial (NCT04056247), plasma samples were obtained at baseline (T0) and early-on treatment (T1; following the first treatment) from 120 NSCLC patients receiving ICI therapy. Proteomic profiling of the plasma samples was performed using proximity-extension assay (PEA) technology; validation was carried out for a fraction of the samples using ELISA-based arrays. To identify a proteomic signature that predicts clinical outcome, machine learning algorithms were applied following a random separation of the cohort into a discovery set and a validation set. Results: A proteomic signature predictive of response to treatment was identified and validated. Bioinformatic analysis identified potential mechanisms of resistance based on differentially expressed proteins associated with pro-tumorigenic biological processes. Statistical analysis of the clinical data identified multiple novel differential clinical parameters between responders and non-responders, either at baseline or by comparing T0 to T1, which may suggest host-mediated effects. Conclusions: Our study demonstrates the potential clinical utility of analyzing the host response to ICI therapy, in particular for the discovery of novel predictive biomarkers for NSCLC patient stratification. Clinical trial information: NCT04056247.


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