Dataset Information

Calculating the net clinical benefit in neuro-oncology clinical trials using two methods: quality-adjusted survival effect sizes and joint modeling.

ABSTRACT:

Background

Two methods combining survival and health-related quality of life (HRQoL) data in glioma trials to calculate the "net clinical benefit" were evaluated: Quality-adjusted effect sizes (QASES) and joint modeling (JM).

Methods

The net clinical benefit in two trials was calculated as proof of concept for other trials. With the QASES method, effect sizes for differences in progression-free survival (PFS) or overall survival (OS) and HRQoL between the experimental arm and standard treatment arm were calculated, while the relative emphasis placed on survival/HRQoL varied. JM allows simultaneous modeling of HRQoL and OS/PFS.

Results

In the EORTC 26951 trial, combined radiochemotherapy significantly prolonged OS (difference 11.7 months), but also resulted in more patients experiencing clinically relevant worsening (?10 points) in appetite loss and nausea/vomiting shortly after treatment. Using QASES, the survival benefit of additional procarbazine, lomustine, and vincristine (PCV) decreased from 42.3 months to 29.5 and 28.2 months when accounting for appetite loss and nausea/vomiting, respectively. JM analyses resulted in a loss of the beneficial effect of additional PCV between 13% and 24% when adjusting for different HRQoL parameters. The EORTC 22033 trial showed no significant PFS difference between radiotherapy or temozolomide alone (46 vs 39 months), nor clinically relevant differences in HRQoL. JM analyses also showed no significant association between PFS and HRQoL scales/items, whereas QASES showed that temozolomide alone was more favorable when considering symptom burden (47-49 instead of 39 months).

Conclusions

Both methods resulted in different outcomes, but adjusting for the impact of treatment on HRQoL resulted in theoretically reduced survival benefits.

SUBMITTER: Coomans MB

PROVIDER: S-EPMC7772555 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature

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Publications

Calculating the net clinical benefit in neuro-oncology clinical trials using two methods: quality-adjusted survival effect sizes and joint modeling.

Coomans Marijke B MB Dirven Linda L Aaronson Neil K NK Baumert Brigitta G BG van den Bent Martin M Bottomley Andrew A Brandes Alba A AA Chinot Olivier O Coens Corneel C Gorlia Thierry T Herrlinger Ulrich U Keime-Guibert Florence F Malmström Annika A Martinelli Francesca F Sloan Jeff A JA Stupp Roger R Talacchi Andrea A Weller Michael M Wick Wolfgang W Reijneveld Jaap C JC Taphoorn Martin J B MJB

Neuro-oncology advances 20200101 1

<h4>Background</h4>Two methods combining survival and health-related quality of life (HRQoL) data in glioma trials to calculate the "net clinical benefit" were evaluated: Quality-adjusted effect sizes (QASES) and joint modeling (JM).<h4>Methods</h4>The net clinical benefit in two trials was calculated as proof of concept for other trials. With the QASES method, effect sizes for differences in progression-free survival (PFS) or overall survival (OS) and HRQoL between the experimental arm and stan ...[more]

PMID: 33409496

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Project description:BackgroundGuidelines recommend warfarin use in patients with atrial fibrillation solely on the basis of risk for ischemic stroke without antithrombotic therapy. These guidelines rely on ischemic stroke rates observed in older trials and do not explicitly account for increased risk for hemorrhage.ObjectiveTo quantify the net clinical benefit of warfarin therapy in a cohort of patients with atrial fibrillation.DesignMixed retrospective and prospective cohort study of patients with atrial fibrillation between 1996 and 2003.SettingAn integrated health care delivery system.Patients13 559 adults with nonvalvular atrial fibrillation.MeasurementsWarfarin exposure, patient characteristics, CHADS(2) score (1 point for each of congestive heart failure, hypertension, age, and diabetes and 2 points for stroke), and outcome events were ascertained from health plan records and databases. Net clinical benefit was defined as the annual rate of ischemic strokes and systemic emboli prevented by warfarin minus intracranial hemorrhages attributable to warfarin, multiplied by an impact weight. The base-case impact weight was 1.5, reflecting the greater clinical impact of intracranial hemorrhage versus thromboembolism.ResultsPatients accumulated more than 66 000 person-years of follow-up. The adjusted net clinical benefit of warfarin for the cohort overall was 0.68% per year (95% CI, 0.34% to 0.87%). Adjusted net clinical benefit was greatest for patients with a history of ischemic stroke (2.48% per year [CI, 0.75% to 4.22%]) and for those 85 years or older (2.34% per year [CI, 1.29% to 3.30%]). The net clinical benefit of warfarin increased from essentially zero in CHADS(2) stroke risk categories 0 and 1 to 2.22% per year (CI, 0.58% to 3.75%) in CHADS(2) categories 4 to 6. The patterns of results were preserved when weighting factors for intracranial hemorrhage of 1.0 and 2.0 were used.LimitationsResidual confounding is a possibility. Some outcome events were probably missed by the screening algorithm or when medical records were unavailable.ConclusionExpected net clinical benefit of warfarin therapy is highest among patients with the highest untreated risk for stroke, which includes the oldest age category. Risk assessment that incorporates both risk for thromboembolism and risk for intracranial hemorrhage provides a more quantitatively informed basis for the decision on antithrombotic therapy in patients with atrial fibrillation.Primary funding sourceNational Institute on Aging; National Heart, Lung, and Blood Institute; and Massachusetts General Hospital.

Dataset Information

Calculating the net clinical benefit in neuro-oncology clinical trials using two methods: quality-adjusted survival effect sizes and joint modeling.

Background

Methods

Results

Conclusions

Publications

Calculating the net clinical benefit in neuro-oncology clinical trials using two methods: quality-adjusted survival effect sizes and joint modeling.

Similar Datasets

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